CHAPTER 1CONCEPT OF RESEARCH

1.0 INTRODUCTION

Research can be called as a purposive investigation. The objectives of the research could be gaining familiarity with research objectives (exploratory research), to describe the characteristics of a market or many markets as well as of consumers, to decide the frequency with which some phenomenon (say stock-out situation for essential goods distributed as ration-card), to test hypothesis, etc. The significance / importance of the research can be understood from the fact that it provides the basis for all policies and strategies may be for any marketer or even to government. There are many types of research like descriptive, analytical, applied, basic, quantitative and qualitative, etc.

1.1 DEFINITIONS OF RESEARCH

(1) It is the activity which extends, corrects and verifies the knowledge.

(2) It is the activity of finding new ways of looking at known / familiar data in order to explore new ways to change it as expected / intended.

(3) Research is the process which involves the steps like defining the problem, identifying research objectives, formulating hypothesis, collecting and interpretation of data, deriving findings, conclusions and then identifying the action plan.

(4) Research is the well planned activity which is designed and implemented to provide the data for solving important genuine and recurrent problems.

(5) Research is the activity which involves manipulation of things, concepts or symbols for the purpose of generalizing to extend, correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art.a

(6) Research can also be said as a movement from the known to unknown facts

(7) It is the systematized efforts to gain new knowledge.

1.2 DEFINITIONS OF METHODOLOGY AND RESEARCH METHODOLOGY

“Methodology is a term which should not be misused for “method” or “technique”. Methodology has got an important meaning. It becomes first an approach towards inquiry and or research then later evolves into particular methods or techniques. In the applied use it is concerned with selecting specific technical tools and techniques for collecting data and analyzing it. In the theoretical use, it is concerned with the philosophical fields of inquiry that can be used to conceptualize the problem under study. There are two different methodological stances. Discipline research is oriented towards enriching knowledge in a scientific discipline whereas policy research denotes to another methodology that is philosophically committed and serve as a guide to social action. Quite often methodology is used in the applied sense undermining its theoretical perspectives, though both are the two sides of a coin. Some of the definitions are as follows:

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(1) Methodology in the applied sense refers to various methods used by the researcher right from data collection and various techniques used for the same for interpretation and inference. Methods and techniques are often used synonymously in research literature. Research methodology is what must be done, how it will be done, what data will be needed, what data gathering will be employed, how sources of data will be selected and how the data will be analyzed and conclusions reached. When we talk of research methodology we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that research results are capable of being evaluated either by the researcher himself or by others.

(2) Research methodology is “a procedure designed to the extent to which it is planned and evaluated before conducting the inquiry and the extent to which the method for making decisions is evaluated”. The word methodology is used freely in different context.

(3) Methodology is not merely description of methods / set of methods or techniques. Techniques are aids to research like are aids to research like regression and correlation. Methodology provides arguments, perhaps relationships, which support various preferences entertained by the scientific community for certain rules of intellectual procedure, including those for forming concepts, building models, formulating hypotheses and testing theories. Methodology is neither a study of ‘good’ methods nor a study of ‘methods’ used but rather a study of reasons behind principles on the basis of which various types of propositions are accepted or rejected as part of the body of ordered knowledge in general or of any discipline. In short, we may define methodology as the science of procedure to build, verify or extend scientific knowledge.

A thorough understanding of a scientific methodology alone will contribute an appreciable research piece. Scientific piece of investigation will provide an argument which is as true for each individual mind as of the researcher’s own mind. Therefore, the most important step in a research design is the selection of an appropriate methodology.

The significance of the use of the term ‘methodology’ is that it requires an argument to connect the choice and practice of particular methods to the way that the problem is conceived and the utility and limitations of the outcome. It is in this sense of the term, as requiring a critical justification for the adoption and practice of particular research methods that we claim that our concern is with ‘methodology’ rather than with methods alone. .. Only rarely do books on research methods discuss situations in which particular methods should not be used, or situations within which the methods chosen may cause distortion or precipitate changes are the not captured by the methods themselves.

(4) Methodologically designed research can be considered as a piece of scientific work though approximation to fool proof methodology is a continuous and never ending

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price. Thorough knowledge about the latest development in the concerned branch is absolutely necessary for designing a scientific methodology. Research is a fact finding process through systematic and indepth study through the various research process including collection compilation, presentation and interpretation of derails or data. The only way to find truth and gain knowledge is the scientific method of investigation.

The researcher must be able to give clear scientific explanation and the logic behind them for a host of the following questions:

1. Why the particular research study is undertaken?

2. How the research problem is stated?

3. What are approaches towards the inquiry?

4. What are the tools and techniques that will be used for data collection? Why this method is adopted? Is the sampling design appropriate?

5. How the hypotheses have been framed?

6. How the hypotheses will be tested?

7. How the various statistical tools and techniques are selected. What is the method of data processing? How it will be calculated?

8. Which techniques are used to evaluate the accuracy of results?

The framing of a good research methodology is compared to that of an architect who designs a building, i.e., “he has to evaluate why and on what basis he selects particular size, number and location of doors, windows and ventilators, uses particular materials and others and the like”. Literature review and interactions with experts will help one to sharpen the methodology. The external examiners who evaluate the thesis, approve one which is conceptually, methodologically and factually correct and to the best of his knowledge it is free from errors and plagiarism (copying) and sufficiently of good standard. The research quality that equates to international excellence or national excellence in all areas or in majority of the areas of a thesis to a great extent depends on the formulation of a good and scientific research methodology.

Finally, the methodology adopted should be open to pubic so that others can know how one reached the conclusions about a study. The means of method of enquiry is opened for public evaluation and criticism.

1.3 OBJECTIVES OF THE RESEARCH

The basic purpose of the research Is to identify the action plan to answer to questions through the application of scientific procedures. The key objectives of the research could be as follows:

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(1) To become familiar with a certain mechanism or phenomenon or to gain new insights into it. This type of research is normally called as exploratory research design. For example, before parliamentary elections all political parties conduct research on priorities of the voters so as to prepare election promotion plan.

(2) To describe accurately the characteristics of a particular consumer-group / segment, market situation or an industry. For example in Indian Tourism marketing per capita spending power of domestic tourists in Rs. 3500 per annum whereas spending power of inbound and outbound tourist is Rs. 58000 p.a. As regards to market situation is concerned, we can describe Indian Software industry revolving around only financial solutions for banking sector. We can also describe Indian home Appliances Industry as a market worth Rs. 48000 crores, growth rates 7.10% and having intense completion, but main dominant players are MNCs like LG, Samsung and home grown Videocon international.

(3) To identify the frequency with which some phenomenon, say stock-out, occurs or the causes associated with the particular phenomenon.

For example in a thick populated area, there could be frequent stock-out of most essential goods like LPG-cylinders, kerosene, food grains, etc. State Govt. may be wanted to know frequency of stock out and causes like distribution bottlenecks, etc. The leading retailers of India like Food world, Shoppers Stop, etc. conduct stock taking and or implement latest SCM software to avoid the situations of stock out. Similarly many industries in India and in globe, conduct research to study business-recession cycle frequency. For example the global recession during 1991 and 1997 was well predicted before occurrence. In similarly way the recession to Indian Telecom Industry during 2000-2002 was also predicted. Current predictions about some of the industries are management education, especially MBA and MCA is booming and will appreciate for another 10 years. There is great demand for MBAs having engineering background.

(4) To formulate and test a hypothesis may be to establish the relationship between say sales and market share or sales and customer satisfaction, level, etc. For example, Gujarat Cooperative Milk Marketing Federation (GCMMF) was under impression that it has 20% market share for liquid milk (Amul-Taza) at Nagpur during 2004. It conducted research for which hypothesis was formulated and tested. The reality was, Amul’s market share formulated and tested. The reality was Amul’s market share was only 11.5% at Nagpur during 2004. Exactly reverse happened incase of Cadbury’s Dairy milk chocolates. It thought that its market share during 2004 is 70% whereas it was 72% across four metros and nine mini metros. After 1999, Videocon’s market share in CTV market was on decline. It wanted to know the cause. The research indicated that Videocon needed rectification in brand-image. Videocon immediately responded and appointed Shahrukh Khan as a brand-ambassador.

1.4 MOTIVATIONS IN RESEARCH

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Why to conduct the research? The motivations could be as follows:

(1) Aspiration to derive the consequential benefits due to the research.For example, till 2004, the brand image of Lux soap was ‘The soap of the stars’. This positioning worked for 50 years. However during 2005, the brand lost the magic. The research was done by HLL, which indicated that (a) positioning should be changed to glamour and luxury and (b) it should come out from the image product for female. HLL repositioned Lux like Luxurious product that make her feel beautiful and special’ with punch line, ‘Brings out stars in you’ HLL also changed celebrity from female actress to male actor Shahrukh Khan. Both things are now working to bring out Lux from red to black and white.

Lifebuoy soap was also suffering from image, like ‘Tandurasti Ki Raksha Karta hai Lifebuoy’. The research said that repositioning and brand extension is must. HLL repositioned Lifebuoy from Tough Soap and Tandurasti to Germicidal perfume and extended to lifebuoy Gold and Lifebuoy Plus. The obvious benefit of rising sales is the contribution of the research.

During 2000 AD, HLL’s all tea brands were suffering from competition due to which growth in sales became stagnant. India’s tea market size is Rs. 6000 crores, in which branded tea market occupies Rs. 2500 cr and unbranded or loose tea occupies Rs. 3500 cr. Research indicated that growth would be only in loose tea market. HLL successfully launched ‘A1’ brand tea to snatch the customers from loose tea market, with punch line ‘strong cup of tea’ and market segments focused are housewife and journalists. The philosophy used, ‘due to strong cup of tea, ordinary man like housewife and or a journalist get courage to face difficult situations in the life.

(2) Intention to face the challenges in overcoming the competition. During 2005, P&G reduced the prices of the detergent Tide considerably. For example its price was Rs. 40 for 500 gm. HLL quickly responded to the change and launched the brand extension of Surf ‘Surf Excel Blue’ for price of Rs. 50 for 750 gm. To sustain the competition from band ambassador Shekhar Suman, HLL opted comparably unknown faces with the statements and counter statements like Jayga-------- Nahi Jayyega. The well thought research by HLL for surf Excel Blue is clear winner. Tide is in big problem.

(3) Intention to apply research for successful creativity. During 21st century, washing machine almost became house-hold item. However, due to heavy traffics, habits towards consuming fast food at road side restaurants became very common. The result was the “dust” and “blackened cloths”. Marketers identified the priority of the consumer -------- she wanted to get rid of ‘daag’. HLL P&G successful developed the ad-campaigns, ‘Dundate Raha Jayoge’, Kuch Pane ke Liye Kuch Dhona Padata hai’, ‘Daag Acche Hai’, etc.

(4) Intention to integrate societal marketing (social welfare) with main strategic marketing.

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Consumer perception about the company and company products is very crucial to understand from marketing point of view. If social welfare is integrated with marketing strategy then it could have cutting edge. History proved that due to social welfare, the image of the company could be changed. For example, Tisco says manufacturing steel is our second priority. Our first; priority is society welfare. Hence Tisco say we also manufacture steel. The result ------------- in last 90 years there are no strikes and no production losses in spite of the location of factory on the border of Bihar and Orissa where labour and labour union problems are dominant.

P&G also designed ‘Project Dhrusti’ for ‘Whisper’ and ‘Mr. Gold’ for all products under umbrella brand.

HLL ‘Shakti Amma’, ‘Mobile hospital’ are winner society welfare projects, which helped to project favourable image.

(5) Intention to get respectability within the country and out of the country.India’s most successful IT trio is Infosys, Wipro and TCS. India and Indians are respected in globe due to the powerful brain of Narayan Moorthy, Azim Premjee and Mr. S. Ramadorai, CEO, TCS. The respectability is the result of lot of hard work to deliver the value to the stakeholders. For shareholders the respect is due to very attractive dividend and stock price. For country, the respect is due to forex earning ability and employment generation. For global countries the respect is due to its intelligence, price, service-delivery and out sourcing ability. I always admire the skill of the trio and hence would like to describe, ‘Real Gold of Indian IT’. (Per employee profit after tax for Infosys during 04-05 was Rs. 5,00,000 for TCS Rs. 4,40,000 and for Wipro Rs. 3,70,000). Please read article 1.35 India’s most Admired Companies.

1.5 IMPORTANCE OF THE RESEARCH

Significance of research and research leads to invention. Following facts highlight the importance of the research

(1) Research facilitates logical or scientific thinking process which leads towards flow less strategy formulation.

(2) It facilitates identification of ‘trends’ which ultimately responsible in marketing opportunities.

(3) Decision making becomes easier for well researched phenomenon.

(4) Research is important in solving various operational and planning problems of business and industry.

(5) It helps understanding perception of the society about the marketer and accordingly designs the marketing strategy.

1.8 SCIENTIFIC METHOD

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The Scientific method is theorizing based on experimentation and is thus very close to Empiricism It also needs quite a bit of logical deduction and so appears closer to the top of the graph. The essential tenets of the scientific method are (1) direct observation of phenomena; (2) clearly defined variables, methods, and procedures; (3) empirically testable hypotheses; (4) the ability to rule out rival hypotheses; (5) statistical rather than linguistic alternative justification of conclusions; and (6) the self-correcting process.' This is the most commonly used style of thinking in research methodology. In this style of thinking hypotheses are proposed based on some proven theories and then they are practically tested. They require a considerable amount of logical deduction, but not as much as in Postulational theories. Nevertheless, the use of logical deduction is considerable. We shall discuss logical' deduction further in the following section.

The Thought Process

Reasoning forms the basis of the scientific inquiry. The thought process of a scientist may be based on deduction, induction or a combination of both. Let us understand in detail each of these processes of thinking needed for conducting and drawing conclusions from marketing research.

Deduction

Deduction is a form of inference where conclusion necessarily follows from the given premises, i.e. neither can the conclusion contradict the premises nor can it assume new premises. A deduction is correct if it is both true and valid.

A deduction is true if the premises on which it is based are true, i.e., they agree with the real world. For example, premise like "world is flat" is a false premise. Deduction based on such premise will also be false. Deduction is valid if it is impossible for the conclusions to be false, and if the premises it is based on is true. That means the method of drawing conclusion should be logical and valid. Truth and validity of a deduction together mean that conclusion is not logically justified (even if true) if either one or more of the premises is false or if the method of deduction is incorrect. The conclusion may still be correct due to some other premises not considered. Let us look at an example.

Premise 1: Pavan is a good boy. Premise 2: Prathap and Pavan are friends. Conclusion: Pratap is a good boy.

Here both the premises are true, but the argument that led to the conclusion is not valid and so the deduction is not valid. The conclusion may be due to some other reasons but not as a result of the given premises. Let us look at an example where the deduction is not true.

Premise 1: Reading too much dulls one's mind Premise 2: Prathap reads too much

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Conclusion: Prathap must be dull-headed.

Here the conclusion logically follows the premises, but premise 1 is a dubious statement. If premise 1 is false then the conclusion is also false. Let us look at an example in which there is a logical flaw.

Premise 1: Dogs do not hate water Premise 2: Rabid dogs hate water Conclusion: Rabid dogs are not dogs.

Here premise 1 is correct and true in general. But, the rabid dogs are also dogs. If we consider this, then the first premise is not correct. One has to correct the first statement, based on the second. This leads to the invalid conclusion.

Premise I: Rain is a probability if the sky is cloudy. Premise 2: The sky is cloudy today. Conclusion: Rain is a probability today.

This deduction is true and valid. Such deductions are made every moment by one and all and look obvious.

Induction

Induction is the conclusion drawn from one or more facts, bat not necessarily from facts alone. The conclusion explains the facts, but the facts just given are not sufficient to lead to the conclusion. There is a need for additional facts from the previously learnt knowledge. To illustrate, suppose Kiran approaches his boss Pavan with a routine problem, but, to his shock, receives rude treatment for no mistake of his. Then Kiran can based on his previous experience conclude any of the following:

Another colleague of his might have just annoyed Pavan. May be, just then, his boss, Sudba, gave him a piece of her mind. May be a personal problem is bothering Pavan May be he was annoyed by a traffic jam on his way to office.

Any of these conclusions can explain the fact that Pavan treated Kiran rudely. However, at the same time, the given fact cannot lead directly to any of these conclusions. These conclusions are based on some previous experience, i.e. on some other facts also. And conclusions are in reality only hypotheses and need further verification to ascertain the correctness.

Combination Of Induction And Deduction

To explain an observed phenomena a researcher formulates some hypotheses that needs to be verified by the use of induction. The researchers then use deduction to check whether each of the hypotheses can explain the given facts completely by itself. Once this is done, it is

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necessary to perform empirical tests with all these hypotheses and then select a hypothesis that passes these tests. This method is described as the double movement of reflective thinking by Dewey and is adapted by Cooper and Schindler. The process the researcher should follow is as outlined below. The researcher

Encounters a curiosity, doubt, barrier, suspicion, or obstacle, generally termed as a problem.

Struggles to state the problem: asks questions, contemplates the existing knowledge, gathers facts, and moves from an emotional to an intellectual confrontation with the problem

Proposes hypotheses to explain the facts that are believed to be logically related to the problem

Deduces outcomes or consequences of the hypotheses: attempts to discover what happens if the results are in the opposite direction of that predicted, or if the results support the expectations.

Formulates several rival hypotheses

Devises and conducts a crucial empirical test with various possible outcomes, each of which selectively excludes one or more hypotheses.

Draws a conclusion, an inductive inference, based on acceptance or rejection of the hypotheses

Feeds information buck into the original problem modifying it according to the strength of the evidence.

These steps are interdependent. They are also not sequentially fixed. Based on the nature of the study some of the above steps may be eliminated or new steps may be added.

Scientific Method And Its Major Characteristics

Two major characteristics of the scientific method are validity and reliability. Validity is a measure of the match between what the research claims to measure and what it actually measures. In other words it measures the effectiveness of measurement in research. For example, a people meter on a TV set is supposed to be measuring the viewership of a particular program, while in reality it only measures the number of occasions the TV was tuned to that particular channel when the program was relayed. The Television may be on but there may be no one watching it. Moreover even if there are viewers, the people-meter cannot count how many. Thus the assumption that the people-meter measures viewership validity is wrong. Hence a research which assumes it is measuring the viewership with people meter is not valid. Validity seems easily achievable, but as in the above example, there may be minor deviations that can easily go unnoticed. Hence, to ensure validity one should carefully and purposefully probe into every detail of the research.

Reliability is a measure of repeatability of the research. It is also a measure of the investigator's independence of the research. In other words, if a research is reliable, then any

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other investigator repeating it will obtain the same results. This characteristic is also known as objectivity. Achieving this is very difficult even in the hard sciences. Apart from validity and reliability, the scientific method has many other characteristics. Some of the important ones are:

Logical: Logic is necessary in designing and following up a research process, and arriving at conclusions.

Systematic: The process of research is marked by thoroughness and regularity, and so it is considered to be systematic.

Empiricism: Research is done through observations that are based on direct sense experience. Thus it is empirical in nature.

Research should be carried out in a scientific manner to reduce the uncertainty in a situation and to ensure accuracy of the results the research yields. This needs the use of scientific method.

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Box 1.7 :Steps that make up the scientific method

Observation:

A good scientist is observant and notices thing in the world around him / her. (S)he sees, hears, or in some other way notices what is gaining on in the world, becomes curious about what’s happenings and raises a questions about it.

Hypothesis:

This is a tentative answer to the question: an explanation for what was observed. The scientist tries to explain what caused what was observed (huy7po=under, beneath; thesis = an arranging)

Hypotheses are possible causes. A generalization based on inductive reasoning is to a hypothesis. A hypothesis not an observation, rather, a tentative explanation for the observation.

Hypotheses reflect past experience with similar questions (“educated propositions” about cause)

Multiple hypotheses should be proposed whenever possible. One should think of alternative causes that could the observations (the correct one may not even be one that was though of)

Hypotheses should be testable by experimentation and deductive reasoning.

Hypotheses can be proven wrong / incorrect, but can never be proven or confirmed with absolute certainty.

Someone in the future with more knowledge may find a case where the hypothesis is not true.

Prediction:

Next, the experimenter uses deductive reasoning to test the hypothesis.

Inductive reasoning goes from a set of specific observations to general conclusions: I observed cells in x, y and organisms, therefore all animals have cells.

Deductive reasoning flows from general to specific. From general premises, a scientist would extrapolate to results: if all organisms have cells and humans are organisms, then humans should have cells. This about a specific case based on the general premises.

Generally in the scientific method, if a particular hypothesis / premise are true, then one should expect (prediction) a result. This involves the use of if then logic.

Testing:

Then, the scientist performs the experiment to see if the predicted results are obtained. If the expected results are, that supports the hypothesis.

Scientific Method-In Marketing As Compare Dot Physical Sciences

Scientific method, as the name suggest, is more applicable to the sciences than to the arts. I Marketing Research, the decision-maker applies the methods of science to the art of marketing. Thus the reliability and validity of the method are lower when it applied to marketing research. The following are the major differences between the physical sciences and marketing research that affect the reliability and validity of research process.

Research conditions

In physical sciences, an experiment is conducted under a controlled environment. For example, in a chemical experiment, temperature, pressure, etc. are controlled to the required extent. In marketing research, it is almost impossible to achieve such perfect control of all the variables. Unlike physical sciences, marketing research does not involve inanimate, controllable factors, but involves people their behaviour, their perceptions and their attitudes, which change with time, place, presence of others at that instance, etc. These factors, being complex, adversely affect the reliability of research in marketing.

Figure 1.7 : Five Point scale to measure the likelihood of purchase

Measuring Instruments

Measuring instruments in physical sciences provide very high accuracy; For instance, physicists can measure up to a 10-15 of a meter, which is a millionth of a billionth of a meter. However, in marketing it is difficult to arrive at such accuracy. For example many questionnaires use a five-point scale to measure the likelihood of purchase. The scale is shown in Figure 1.7. Such a scale gives only a crude measure: moreover the meaning of the words in the given scale may mean different things to different people. This affects both the validity and reliability of the research.

Personal Interests

Ideally, the personal interests of the researcher should not affect research results. But it happens both in physical and social sciences. The extent to which the results affect the researcher is more in marketing compared to the physical sciences. In physical sciences, research results affect only the fame of the researcher whereas in marketing research, they affect their work and thus their life in marketing research, it often happens that strong willed marketing managers may need research to support their decision, while researchers themselves may be anxious to see their organization, and thus their careers, prosper. This

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forces the researchers to push their research so that it is acceptable to their clients. If the researcher is associated in the decision-making process as well, the personal interests of the researcher in the result increase further. Influence Of Measurement

Sometimes the process of measurement may itself affect the result, i.e. the process researchers undertake for making the measurement may result in a change in the outcome. In science, the affect of measurement on the result is not very pronounced except in fields like quantum mechanics. But in marketing research the influence of measurement on the result is very appreciable. For example, when a family has a "people meter" on its television set, it may modify their viewing habits because they know that all their viewing is recorded. Similarly, people participating in focus groups know that they are being observed and so they may come up with socially acceptable answers.

Re-questioning a group of respondents may also affect the results. If a group of respondents are questioned a second or third time, they may give different answers from what they would have given if they were questioned for the first time. For example, if a company has quizzed a group of respondents about its brand before an advertisement campaign, then after this the group will start noticing the advertisements with more interest than it would have done without us quizzing. This would change their responses in the questioning from what the responses would have been had they not been questioned before.

The person who administers questionnaires or conducts an interview can also influence the result by his communication skills and his knowledge of the project. When the respondents approach him for clarifications the knowledge or lack of it will affect his reply. This, in turn will affect the result. Some times the mere presence of an interviewer may affect the result. This is more clearly explained in the chapter "Instruments of Respondent Communication". Respondents themselves often change opinions with time. These factors affect the validity and reliability of marketing research.

Time Pressure

In managerial decisions, timeliness becomes more important than the aptness of the decisions, per se. In any given situation, there is no perfectly right decision, but only the most appropriate decision that can be taken in the available time. This exerts time pressure on marketing research and may reduce validity and reliability of the research results.

Short-Term Goals

Marketing research generally aims at reaching short-term goals, i.e. helping in solving an immediate managerial problem Management does not aim at preserving and propagating the acquired knowledge. In comparison, science aims at the accumulation of a knowledge pool and uses this knowledge pool to arrive at some general theories. Once established, these theories remove the need for reinventing the wheel and allow the researchers to concentrate

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on more advanced areas of research. But in marketing research, the knowledge once learnt is neither preserved, nor propagated. Even if the results are preserved by a company or a marketing research firm, the results are not shared with other firms due to competition.

This internal data may not be sufficient to give rise to stand-alone theories. Moreover, the firm would not spare resources to go into such 'theoretical' research. This results in unnecessary repetition of the research. But increasingly, researchers are recording what was known in a project and are using it as a base the future projects. They are using more efficient methods like knowledge management for organizing and reusing internal knowledge. This is leading to a gradual understanding of the theoretical behavior of many issues, like consumer behavior, with respect to particular products.

Difficulty In Experimentation

In the physical sciences, cause and effect relationships can be easily identified with the help of experimentation. But experimentation with complete control of all the factors is impossible in marketing research. For example, to test the effect of a new design on the sale of umbrellas, it is not possible to hold factors like weather constant. Similarly, when one is testing the effect of a new design on the sale of jeans, one cannot control factors like changing fashions. Thus experimentation, to its fullest extent is not possible in marketing research.

Terminology In The Scientific Method

Scientific method, since it is founded in science, derives its terminology from science. The basic terminology required for understanding the scientific method is given below.

Facts And Observations

Facts are phenomena that we believe are true. These facts do not change with the person who reports them. Original documents and fact-gathering agencies are important sources of facts in marketing research.

Observation is the process by which we recognize or note facts. These are experiential in nature (they are the expressions of our perception of reality) and tend to change from person to person. For example, during a sales promotion, it may be a fact that the sales volume has not changed, but the sales staff at an outlet may give higher sales estimates based on their perception. The perception of increased sales may be due to the increased work pressure on the reduced staff. Or it may be due to an actual increase in the number of non-buying customers visiting the outlet. Thus observations are the perceptions of the individuals based on 'their experience of reality,' and hence may vary from the facts.

Variables And Definitions

A variable is a physical or non-physical quantity that can take anyone of a predefined set of values, numerical or otherwise. It can be defined as a formal representation of a property of entities. An entity is something that exists as or perceived as separate object. For a table,

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chair, a human etc. are entities. Every entity has a multitude of properties. For example, a table has legs, wood type, feel, height, width, length, etc. Similarly we can consider two properties of human beings 'blood group and 'height.' It is usually represented by a symbol. The variables can be classified either based on their measurability or on their relationship with each other. On the basis of measurability the variables are of two types.

Continuous: The variable that takes an infinite number of continuous values is called as continuous variable. For example, if satisfaction is represented as a variable, it can actually take any value from zero to infinity. Mathematically a continuous variable is such that, if we take any two values of a continuous variable we can find at least one more value between these values.

Discrete: This type of variable takes only a fixed number of values. For example, the variable ‘occurrence of sale' can take any of the two values' 1 for sale and '0' for no sale, and so can be called a dichotomous variable. Similarly, 'degree of liking' is referred to as a polytomy because it takes multiple values. It can take the value '-1' for dislike, '0' for neither like nor dislike, and '1' for' like.'

Dependence: The researcher tries to establish a relationship between two variables in his research. For sample, when he is conducting an experiment, the researcher manipulates a variable and measures the effects on some other variable. The variable manipulated is called the independent variable (IV), and the variable measured is called the dependent variable (DV). To illustrate further, suppose a researcher is trying to find the relationship between the length of an advertisement and the recall. The recall percentage is the dependent variable and the length of the advertisement is the independent variable. Table 1.7 lists some terms that are used as synonyms for the dependent and independent variables.

The above two types of variables are different from each other in terms of their relationship. The other types of variables, based on their relationship with other variables are the following.

Moderating variable (MY): The moderating variable is the second independent variable included in the study since it is believed to have a significant effect on the relationship between the main independent and dependent variables. For instance, let us state a hypothesis - the introduction of a dating allowance (IV) will lead to higher productivity (DV), especially among younger (age is MY) workers. Here the younger workers have a moderating effect on the original relationship.

Extraneous variables: These are variables outside the immediate relationship between independent variables and Dependent variable. There are many extraneous variables that have some impact on the original relationship between the IV and DV, but the effects are either not significant or so random that they are not measurable.

Intervening variable: Sometimes one finds that the IV-DV relationship stated is not direct and that the independent variable actually affects some other variable (the intervening variable or IIV), which in turn affects the dependent variable. In the hypothesis stated above,

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we can see that the dating allowance (IV) does not directly affect the productivity (II V), but affects the satisfaction in the personal life (IIV), which in turn affects the productivity (DV). Definition: Definitions are of two major types, constitutive and operational definitions. In constitutive definitions concepts are defined with the help of other concepts and constructs. In other words, they are theoretical definitions. Operational definitions are those which define concepts in terms of the process of measurement or manipulation. Definitions are required in research to provide an understanding and measure of concepts.

Table 1.8 : Synonyms for dependent and independent variables

Independent variables Dependent VariablesCause EffectStimulus ResponsePredicted from ………. Predicted to ……..Antecedent ConsequenceManipulated Measured outcome

Concepts and constructs

Concepts are abstract ideas generalized from particular facts. They are characteristics associated with certain events, objects, conditions, situations and the like. They are used to classify, explain and communicate a particular set of observations. Concepts are developed out of personal or group experiences over time. The concepts developed are shared between the users and thus they form the basis for the development of new concepts. Concepts are also borrowed across fields. For example, the concept of distance is borrowed from physical is used in attitude measurement to refer to the degree of difference between the attitudes of two people. Further, we keep adding new meanings to the existing concepts, that is we broaden them as we acquire more knowledge about it. But people teed to differ in the meanings they attribute to a concept, and this may cause problems in communication. For example, concepts like personality, leadership, motivation, social class, etc. have a variety of meaning and so people may to perfectly understand each other when they use these words.

Constructs are highly abstract concepts. These are not directly tied with reality but are derived on the basis of other concepts. These are normally ideas or images specifically invented for a specific research or theory building purpose.

The difference between concepts ad constructs can be best explained through an example. A magazine wants to check the quality of the news reports it receives on various parameters. The job has been given to Vivek, the quality consultant. Vivek finds that various attributes like news coverage, grimmer, lucidity are important. These concepts are qualitatively measurable. Now he finds that these concepts can be classified under some related groups. These groups can be labeled and they represent some idea or image to describe the qualitative requirements of a news report.

Problems

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There are two major types of problems in marketing research managerial problems and research problems. Managerial problems are defined as questions raised in a business setting. Every managerial problem may not require research. Where there is a need for research, one needs to define research problem. Research problems are the restatements of managerial problems so that the researcher understands the problem the decision-maker is facing. The objective of marketing research thus becomes solving the managerial problems by finding a solution to the respective research problems.

DrinkIt, a soft drink company with a strong trendy image, now intends to target the older generation in order to expand its market. They have several alternatives before them. They can either shift the brand image and project it as a soft drink for all ages or they can introduce promotional campaigns with the message that the older generation can project themselves younger by consuming DrinkIt. Or they can introduce a new product for the older generation, with different packaging and a different brand name. This is the managerial problem.

Now the research problem can be stated as, "Will the older generation like to project themselves younger? How will the younger generation react to each of these alternatives? Will too many brands create confusion? "

The research problems are questions about the interaction between two or three variables or concepts. To further analyze these problems, a hypothesis is prepared.

Hypothesis

Hypotheses are conjectural statements of the relationship between two or more variables that carry clear implications for testing the stated relations. They further classify research problems into statements which can be tested. These can be considered as probable answers to the research problem. In the above example, the hypothesis statements can be as follows:

The older generation feels that projecting themselves as young means accepting that they are old.

The older generation feels that they should be accepted as they are.

The younger generation will lose interest in a drink that is meant for all ages.

People who consider themselves as neither young nor old may get confused with two brands.

Types of Hypotheses

Descriptive Hypotheses: These are propositions that describe the state of a variable. For instance, one can hypothesize that the market share of Maruti is more than 50%.

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Relational Hypotheses: Relational hypotheses describe a relationship between two variables with respect to each other. The dating allowance example used earlier is of this type. Similarly, an increase in market share (DV) due to improved features (IV) is an example of a relational hypothesis. There are two types of relational hypotheses, Correlational and Explanatory hypotheses. Correlational Relationships state that two variables occur together without implying that one is the cause for the other. In such cases, the two variables occur together, but we do not know of any other variables that might cause both of them. Also, we have not developed enough evidence to make a strong claim. For example, we have a hypothesis stating that monsoon and demand for coffee are directly proportional to each other. These two occur together, but we do not have any evidence to prove that one of them causes the other. The explanatory or causal hypothesis have two variables interrelated to each other such that one implies the other.

Laws

Once a hypothesis is verified by numerous researchers, in different situations, the relationship between the variables may be considered a law. A law can be defined as a well-verified statement of relationship about an invariable association among variables. In business, we do not have many well-established laws, as the relationships are not fully invariable. We only have tentative laws that are the only to some extent. This is because of the presence of a number of IIVs and MVs in real situations. Theories And Models

A theory is a set of statements that explains or predicts a phenomenon of interest. These statements may be facts, concepts, constructs, hypothesis or laws. Theories are always grounded in reality. An imaginative statement can at the most be called a hypothesis, but not a theory. These theoretical statements guide marketing researchers in conducting future research, and they also guide managers in decision-making. Theories narrow down the range of facts that researchers need to study. They also suggest methods for tackling a problem that are likely to yield the most accurate results. Such theories may also provide or suggest a system in which researchers can fit in the data, classify and analyze it. Theories are also useful for predicting facts that need to be found.

If a system is represented in terms of symbols or physical analysis with the purpose of simplifying the understanding, testing and analysis of it, it is known as a model. They represent phenomena through the use of analogy and can thus explain theory better. There are three major functions of a model description, explication and simulation.

Descriptive models seek to describe the behaviour of elements in a system where the theory is inadequate or non-existent. Explicative models are used to extend the application of well-developed theories or improve our understanding of their key concepts. Simulation models go beyond the goal of clarifying the structural relations of concepts and attempt to reveal the process relations among them.

1.9 STEPS IN THE RESEARCH PROCESS

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The steps in marketing research process are given in Figure1.81. These steps are interdependent and simultaneous, though they are treated here as if they were sequential. For example, data collection methods are often dependent on the choice of sample and the analytical approach to be used. At the same time, the analytical approach itself is dependent on the data collection method. Thus the interdependency of these two steps requires them to be simultaneous. Thus the steps given here are only indicative of the possible major steps that can be taken in a marketing research project and do not represent the exact sequence of the steps that are taken. This sequence varies for one problem to another.

Step 1: Defining The Research Problem and identifying research objectives

Problem identification in a research project is like choosing the destination a journey. A research project without identifying the right problem is as meaningless as a journey without a destination. A problem as presented to the researcher is only a tentative problem, and it is just a statement of the problem as perceived by the decision-maker. It may not be the real problem in most cases, and it is the duty of the researcher to identify it correctly. But the researcher cannot identify the problem on his own because he does not have all the information that the decision-maker has, and hence he needs the active participation of the decision-maker in problem identification.

But in most cases the decision-makers may not be willing to give the complete details of the problem to researchers, either because they do not see the need to do so or because they would like to maintain secrecy. Further, some of the decision-makers perceive problem identification as the duty of the researchers and do not see any role for themselves in it. The reluctance of the management to discuss the problem and the lack of initiative on the part of a researcher often leads to either incorrect or partial problem identification. Obviously, if the research process is continued with such a defective problem identification and statement the results will not be of any use. This financial human and temporal resources used in that research would have been wasted. To avoid such wastage, the researcher should identify the problem in the first instance itself. Thus one can consider that in marketing research, problem identification is one of the most important steps. We can even say that right problem identification is equal to research half done.

To identify the right problem and understand all its dimensions, the researcher should ideally know the following.

The complete situation faced by the decision- maker The alternatives he can choose from The expected outcome of the alternatives The objectives of the decision-maker.

But as we have already seen, the researcher is not provided with all this information. So, it is up to the researcher to use appropriate techniques to collected the needed information For example, a researcher can analyze the problem statement given by the client word by word. This will bring out the real objectives of the client.

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Fig. 1,81: The steps involved in the Research Process

20

Defining the Research Problem and identifying research objectives

Cost/Value Analysis of the Information, Formulation and testing of hypothesis

Selection of the Data Collection Method

Selection of the Sample

Selection of the Method of Analysis

Estimate the Resources needed

Prepare the Research Proposal

Data Collection

Data Analysis

Reporting

P = Perifery of circle as market datar1, r2 = radius of circle as research objectivesF = Focus of circle as research problem

The researcher needs to understand not only the problem but also the objectives of the management. Then alone the researchers can align the research objectives with that of the managerial objectives. Towards this end the most important requirement in research process is the communication between the researcher ad the decision-maker. Better the communication between them, closer the problem statement will be to the actual problem. The problem will be further clarified, if the researcher develops a situation model. A situation model is a description of variables and their relationships to the outcomes.

Specification of information requirements

Information requirements can be derived once the research objectives are clearly established. Even at this stage, the management and the researcher need a good amount of communication between them so as to avoid collecting irritant data or missing out the requirement data.

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F

r1r2

P

Researcher should consider the data availability, data collection technique, and ability of sample to yield the required data and the techniques of analysis to be used to decide on the requirement of the data. A common temptation is to collect “all” the possible data. In aiming to collect all the possible data there are chances of missing some important data elements and including some irrelevant data elements. Moreover, the data elements collected with such an unclear objective as all the possible data will not be focused and so will not serve any purpose.

The researchers can ensure that the data collected is indeed relevant by asking questions concerning the possible findings, due to each of the data elements. They should then trace the implications of each of these findings on the decision. If the findings do to affect the decision then the data element that lead to the finding, should be dropped.

Step 2: Cost / Value Analysis of the information formulation and testing of hypothesis

The major cost constraints in marketing research are time and money. These costs are justified only by the value of information which is the result of the research. The decision-maker depends on research for some additional information that can reduce the uncertainty about the situation. But the additional data does not have any value if it is not supplied in time. So the time factor is important in research. Thus time can be considered major resources for marketing research.

At this stage of research, only a rough estimate can be made of financial d time costs. Larger the sample to be taken, the larger the costs in observational of experimental studies, because per day costs are going to remain the same. However, if one tries to reduce the time period in questionnaires and interviews, the costs will increase, often exponentially. Also, the quality of the output may come down exponentially again.

There are two methods to estimate the value of research. Intuitive method, the first of the two, relies entirely on the judgment of the decision-maker. The second approach, known as the expected value approach, uses Bayesian statistics to qualify the judgment probabilities. In both these methods, certain considerations should be taken into account to estimate the probable value of the research. The following are the essential considerations for every problem. Apart from these there may be some considerations specific to the problem.

The possible outcomes and their pay off: When a problem is being considered, if the payoffs in each of the possible outcomes are not very different from each other, then it does not matter which one is chosen. In such cases, the value of the research is very low. The higher the difference in pay off of various outcomes, the more valuable the information becomes.

The degree of uncertainty in the situation: Research is done basically to reduce the uncertainty connected with a decision. If the decision-maker does not perceive much uncertainty in a situation, then the research does not have much importance. Thus the research is more valuable in a situation where the uncertainty is greater.

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The ability of research to reduce the uncertainty: From the above discussion it is cleared that higher the ability of research to reduce the uncertainty, the greater is its value.

The risk presences of the decision-maker: Different firms and different individuals have different risk preferences. If an organizational culture is such that it prefers more risk, it will not value the research greatly. Individuals who are more risk averse value the research more than individuals who are risk takers. Thereafter appropriate hypothesis may be framed and tested.

Step 3: Selection of the data collection method

A researcher can use two types of data: primary data, data exclusively collected for the current problem; secondary data, data collected for some other purpose and which is useful in the present research. In an exploratory survey, secondary data is used more regularly because of its cost and time advantages, whereas in conclusive research the usage depends upon the case.

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SIGNS OF POOR PROBLEM DEFINTION

Poor problem formulation will come to light during the marketing research exercise. Three main signals indicate poor performance in this task.

Extensive Iteration

One early manifestation of inadequate problem definition in a research exercise is extensive iteration and reviewing of study proposals. With a growing uncertainty and lack of confidence in progress, mangers retreat to the exploratory research and to repetitive desk research. They begin to reread the study proposal in an attempt to regain the conviction that their particular line of inquiry is correct.

Difficulties in drafting research Instruments

Another induction that all is not clear problem statement terms is when considerable difficulties are experienced in drafting suitable questionnaires or other research instruments. The right questions are not asked and this is not noticed until after the survey. Lack of purpose and focus causes time-wasting and frustration.

Data Mining in Analysis

When it comes to that analytical stage of a study that was originated in poor definition, it is found that substantial “data mining” is called for an endeavour to discover “interesting” relationships. The absence of clarity and purpose in collecting data means that its content, depth, and format might to be correct, or it might. Exhaustive searching for meaning may uncover useful relationships in the data, or it might not….. Ultimately the definition of the problem is a key determinant of the effectiveness of technique application, the quality of the proposed solution and the marketing management decision in turn.

-------------------------------------------------Source: www.mcb.co.uk

The data can also be classified into survey data and experimental data depending on the method of collection used to collect it. These methods, i.e. the survey method and the experimental method, can be sources of primary and secondary data.

Table 1.9 : Major Data Collection methods

I. Secondary Research Utilization of data that were developed for some purposes other than for solving the problem at hand.

a) Internal secondary data Data generated within the organization itself, such as sales person call reports, sales invoices and accounting records

b) External secondary data Data generated by sources outside the organization, such as government reports, trade association data and data collected by syndicated services

II. Survey Research Systematic collection of information directly respondents

a) Telephone interviews Collection of information from respondents via telephone

b) Mail interviews Collection of information from respondents via mail or similar techniques

c) Personal interviews

Home interviews Intercept interviews

Collection of information in a face-to-face situation.Personal interviews in the respondent’s home or officePersonal interviews in a central location, generally a shopping mall

d) Computer interviews Respondents enter data directly into a computer in response to questions presented on the monitor

Projective techniques and depth interview

Designed to gather information that respondents are either unable or unwilling to provide in response to direct questioning.

Experimental Research Researcher manipulation of independent variables in such a way that its effect on one or more other variable can be measured.

Laboratory experiments Manipulation of the independent variable (s) in an artificial situation.

Field experiments Manipulation of the independent variable(s) in a natural situation.

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Step 4: Selection of the sample

Marketing Research, as discussed in the chapter “Introduction to marketing research” has come into existence basically because of the vast size of the market. Due to this size, it has become impossible to collect information from the entire population of a target market. Sampling is used to overcome this problem. The other reasons for the use of sampling are given in the chapter “Sampling”. There are various decisions a manager should take to arrive at a sampling plan. They are as follows:Population – determines what forms the population that provides the information.

Sampling Unit – determines the individual sampling unit. (Persons, households, companies and city blocks, etc.). This is treated as an individual unit in the process of sampling.

Sampling method – determines the method of sampling.

Probability – sampling units are selected at random and there is a known probability of each unit being selected.

Non probability – sampling units are selected on the basis of convenience or judgment, or by some other means, and so one cannot allocate to a unit a particular probability of being selected.

Sample size – determines the size of the sample to be used. This is based o the time, cost and necessary precision.

Step 5: Selection of the Method of Analysis

A given method of analysis requires a particular data element in a particular format. Since each analytical method request different data elements, in different forms, it is necessary to determine the analytical method before venturing into data collection. Again, the method of analysis depends on the nature of the sampling process and the data collection method. So, decisions about the data collection method and the method of analysis should be taken simultaneously.

Once the analytical methods have been selected and proposal approved, the researcher should design the response instruments and generate dummy data. Dummy data is the hypothetical data generated imaginatively by the researcher to check whether the analysis techniques are working as they should. This imaginative data has all the characteristics of original data. The dummy data should then be fed into the analysis tables and checked for completeness. The analysis will also expose any redundant data-elements in the original plan, and it will also reveal any missing essential data-elements.

Step 6: Estimate the Resources Needed

The resources needed for research are time, finance and personnel. Time and financial requirements are inversely dependent on each other i.e. if one likes to reduce financial

25

expenses then the research may take a longer time period, and if one tries to conduct the same research in lesser time period the financial expenses may increase. Researchers need to strike a balance between the use of these two resources.

Financial costs include direct and indirect costs like manpower, materials, overheads, etc. Many organizations have a rule of thumb for estimating the cost. For example, they can have Rs. X as fixed cost and Rs. Y as variable cost for each interview. Such formulae help in faster estimation of the resources needed.

Step 7: Prepare the Research Proposal

A written research proposal puts down on paper the management problem, the research objectives, the research methodology and the resource requirements. This will help the researcher and the decision-maker to be in perfect agreement with each other to the extent that they derive the meaning from the same words. This will ensure that the research is on the right track. Box 1.82 gives a complete description of the various elements of the research proposal.

Step 8 : Data Collection

Data collection requires trained people for ensuring the validity of the research. If a firm’s requirement does not warrant a permanent team, it can outsource personnel from data collector suppliers. When the firm hires data collectors, it should take care that these hires are well trained. Moreover, data collectors, whether external or internal should be given a complete picture of the research before they are assigned a task. This will reduce errors as the data collector’s interpretation will be in tune with that of the researcher. Research training

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Box 1.82 : ELEMENTS OF THE RESEARCH PROPOSAL

Executive Summary – a brief statement of the major points from each of the other sections. The objective is to allow an executive to develop a basic understanding the proposal without reading the entire proposal.

Background – A statement of the management problem and the factors that influence it.

Objectives – a description of the types of data the research project will generate and how these data are relevant to the management problem. A statement of the value of the information should generally be included in this section.

Research Approach – a non-technical description of the data-collection method, measurement instrument, sample ad analytical techniques.

Time and cost requirements – an explanation of the time and costs required by the planned methodology accompanied by a PERT chart.

Technical Appendixes – any statistical or detailed information in which only one or a few of the potential readers may be interested.

and evaluation of field workers can also help standardize data collection methods and reduce errors.

Step 9 : Data Analysis.

Before the data is collected is analyzed, it needs to be edited, coded and tabulated. Once it is tabulated the data is analyzed using statistical analysis. The results obtained from analysis are interpreted to some extent by the researcher. The rest of the interpretation is done by the decision-maker himself, as he understands the problem situation more clearly than the researcher. The reliability of the analysis is estimated by error estimation methods. Once the analysis is done and the interpretation is made, the researcher needs to report the research to the decision-maker.

Step 10: Reporting

Reporting is the culmination of a research effort. Since it involves communication, one should take care of the factors affecting communication. This means that the report should contain both technical detail and managerial implications. It should consist of an executive summary that mentions the managerial implications. This should be then supplemented by the technical details, so that the decision-maker can refer to them as and when needed. The report should also cover the methodology used in the research.

However, one should remember that a written report might not really invite action unless the management is very interested in it. Such an interest can be generated only if the manager is involved in the research from the beginning of the project. Also, many managers do not respond to the written word. Some managers may respond, but may misunderstand the written material. Hence the written report must be supplemented with an oral report. This oral report, depending upon the situation, can range from a briefing to a full-fledged audio-visual presentation to an executive body.

1.10 REPORT WRITING

Steps in writing report(a) Logical analysis of the research objectives(b) Preparation of the final outline on findings(c) Preparation of rough draft (d) Preparation of final / corrected draft

1.101 Layout of Research Report

(a) Preliminary pages TitleAcknowledgementPreface / ForewordContent

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List of tables, figuresAbbreviations used

Company(b) Main Text Introduction Product / Service

Statement of findings, conclusions and recommendations

Summary / Synopsis

(c) End Matter IndexBibliographySample questionnaireList of sample

1.102

Type of report Emphasis on(a) Technical Report (i) Methods and research

(ii) Assumptions(iii) Analysis of findings

(b) Popular Report Objectives, findings and recommendations (mathematical part is avoided)

1.103 Essential qualities of research report

(1) It should have adequate length to cover the research subject.

(2) It should maintain interest of the reader. For this big paras as part of discussions to be avoided.

(3) Abbreviations to be avoided.

(4) Readers are interested in quick knowledge. Hence in the beginning of report, the findings should be highlighted in executive summary

(5) Layout must be as per research objectives.

(6) No grammatical mistakes

(7) All figures must be named, analysis must be in structured manner

(8) It must show originality.

(9) Implications of the findings to be discussed

(10) Report must be attractive i.e. clean, neat in appearance

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Above format is used mostly by western corporates. The type of the format Indian Corporate use is given in last chapter.

Research Problem – To study market penetration of Surf Detergent Powder in & around Pune and to recommend marketing strategy.

Research Objectives

To find out:

(a) Which is the most commonly used detergent in the market?(b) What influences people to buy a particular brand?(c) What is the penetration level of Surf in the market?(d) To identify customer needs.(e) To design marketing strategy

Step 2 : Developing research plan

(a) Research Design – Descriptive(b) Sources of secondary data – From Indian retiailers association, name and

addresses of grocers, supermarkets were collected, from whom name and addresses of detergent users were collected. Sources of primary data – Household samples

(c) Research approach – Focus group interviews and individual sample interview. (d) Research instruments – Structured questionnaire

(e) Sample Plan Universe – Residents of PuneFrame – Detergent usersSample size – 52 / 40 (52 list of detergent users from whom 40 Surf users were picked up)Method – Systematic samplingUnit – Household

(f) Contact method – TI / PI Questionnaire for households samples.

Dear Sir / Madam,

The students of Management studies, Pune are conducting this survey, as a part of their project in the field of Maret Resarch. The purpose of this activity is to measure the penetration of Surf in Pune.

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1. Do you wash your clothes at home? Yes No

2. Do you use Surf? Yes No

3. If Surf, which sub-brand do you use? Surf Excel,Surf Excel Blue Surf Ultra Surf Super Excel Surf Excel Matic

4. What influences your decision while buying Surf? (Tick as many as applicable) Whiteness Lather Easy on hands Easy on fabric Stain removal Any other (please specify)

5. have you seen any promotional campaign of Surf? Yes NoIf yes, which one do you like the most? Lalitaji Surf Excel hai na Dho daala Dhoondhthe Reh jaaoge Any other (please specify) ________

6. Do the various schemes associated with Surf affect your purchase? Yes No

7. Would you suggest any changes for Surf in the following fields? Availability in different quantities Style of packaging More schemes to be associated with the brand Pricing Any other (please specify) ____

8. Why not Surf? Price Quality Packaging Fewer schemes as compared to other brands Any other (Please Specify) _

9. Which detergent do you most frfequently use? (Tick as many applicable) Ariel Nirma Wheel

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Rin Tide Henko Any other (please specify) _______

10. What influences you to buy your preferred brand? Friends Neighbours Advertisements Self Experience Any other (please specify) ______

11. While purchasing a detergent, what quantity do you usually go for? Less than 1 Kg 1-2 Kg 2-3 Kg 3-4 Kg More than 4 Kg

12. how frequently do you purchase detergents? Once a week Once a fortnight Once a month Once in two months

13. You prefer your detergent in: Sachets (10 gm, 20 gm, 50 gm, etc) Packets Jars Bigger containers Any other (please specify) _______

14. If your preferred detergent is not available, you go for:First Choice ______________Second Choice ___________

15. do you keep a stock of detergents in your home? Yes No

16. Most preferred detergent among people you know Surf Ariel Nirma Wheel Rin Tide Henko Any other (please specify) _______

Something about you

Name :Mr./Mrs./Ms. _________Age Group : Kindly tick whichever is applicable

< 25

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25-34 35-44 45 and above

Address:

Occupation:

Do you own a washing machine? Yes No

Who washes the clothes in your house?

Yourself Maid Any other (please specify) _______

How many members are there in your household?

_________________________________

Income Group : (Tick whether is applicable)

< 5000 5,001-10,000 10,001-15,000 15,001 and above

Thank you

Step 3: Forming temporary marketing organization for collection of market data

Project Leader - 1

MR Officer (Not required)

Investigators – 1

Time to complete the project – since 52 / 40 samples to be interviewed and one sample might take 30 minutes and 30 minutes could be consumed in traveling, in one day, 8 samples could be interviewed. Hence project will be over on 5th day. No. of investigators needed is only one .

Step 4 & 5 : Data analysis by using SPSS. Data presentation and preparation of Research Report.

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Sample Composition

In all, group members as a part of our survey visited 52 households. 12 of them revealed that they were entirely dependent on local washermen or launderettes. Therefore these respondents were not considered for answer the questionnaire. The remaining 40 thereby formed the sample size of our survey.

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Sex

Male

Female

Sample size = 40

Sample Size = 40

OCCUPATION

23

107

0

10

20

30

Housewife Student Working

AGE GROUP

9

1412

5

0

5

10

15

<25 25-34 35-44 >44

Sample Size = 40

Sample Size = 40

INCOME GROUP

10

10

14

6

0 2 4 6 8 10 12 14 16

>15000

10001-15000

5001-10000

<5000

Sample Size = 40

Findings of our Survey

As a part of our survey, we visited 52 houses. It was found that 12 households gave all their clothes to launderettes, while 40 households washed their clothes at home. Since the

34

Sample Size = 40

12

22

6

05

10

152025

1

Yourself Maid / servant Others

OWN A WASHING MACHINE?

50%50%

Yes No Sample Size = 40

77%

23%

Yes No Sample Size = 52

MEMBERS IN HOUSEHOLD5%

10%

14%

19%24%

28%

1

2

3

4

5

6

Sample Size = 40

objective of our survey was to find out which detergent is popular in the households, we did not take into consideration the 12 who depended entirely on launderettes.

Of the 40 people interviewed, we found that 21 households used Surf, while 19 of them washed their clothes with other detergents. This is a clear indicator of the popularity and the presentation of this particular brand in the consumer’s mind and in the market.

3. Sub-brands of Surf used

Of the 21 consumers using Surf, it was found that Surf Excel & Blue as a sub-brand was the most commonly sued, with 17 consumers stating it as their preference. This was followed by Surf Ultra and Surf Super Excel with 2 consumers each. However, no users could be detected for Surf Excel Matric.

4. Influential factors while buying Surf

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Sample Size = 40

48%52%

Surf

Others

17

2 20

0

5

10

15

20

Surf Excel Surf Ultra Surf SuperExcel

Surf ExcelMatic

Users of Surf 21

37%

10%10%

24%

14%

5%

Whiteness

Lather

easy on hands

easy on Fabric

Stain Removal

OthersSample Size = 21

As indicated above, whiteness that the detergent provides, say 8 of the consumers, is one of the most potent influences while buying the detergent. The second most important influence is the fact that it is easy on the fabric, say 5 of them. Other influential factors are its gentleness on hands and its good stain removing capacity (Daag dhoondte reh jaaoge).

5. Consumer awareness with respect to the advertising campaign of Surf.

Of the consumers surveyed, awareness with respect to advertising by Surf was cent per cent-that is, all consumers using Surf were aware of its promotional campaigns and all ahd been Surf ads at one point of time or the other. Of the different tpes of ads aired by the media, the lalitaji ad held the greatest retention power and linking, with 7 out of the 21 consumers liking it the most, followed by the ad for Surf Excel and Dhoondthe Reh Jaaoge, with a fan following 5 consumers each.

6. Persuasive Powers of variousSchemes, which are launched by Surf to promote sales, are generally not THE major criteria when the consumer goes in for a purchase. This is also reflected by the survey in which 13 out of 21 of the consumers supported the fact. Only 8 were those who were affected by the schemes propagated by Surf.

7. Suggestions provided by the consumers

The following change were suggested in the Any other category.

Demands for a measuring scale so as to avoid wastage of powder. Change in the colour of the detergent powder. Fragrance of the detergent.

8. Reasons for not using Surf.

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32%

24%10%

24%

10%

Lalitaji

Surf Excel hai Naa

Dho Daala

Dhoondte RehJaaogeAny Other

Sample Size = 21

0

1

2

3

4

5

Price Quality Packaging FewerSchemes

Others

Sample Size = 19

Price of the detergent and association with fewer schemes were the two primary reasons for which consumers preferred other brands to Surf. Price was a factor for users of cheaper washing powders such as Nirma, Rin and Wheel. Users of Henko, Tide and Ariel insisted that the quality of their detergent was superior to that of Surf.

9. Detergents (other than Surf) frequently used by consumers

Amongst many existing brands available (excluding Surf) in the market, the most frequently used ones are Rin and Wheel followed by Ariel with others (Local) constituting the rest of the market. In the chart indicted above, 19 were nonusers of Surf, while 5 of them also preferred an additional detergent besides Surf.

10. Major influencers while making a purchaseFactors No.Friends 3Neighbours 4Advertisements 13Self-experience 19Others 1Total 40While conducting the suryve, personal experience of using the product along with many others over a period was major influence while indulging in the purchase. Apart from this, effective advertising was a close runner-up and was largely responsible in influencing people while buying their preferred brand.

11. Quantity usually purchased

Quantity No.Less than 1 kg 101-2 kg 202-3 kg 63-4 kg 3

37

2

1

1

7

7

2

4

0 2 4 6 8

Others

Tide

Rin

Wheel

Ariel

Sample Size = 24

More than 4 kg 1Total 40

As is predictable, due to the fact that the survey was done in an area which was a middle – class one, the housewives usually went in for the 1-2 kg pack and the frequency of purchase was once in a month which is depicted in the cahrt above.

12. Frequency of purchase

Frequency No.Once a week 3Once a fortnight 11Once a month 23Once in two months 3Total 40

Usually households preferred to buy their stock of detergent once in a month, as is mostly the case with all stock being ordered along with the ration that comes monthly. But still many households also buy it fortnightly.

Packets (500 gm, 1 kg, 2 kg) are the outright winners in this section with more than 50% consumers in this category preferring this particular style of packaging. However, Jars were also preferred because of their multi-utility purpose after using the primary product.

14. Alternative brand of detergent

First choice Second choiceBrands No. Brands No.

Rin 9 Rin 11Wheel 8 Wheel 12Surf 10 Surf 5Tide 1 Tide 6Ariel 7 Ariel 3Henko 4 Henko 1Nirma 1 Nirma 2Total 40 Total 40

There were 10 non-users of Surf who preferred it as their first choice of purchase in case of non-availability of their preferred brand. Users of Surf voted for Ariel, Rin and wheel as their first choice given the same situation.

15. Stock of detergents

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0 5 10 15 20 25

Yes

No

Sample Size = 40

More than 50% of the households did not keep a stock of detergents at home and resorted to purchase only when the need arose.

16. Preferred detergent amongst acquaintances of consumers

The general impression that we get after conducting the survey is that Surf rules the market because it was revealed that amongst the acquaintances also Surf was the most popular brand followed by Ariel and Nirma.

Findings

In Q.No. 7, eight respondents stated that they would like more schemes to be associated with Surf. However, when they were asked that what change would they suggest in their detergent (Q.No. 8), only 5 of them suggested more schemes.

Surf Excel (17/21) is preferred by the consumers because of its extraordinary whiteness (8/21) and the fact that it is easy on the fabric (5/21).

When it comes to housewives the verdict is almost equal with 12 saying ‘Yes’ and 11 saying that they do not use Surf but when it comes to students, Surf is the clear winner with 6 out of 7 favouring the product.

Of the 40 consumers surveyed, 21 Surf and of those 21, 19 were women as Surf is more a product that homemakers use. Of the 19 non-users, 16 again were women with the rest being men who had genuine knowledge about the product and who had used it at one moment of time or another.

Of the users of Surf, all of them were more or less equally distributed when categorized according to the income group with the higher income group categories preferring Surf a little more as Surf is costlier than most of the other brands (13/21).

An interesting fact is revealed, 4 suers stated that some of their clothes were washed either by themselves or by their maids, however the expensive clothes were given to launderettes.We also find that the trend of people who are in the different categories is almost the same with almost an equal number in each category.

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One interesting observation may be possible. It is seen that the less than 25 age group of users are more inclined to sue Surf and as the age group increases the number of users decrease, this may be due to the new positioning that Surf is using where it is targeting the younger generation too, through its advertisements.

Surf is popular with acquaintances of both the users and the non-users. In the acquaintances of users section Ariel follows (5/21) while in the alternative category Nirma (6/19) and Ariel (4/19) are preferred widely.

Recommendations for designing marketing strategy

Of the sub-brands, Surf Excel was the most recognized one, so the company ought to take some measures to make the consumer aware about other sub-brands.

If possible, pricing should be reviewed, with many consumers citing it as a negative factor.

Surf being viewed as a premium product could come up with a lower priced sub-brand for more rural market penetration to compete with Wheel, Nirma and so on.

More schemes should be introduced to attract non-users. Advertising standards should be maintained, if possible improved, as

advertisements have contributed immensely to the awareness level and usuage of the product.

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40

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CHAPTER 2TYPES OF RESEARCH-DESIGNS

2.0 INTRODUCTION

In the previous chapter we examined the various steps in the marketing research process. We also discussed the benefits of the research design. In this chapter we will analyse two types of research designs viz. exploratory and conclusive. Marketing research projects can be classified into two major categories. Exploratory research and conclusive research. Exploratory research helps in discovering new relationships while conclusive research helps such situations. Decision-makers use exploratory research for a preliminary investigation of the situation. Thus the purpose of an exploratory study is to provide new insights into a confusing issue in choosing the best from various possible courses of action.This chapter makes a detailed study of the above mentioned research techniques.

2.1 USE OF EXPLORATORY RESEARCH

Exploratory research emphasizes the discovery of new ideas. Through exploration researches develop concepts more clearly, establish priorities develop operational definitions and improve the final research design.

Quite often managers face situations that are vague in nature. They may not be able to understand whether a situation present an opportunity or poses a problem for them.

Exploratory research is ideal in dealing with such situations. Decision-makers use exploratory research for a preliminary investigation of the situation. Thus the purpose of an exploratory study is to provide new insights into a confusing issue.

Usually the researcher studies the situation and identifies the main factors contributing to that particular situation. As the number of the factors affecting the bottomline of an organization may be large, the researcher then converts these factors into specific hypotheses relative to possible actions. These hypotheses are then tested by conclusive research .For instance, a television company may notice a change in the sales figures. But they may not be able to pinpoint the factors that affect the sales. These factors may be technological changes poor marketing efforts, changing consumer preferences etc. In this case, the researcher may identify two factors and then convert them into a hypotheses that is subsequently tested by conclusive research .The process can be illustrated through the following figure

2.11 Design Of Exploratory Studies

Exploratory studies are characterized by their flexibility and ingenuity. Researcher use their imaginative skill in exploratory research to develop new ideas. Their research will be based on certain hypothesis. But as they proceed ,they may redefine their approach or they may proceed with a new set of hypothesis. So the researcher’s expertise is of paramount importance in exploratory studies The researcher can take three approaches to arrive at a meaningful hypothesis.

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1 Study of secondary sources2 Survey of individual who are expert in the subject3 Case analysis

Fig. 2.1Source: Marketing Research, Harper W. Boyd, Jr., Ralph Westfall and Stanley F. Stasch.

2.12 Study of Secondary Data

Researcher can utilize the data compiled by other organizations to formulate the hypothesis. Research reports prepared by consultancies and marketing research organizations, sales data brought out by trade associates, survey reports of governmental and nongovernmental organizations are generally used for this purpose .Secondary data has proved to be quickest and most economical source for researcher.

The information technology boom has made the search for data very easy .The internet is one of the largest repositions of secondary data, available at minimal or at no cost at all. Several Information Technology techniques such as datamining help researcher collect the right data and also aid them in establishing connections. At times researcher may be confused by the information glut.

In such cases, the researcher should be prudent enough to select the relevant data and discard the rest. A detailed discussion regarding secondary data is given in the chapter Secondary Data.

2.13 Survey of Individuals with Ideas

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Vague problem

Exploratory Research

Hypothesis

Conclusive Research

Decision

New Idea

Researchers usually interview individuals who have a general idea about the subject and are also imaginative .They provide the necessary direction for researcher to focus on .Sales managers, sales representative and dealers can be interviewed for eliciting information. Even the consumers are approached directly by different organizations for new ideas. For instance, companies like GM has used this technique in the past. While approaching consumers care should be taken to interview a heterogeneous group.

It is absolutely important for the researcher to find new ideas. Hence research is usually conducted by interviewing people who are cooperative and have an interest in the subject being researched. Respondents should be given the freedom to express their ideas however radical they may be.

Qualitative research technique are used to collect exploratory data from individuals.These involve interviews with individuals and groups .Depth interviews or projectives techniques are used to elicit information from individuals, while focus group interviews are employed to elicit information from groups.

2.14 Depth Interviews

Depth interviews are conducted to elicit information (from consumers) that is difficult to obtain through direct interviews. Factors such as consumer attitudes and motivation are understood mainly through depth interviews. In such interviews the researcher approaches the consumer with only an outline in mind. Formal questionnaires are not used in this technique.

The interviewer may probe deeply to prompt the respondent to elaborate on new ideas. This is necessary because a direct question regarding the motivating factors behind a purchase may fail to elicit the appropriate answer. For instance, a consumer may avail of five star hotel services, because he perceives such behavior as a statement of his social status .But he may not admit the factor that actually mot6ivates him. The researcher has to probe deep into the consumer’s mind by asking several indirect questions. Procter & Gamble conducts depth interviews to identify ambiguity in consumers’ answers and to understand the true meaning of their responses. If a consumer says she uses a certain brand of shampoo because it does best job of getting her hair clean, researchers ask her, what does she mean by “clean”? Does “clean” mean the way it feels or the way it looks? Does “clean” mean free of dirt or nongreasy or free of dandruff or a less itchy scalp? Is “clean” hair squeaky, slippery, lively, bouncy, fluffy, shiny, easy-to-comb, or manageable? Only researchers with extensive experience and training will be successful in eliciting the information needed without any bias.

One major disadvantage of depth interviews is that their results cannot be compared as interviewers have different interview styles. Another major disadvantage is the difficulty in analysing the data. The data available is highly subjective and varies from one analyst to another.

2.15 Projective Techniques

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Projective techniques are used to provide extremely useful data regarding the attitudes and values of the respondent. These techniques are based on the theory that the interpretation of any vague object or picture will reflect the individual’s background, attitudes and values.

There are four categories of projective techniques. They are association, completion, construction and expression.

Association Techniques

In this technique the respondent is required to respond to the presentation of a stimulus with the first thing that comes into his mind. The respondents should give the first word that comes to their mind in a free word association technique while they should give a series of words or thoughts in a successive word association technique. This technique is normally used for testing potential brand names. It is also used for measuring customer attitude about products or product attributes.

Completion Techniques

In this technique, the respondent is required to complete an incomplete stimulus.Researchers use two types of completion techniques : sentence completion and story completion.

This has proved to be an effective technique to understand more about a respondent’s attitudes and values. Normally, if a question is posed to respondents, they come up with an answer. But if they are asked to complete a sentence or a story, they will express a more revealing answer. For instance, an incomplete sentence like, “People who like white rum are .........” will induce the respondents to come up with an answer that they genuinely feel is true.

The story completion method is similar to the sentence completion technique. Here the respondents are asked to complete a story told to him. Usually, a specific situation, like a couple visiting a shopping mall and having a disagreement over the purchase of a product, is presented to the respondent for completion. It has been found that the respondents will build the story using their own experience and attitudes.

Construction Techniques

Construction techniques are quite similar to completion techniques. These techniques require the respondent to construct a story, dialogue or description.

In the cartoon technique the respondent may be asked to fill in the dialogues in a cartoon. In the picture response technique, a picture will be shown to the respondents and they will be asked to interpret it. The picture will be vague so that the respondent has to use his imagination to interpret the picture.

2.16 Focus group interviews

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Researchers conduct focus group interviews by bringing together consumers who have a common interest. The group will be interviewed by a researcher (who acts as the moderator). A large number of organizations now employ focus group interviews to gain more insights into customer preferences and expectations. Focus group interviews provide qualitative inputs and normally do not measure the subject qualitatively. For instance, Procter & Gamble uses this technique “to develop hypotheses for further exploration” or “to help design” a quantitative study. It uses a lot of focus groups to gain insights - such as to explore whether using any fabric softener has any perceived connection with being a good mom or what kinds of reactions consumers might have to a new way of demonstrating Bounty paper towel’s absorbency. But, it doesn’t make road generalizations and major decisions based on three or four focus groups*. Of late, research organizations have started recording the proceedings in order to do a detailed analysis later.

In a focus group interview, the moderator normally briefs the group about the topic to be discussed. Then the moderator throws out some questions to the group. These questions will usually be simple, often aimed at breaking the ice. For instance, they may ask questions like, ‘what do you think about the product?’ Such questions are easy to answer. This will slowly help generate a discussion among the group. Once the atmosphere is relaxed, the moderator may bring up more specific issues and carefully watch the proceedings so as to check whether the group is coming up with new ideas. Towards the end of the discussion the moderator may give the group a task. The moderator then leaves the room and watches the proceedings through a television (or a one way window) to see if the discussion has caused the client to think of any more questions to ask.

Normally the researcher will ask around nine to twelve questions. The moderator also informs the group that the proceedings are being watched by another group (clients/researchers). Usually, the discussion is watched by the organization’s staff through a one way window.

Special care has to be taken to see that the moderator blends with the group. If the moderator is of the same age group and sex, the group members will express themselves freely. Normally a group consists of 6 to 12 people. However, groups can range from one to a dozen. This depends on the size of study being conducted. Members for the group are selected on the basis of their familiarity with the product. And if they are also articulate they will contribute more effectively to study. The sample should be dominated by the segment important for the project. It has been found out that different forms of group for different segments yield good results.

1 Synergism : The combined effect of the group will produce a wider range of information, insight, and ideas than will the cumulation of the responses of a number of individuals when these replies re secured privately.

2 Snowballing : A bandwagon effect often operates in a group interview situation in that a comment by one individual often triggers chain of responses from the other participants.

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3 Stimulation : Usually after a brief introductory period the respondents get “turned on” in that they want to express their ideas and expose their feelings as the general level or excitement over the topic increases in the group.

4 Security : The participants can usually find comfort in the group in that their feelings are not greatly different from other participants and they are more willing to express their ideas and feelings.

5 Spontaneity : Since individuals aren’t required to answer any given question in a group interview, their responses can be more spontaneous and less conventional, and should provide a more accurate picture of their position on some issues.

6 Serendipity : It is more often the case in a group rather than individual interview that some idea will “drop out of the blue”.

7 Specialisation : The group interview allows the use of more highly trained, but more expensive, interviewer since a number of individuals are being “interviewed” simultaneously.

8 Scientific scrutiny : The group interview allows closer scrutiny of the data collection process in that several observers can witness the session and it can be recorded for later playback and analysis.

9 Structure : The group interview affords more flexibility than the individual interview with regard to the topics covered and the depth with which they are treated.

10 Speed : Since a number of individuals are being interviewed at the same time, the group interviews speeds up the data collection and analysis process.

2.17 Case Analysis

The case method involves examining a single or multiple situations when an organization is addressing a problem. The situation may involve factors that are interrelated. The organization may find the case method to be of absolute value sine it involves an in-depth examination of the problem. For instance, an organization which has noticed some variation in the quality of the product manufactured may feel that this variation is primarily due to several factors that may include proper design, testing, manufacturing processes, labor constraints, etc. In order to deal with the problem, they may take up a case that is similar to the situation at hand. The situation is analyzed thoroughly, thus helping them to arrive t a hypothesis.

2.171 Case Method Design

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Researchers use analogy as a method of analysis in cases. Through cases, researchers attempt to find

Common features to all cases in a general group Features which are not common to all groups, but common to some subgroups Features unique to a specific case

Features that are common and those that are uncommon are analysed thoroughly to formulate hypothesis. Researchers should be careful in selecting the cases for analysis.

Advantages of Case Method

Advantages Cases are studied comprehensively, taking into consideration all aspects.

Unlike statistical studies which involve abstracts from real situation, case study describes a real-time situation.

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Box 2.16: KEYS TO SUCCESSFUL FOCUS GROUPS

Focus groups can be an effective marketing research tool. But like tools they need to be used properly in order to provide meaningful results. The most successful focus groups include the following characteristics.

Appropriate research objectives : Robert Bohle, President of Focus on Issues, at a St. Louis-based marketing, consulting and research company, says the primary purpose of focus groups is to test and develop hypothesis. “Focus groups help define various customer population segments. They help companies make better judgements”, says Bohle.

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William Newbold ,supervisor of marketing research at Detroit Edison adds, “Focus groups are ideal for concept testing ,copy testing and preliminary advertising testing. ”The focus group format allows the moderator to change things on the fly and retest it. " When you need a real fast turnaround and fast input, "focus groups are appropriate", Newbold says. Bohle points out, however, that focus groups have a major limitation - they only provide directional information.

The central figure in focus groups is the moderator, who guides and leads the discussion. This role is crucial to the overall success of the groups. However, a good moderator must walk a tightrope between asking questions and eliciting feedback from all the respondents. "The moderator has to be able to manage without leading (the respondents) and has to be able to control strong personalities in the group", Newbold says. "A moderator is 70 percent of what you get from a focus group. The moderator has to make everyone feel important, so they will talk."

"You need a good moderator who knows the issues but isn't defensive - a moderator can't be too close to the issues. It should be a third party", says Robert Sitkauskas, director of communications technology for Detroit Edison's VRU system. Bohle agrees. "The discussion guide and the moderator are key. The most important part is the ability of the moderator to listen and probe without passing judgement."

Good recruiting: Another key to good focus groups is proper recruiting. Good representation is crucial for achieving meaningful results. "The recruiting should be really representative of he customer base", says Newbold. Representative and balanced focus groups were one reason Detroit Edison's VRU groups were so successful. Sitauskas says focus groups are ideal for eliciting customer response from a variety of demographic groups relatively quickly and easily.

Well planned discussions guide : While Detroit Edison's focus groups had a clear agenda, Detroit Edison was careful to build flexibility and fluidity into the groups. "You should have an genda, Sitauskas explains, "but not a rule -based agenda." Bohle adds that a discussion guider should be just that - a guide. Part of the success will depend upon the ability of the moderator and the respondents to go beyond the original guide and delve into the important underlying issues. Indeed, the accessibility issue was never a part of the original focus groups moderator’s guide. The utility thought power outages were the problem. However, the moderator uncovered inaccessibility as an underlying problem.

Proper environment : Creating the proper environment is another key to the overall success of focus groups. To be truly effective, the research sponsors must establish the proper setting. "The setting has to provide the kind of environment where you can communicate not what you want to hear." To be truly effective, the research sponsors must establish the proper groups. " The setting hs to provide the kind of environment where you can communicate not what you want to hear, but what you ought to hear," Bohle says.

Interpretation : Focus groups are meaningless if the findings are not interpreted correctly. You need someone insightful to draw the conclusion from the groups. Newbold says "People can jump to conclusions based on focus groups and can be misled by one strong personality. We advocate holding multiple groups."

Since the researchers will be in association with the respondent for a longer period, they will develop an informal relationship. This relationship will help in collecting more data. Moreover, the data available will be accurate.

Some of the disadvantages of case study are :

Case analysis is very subjective.

It is difficult to have a formal research method.

Researchers tend to generalize the situations, though the case may not call for such generalizations.

2.2 CONCLUSIVE RESEARCH (DESCRIPTIVE RESEARCH OR EXPERIMENTAL RESEARCH)

Conclusive research provides information that helps the manager to evaluate and select a course of action. The decision-maker will have to choose one course of action from different alternatives. Conclusive research provides the relevant information to help the manager arrive at a decision. Conclusive research design is characterized by formal research procedure. Research Objectives are clearly stated and information needs are explicitly stated in this type of research. A formal study procedure achieves a variety of research objectives : description of phenomena or characteristics associated with a subject population, estimation of the proportion of a population that have these characteristics, discovery of association among different variables and discovery and measurement of cause-and-effect relationship among variables. Conclusive research ca be classified as either descriptive or experimental. We will start the discussion with descriptive studies.

2.21 Descriptive Research

Unlike exploratory studies, descriptive studies are characterized by a formal design and an accurate description of the problem. This helps in identifying the information required and ensures that it covers all the areas required. It is imperative that the design of descriptive studies be such that it specifies the source of information and the data to be collected from those sources. This is done mainly to ensure the accuracy and the appropriateness of the information collected. It is equally necessary to prevent the collection of any unnecessary data associated with such research.

There are basically two types of descriptive studies.

1. Case method2. Statistical method

Case Method

This method is not often used in descriptive research. It is more widely used in exploratory research. The procedure is the same as in exploratory research. The only difference between the two lies in the fact that while exploratory research offers flexibility, descriptive research is more structured, clearly defining the research problem and the points to be investigated.

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Statistical Method

This is the most widely used method in Marketing Research. It makes use of techniques that vary from simple means and percentages to very sophisticated techniques. In this method, a limited number of factors from a large number of cases are studied in-depth.

Use of statistical method

Statistical tools are used by most marketing research professionals to understand the dynamics of the market. Data is usually collected through observation or through interviewing.

Surveys to find the consumption patterns of children ad generate profiles of the people using the internet to make purchases are examples of the statistical method. Let us see how Forrester research, the leading global marketing research agency, has studied online purchases. They have discovered that the average online buyer reports purchases in two product categories : books and software.

The following are some of the observations made by Forrester on line purchases.

Media and technology lead net shopping. More than 50% of online shoppers buy software and more than 40% buy books. Buyers spend an average of more than $100 over three- month period.

More than 250,000 North Americans bought cars and computers online.

Three percent of online consumers report online securities trades.

Advantages of Statistical Method

Statistical study involves a large number of interviews or observation. Statistical techniques are used specifically for mass data. Two different researchers conducting statistical research will arrive at the same results, while two people using the case study method may not arrive at the same results. This is mainly because of the subjectivity of the case study method. Statistical study helps the researcher to make more accurate generalizations. If the sampling is properly done, the generalization will be universally true.

Disadvantages

Fails to prove cause-and-effect relationship Direction of causal effect is not clear in statistical studies.

Causal Research

Causal research is also a technique used to perform conclusive research. It attempts to specify the nature of the functional relationship between two or more variables in the problem model. Managers analyze the impact of advertising etc. through causal research. For instance, once

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they give an advertisement they study how the advertising has caused the sales to change. Usually conclusions are drawn from three types of evidence.

Concomitant variations Sequence of concurrence Absence of other potential casual factors.

Concomitant Variations

Advertising expenditures for an organization vary across geographic regions. An organization may notice the variations of sales across the region. It may also notice that sales revenue is high in those regions where the advertising expenditures are high and is low in those regions where advertisement expenditures are low. So it may infer that sales revenue is directly proportional to advertisement expenditure. But this has only been inferred, not proved.

Sequence of Occurrence

This is another type of evidence that can be used to make inferences about causation. Some events that happen first, cause the next event to occur. In such cases, researchers can infer that the first event has caused the second event.

Absence of Other Potential Factors

If the causative factors are identified the researcher will be able to eliminate all the factors except the one that he believes is the real causative factor. Then he can establish the same as the real causative factor. Quite often it is difficult to eliminate all the possible causative factors.

2.22 Experimentation

Experimentation is another method used for conclusive research. It can be used to find the cause and effect relationship between two or more variables. This is usually done by manipulating one or more variables (known as the dependent variable). Unlike observational studies, the researcher systematically alters the variables of interest and observes the changes that follow.

There are two types of experimentation, laboratory and field experiments. Laboratory experiments are more controlled but are conducted in an artificial environment. The environment is totally man-made. Field experiments are done in a natural environment and so are less controlled, but yield more realistic results.

Advantages

The degree to which the certainty of the causal relationship between two variables can be established is highest in experimental studies. This is mainly because the experimenter can

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manipulate the independent variable and directly observe the effects of this manipulation on the dependent variable. At least in theory, the ability to manipulate is unlimited and the relationship can be established with complete certainty. The use of a control group is useful to assess the existence and potency of manipulation.

In experimental designs, one can isolate the experimental variables and thus avoid the effect of the extraneous variables on the results. The presence of control group assists further, due to the possible comparisons and the exclusion of the effects of the extraneous variables.

Experimentation is a highly convenient method compared to others since the experimenter can adjust variables so as to achieve the extremes which are not observable under routine conditions, i.e. the manipulative control on the independent variable is high. Moreover, the researcher need not wait for the fortuitous occurrence of a particular incident to measure its effect, but can manipulate the variables to achieve the same.

The experiments if repeated with various groups and various conditions can give rise to generalized theories about the relationship between the given variables. Once a theory is established, the need for many similar experiments is reduced.

Researchers can use field experiments, to minimize distortions due to the intervention of the researcher on the results. Such experimentation also generally needs less financial resources than other methods.

Disadvantages

In laboratory experiments, artificiality is the main disadvantage. There are many internal and external validity problems unique to each experimental design. These are discussed in the following sections. Experimentation is not possible of the past and predictions based on experimentation are not possible for the distant future. So, it is applicable only to current problems or problems of the near future. The most important disadvantage is that the marketing research concerns itself with people and their behaviour, and so does not yield itself for thorough manipulation and ethical considerations limit these further.

Experimentation is a familiar technique used by one and all in their daily lives. But it has been structured, theorized and systematically practiced by the scientific community to develop scientific knowledge and use it for social benefits. In this technique, one identifies the change that takes place in a variable because of changes in other variables under controlled conditions.

Though ancient physical scientists like Archimedes used scientific methods, the behavioural sciences did not take to experimentation in its systematic form until recently. This is due to the fact that it is not possible for behavioral scientists to control all the variables of the experimental environment to the extent it can be done in the physical sciences. However this technique has recently gained popularity particularly due to the success of the technique in theorizing the learning theories.

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In chemistry, for example, the scientist adds two chemicals, under controlled temperature, pressure and other variables, to find the effect of these on the reaction. In marketing research, the researcher observes the purchase behavior of a customer, while controlling the variables like the amount of exposure to the promotion, store layouts, etc. The difficulties in applying the experimentation in marketing research lies in the fact that experimentation needs control of variables, and it is not easy to control variables in markets. The difficulties in doing so have been explained in the chapter “Scientific Method”.

In the chapter “Types of Research”, we read about the various advantages and disadvantages of experimentation in comparison with other techniques. This chapter deals with various types of experiments based on the settings namely, the laboratory and field settings. To make the discussion easier, it also defines the terminology and symbols used in the latter section. Further, the potential threats to the validity of an experiment are discussed and various designs of experiments are also discussed in detail with their advantages and disadvantages.

2.7 SOME ADDITIONAL TYPES OF EXPERIMENTAL RESEARCH DESIGNS

A) After only design : This design consists of measuring dependent variable after exposing independent variable at Test units.

B)Input Test Units Output

Explanatory Variables

Independent variables likeProduct, Price, Place andPromotion

Extraneous Variables

Un-Controllable VariablesLike competition

Samples andTerritories

Dependent Variables

These are Sales, ad-recall, attitude, marketshare etc.

Examples :

a) Marketer distributes discount coupon to the consumers to buy the brand. The study measures the extent of coupons redeemed by the sample covered. Britania’s campaign, ‘britania khao, world cup jao’, involved scratch coupon to be surrendered to the retailer for matching the number.

b) Reliance Industry Ltd.,, in the month of June-July every year disdtributes dividend warrants, along with which it also sends to shareholders 20% discount coupons for buying Vimal Fabric. In the month of February it conducts research to study number of coupons redeemed by the shareholders.

c) Before-After Design : In this design the dependent variables are measured across test units with specific independent variable, once before the independent variable is

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exposed to test units and again after exposure. The difference between the two measures is treated as the effect of experimental variable.

Example : Pepsi Co. measured the sales before launch of ‘Oya babli’ campaign and after the ad campaign. It noticed healthy growth in the sales due to classic picturisation and content of the ad.

C) Before-After with Control Group : In this design the research study includes a control group in the experiment, but the control group is not subjected to the experimentation.

Suppose the marketer wants to study the movement of market share with reference to price reduction, then the impact of experimentation is studied as follows :

Measurements Experimental GroupMarket Share

Control GroupMarket Share

Before Experiment

After Experiment

E1

E2

C1

C2

Mathematically, impact of experimentation = {(E2 – E1) – (C2 – C1)}

Example : Most of the telecom companies reduce the tariffs, initially only for say one circle, study the impact of reduction in tariff on sales and market share and then repeat same strategy at other circles too. In such cases, the circle where benefit is offered is called Experimental Group, whereas the consumer outside the circle is called Control Group. They do not participate in experiment, but hope to get the benefit at later stage and hence buy the pre-paid or post-paid of same company which offered benefit at other circle.

2.71 Experimental Research Design Case Study

Case Study : Experimental Research Design

Sr. No.

Name of Company

Brand Details of Experiment Time period of experimentation

Impact

1. AT&T Idea Life long recharge Rs.995, local calls Rs.1.99, STD calls Rs.2.99. In case of Post-paid all calls At Rs.0.99

1st Jan. 2006 to 31st Jan. 2006

Three lacs new consumers added

2. Reliance Reliance India Mobile

(1) Buy Reliance hand-set at Rs.2500 and get equivalent free talk time.

1st Dec. to31st

Dec.2005Ten lacs new consumers added. (Total Consumers 170

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lacs as on 1.1.2006.

(2) Recharge forever

@Rs.1,000, call any-Where in India. Re.1SMS within the stateAt Re.0.01

1st Jan. to31st Jan. 2006

Five lacs new consumers added

3. BHARATI TELECOM

Airtel Life-time recharge Rs.999 1st Jan to31st Jan. 2006

Three lacs sixty thousand new consumers added

4. HLL Liril a) Change of ad-agency from Lowe India to Mc Cann Erickson. from freshness, youth & exuberance to a youngcouple in a naughty mood with a slow humming jingle ‘l-ee-ra-ee-ra’, exhibiting husband wanting to catch the wife nearby bathroom.

November2005 No change in brand sales

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CHAPTER 3SOURCES OF SECONDARY DATA

3.0 INTRODUCTION

Once the research process starts, the researcher charts out the research objectives. Then the researcher turns his attention to the research design and determines the sources of marketing data. At times, researchers make the mistake of conducting primary research for collecting data while data is available from secondary sources at a lower cost. Moreover, the secondary data sources provide information that may not be obtained by other external agencies. However, the researcher should evaluate the secondary data for its reliability and accuracy. The researcher should also check whether the available data will fulfill the requirements.

In this chapter we will discuss the nature of secondary data and its advantages and disadvantages. We shall also survey the sources of secondary data.

3.1 THE NATURE OF SECONDARY DATA

Secondary data is available from publications, in-house databases, research agencies etc. It constitutes readymade information that can be used for research purpose with minimal analysis. However, the researcher should bear in mind that secondary data is published for purposes other than the current research.

Collecting primary data involves field work and further analysis on the data collected to arrive at a conclusion. For instance, a marketer who wants to launch a particular product may be interested in collecting data regarding the buying habits of consumers in that particular region. The marketer can conduct field surveys to collect the relevant data, which, in turn, can be analyzed to arrive at a proper conclusion. But at the same time, he can refer to any published material that has already done an analysis. While the first method is tedious, time consuming, and expensive, the second method, which is collecting secondary data, is fast and inexpensive.

3.2 ADVANTAGES OF SECONDARY DATA

One of the main advantages of secondary data is that it is quite inexpensive. A small start-up company study the market to launch a product may not be able to afford to do primary research. By getting hold of good reports and articles, such small organizations will be able to do the study cost effectively.

Secondary data helps researchers save time. While primary research takes a considerable amount of time in the form of collecting and analyzing the data, secondary data offers readymade solutions.

If the demographics of a particular region have to be studied, the researcher has to collect the statistics of the population. It is impossible for any organization to conduct such a census study. Here too, secondary data published by a government organization will be of

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considerable use. Moreover, data collected and published by the government will be less biased.

3.3 DISADVANTAGES OF SECONDARY DATA

The major disadvantages of secondary data are Relevance Accuracy Sufficiency Availability

3.5 TYPES OF SECONDARY DATA

Internal and External Data

The data available within the organization, which may be published for purposes other than the problem at hand, is called the internal data. Internal Data may be sales reports, accounting records, inventory reports, budgets, profit and loss statement, etc. External data is the data available outside the organization. This can be data made available to the organization by external research organizations. Syndicated sources publish and sell data periodically and library sources include information from a wide array of publications.

Census Data

In the US, the Bureau of Census publishes census data. Data published by the Bureau of Census is known for its quality and credibility. In India, the census report is published by Registrar General of India. The survey is usually done once in ten years. Census data contains demographic information. The report contains information on different aspects such as age, sex, education and occupation. But one of the main disadvantages of census data is that it has a time lag of three to four years. Yet this is the only demographic data recognized by users as authentic.

Commercial Information

Organizations periodically provide information. This is also referred to as syndicated research. This service is mainly aimed at meeting the requirements of their clients. Information may be provided on a daily/weekly/yearly basis. Usually organizations providing such information conduct primary research to collect the data. Some organizations collect data from a sample growth over a period of time. This is called panel research. It helps track changes in customer preferences over a period of time.

Marketing Research organizations like ACNeilsen, Forrester Research, Gallup MBA, ORG, MARG, IMRB, etc. sell information for a price. They usually study the client’s needs and customize their reports to suit those needs. In India, quasi-governmental institutions like the National Council of Applied Economic Research (NCAER) and Indira Gandhi Institute of

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Development and Research (IGIDR) sells market information and is considered to be highly qualitative.

Consumer Purchase Data

Consumer purchase data is extremely useful for devising marketing strategies. It provides information which can be used for understanding the market share, market segments, competitors, effects of advertising, etc. Syndicated Research agencies provide such information on a continual basis.

Retail and Wholesale Sales Data

Agencies like ORG-MARG, Francis Kanoi and ACNeilsen conduct studies and provide information on sales data for a number of items. These studies are mainly on packaged foods, toiletries, cosmetics and over-the-counter drugs. Different agencies collect data at different intervals. They usually collect data from stores located in different towns.

Advertising Data

Organizations spend a huge amount of money on advertising every year. Obviously, they will be interested in knowing the readership of various newspapers and magazines. They will also be interested in understanding the television viewing patterns. The National Readership Survey (NRS) is conducted in India to assess the consumer profile of newspapers and magazines. NRS is conducted by the National Readership Studies Council. It is constituted of the Advertising Association of India, the Audit Bureau of Cir5cultion and the Indian Newspaper Society (INS). The actual fieldwork is carried out by ACNeilsen, IMRB, and Taylor Nelson Sofres Mode. In India, IMRB conducts studies and publishes data on television viewing patterns. All this information is used by or4ganisations for media planning.

Test Marketing

Some marketing research agencies provide test-marketing services. A new product, before it is launched in the market, is introduced in a test market and its availability is ensured. Then the sales data is collected on a periodical basis and reported to the client.

3.6 ADDITIONAL SOURCES OF SECONDARY DATA

3.61 Government Sources

Name of the Source Information provided1) Directorate General of Supplies &

Disposal (DGS&D)Installed manufacturing capacities & actual utilized capacities for all manufacturers

2) Directorate General of Trade & Disposal3) Reserve Bank of India (RBI) Availability of foreign currencies.4) Directorate General of Commercial Import-Export statistics

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Intelligence & Statistics5) Centre for monitoring Indian Economy (CMIE)

Economic Growth, GDP

6) Census Population, no. of families, no.of voters7) Geographic Survey of India Regionwise production of agri-produce8) Horticulture Board of India Value-added fruits, vegetables & flowers

and markets

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Name of the Source Information provided9) Directorate General of Foreign Trade (DGFT)

Import Export Regulations

10) Exim Bank Creditworthiness of importers and countries.

11) Export Credit Guarantee Corporation of India (ECGC)

Insurance covers and financial guarantees available to exporters.

12) Agriculture & Processed Food Export Development Authority(APEDA)

High Tech Agri Farming, technology tie-ups, seed capital, inspection, etc.

13) Central Statistical Organisation (CSO) Industry Economics14) National Sample Survey (NSS) Per Capita consumption & monthly per

capita income, literacy per state, employment across male & female etc.

3.62 Non-Government Sources

1) Org Marg TRP ratings, Retail Store Audit2) INSDOC (private Library) Any publication after 19703) Path Finder Household disposable income & consumer

behaviour.4) University Public Relation Offices Various courses, fees, duration and

eligibility.5) Yellow Pages & Ask Me Classified information6) Internet Sites Classified information7) Indian Association of Retailers No.of Retailers, their classification, types,

etc.8) J.D.Power Asia Pacific Customer satisfaction

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CHAPTER 4HYPOTHESIS

4.0 INTRODUCTION

In the chapter on preparation and tabulation of data we discussed the appropriate procedures for collection and tabulation. Once we tabulate the data we need to analyze it, i.e. is we should verify the hypothesis stated in the problem. To do so we need to learn hypothesis-testing methods. If the manager of a shopping mall wants to find out if customer satisfaction is at least 90 percent, we can test the validity of this hypothetical parameter by the use of hypothesis testing. Hypotheses test, also known as tests of significance, enable us to decide on the basis of the sample results if the deviation between the observed sample statistic and the hypothetical parameter value (or) statistic is significant (or) might be attributed to chance (or) the fluctuations of sampling.

4.1 METHOD OF HYPOTHESIS TESTING

Definitions of Hypothesis

(i) Hypothesis – It is a statement or assertion about the statistical distributor or parameter of statistical distribution. Alternatively hypothesis is a claim to be tested.

(ii) Null hypothesis – A hypothesis of ‘no difference’ is called null hypothesis

(iii) Alternative Hypothesis – It is a hypothesis to be accepted in case null hypothesis is rejected. In other words, a complementary hypothesis to null hypothesis is called alternative hypothesis.

4.12 Steps In Formulating And Testing

Testing for statistical significance follows a well-defined pattern. Though one may not be able to understand all the terms in these steps at this stage, we are mentioning them here. They will be discussed in subsequent chapters. The steps are as follows:

State the null hypothesis: The null hypothesis must be stated.

Choose the statistical test: The choice of the statistical test is dependent on the power and efficiency of the test, the nature of the population, the method of drawing the sample and the type of measurement scale.

Select the desired level of significance: The exact level of choice depends on how much Alpha risk one is willing to take in comparison with beta risk (Alpha risk and Beta risk are explained later in this chapter).

Compute the calculated difference value: After the data is collected, the formula for the appropriate significance test should be used to obtain the calculated value.

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Obtain critical test value: The critical value for the calculated value should be looked up in the appropriate tables. The critical value is the criterion that defines the region of rejection from the region of acceptance of the null hypothesis.

Make the decision: For most tests, if the calculated value is larger than the critical value, we reject the null hypothesis rejected and it is conclude that the alternate hypothesis is accepted. If the critical value is larger, we conclude we have failed to reject the null.

4.13 Formulating A Hypothesis

The first in hypothesis testing is stating the hypothesis itself. A hypothesis to a problem can be basically stated in two ways – Null hypothesis and Alternative Hypothesis.

Null Hypothesis: In tests of hypothesis we always begin with the assumption (or) hypothesis called Null Hypothesis. The Null hypothesis asserts that there is no significant difference between the statistics and the population parameters; and whatever observed difference is there is merely due to population. It is denoted by the symbol H0. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data bring out the contradiction.

In the above example, the null hypothesis is that the average purchase has not changed from Rs. 1500. it is represented by

H0 : μ (mu) = Rs. 1500

Alternative Hypothesis: Alternative hypothesis is complementary to the Null hypothesis and is denoted by the symbol H1.

In the above example, the alternative hypothesis is that there has been a change in the average purchases per week from Rs. 1500. We can have three different alternative hypotheses about this change. These are indicated below as:

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2.5% of area

Rejection region

95% of area

Acceptance region

2.5% of area

Rejection region

HA : μ (mu) ≠ Rs 1500

HA: μ (mu) > Rs 1500

HA : μ (mu)< Rs 1500

The hypothesis can be tested with a two-tailed test. The regions of rejection for null hypothesis are divided between the two tails. The second hypothesis uses the right tail for rejecting the null hypothesis whereas the third uses the left tail for rejecting it.

A hypothesis is never accepted; it is only rejected or failed to be rejected. This statistical testing is not sufficient proof for disproving a hypothesis. But instead of a clumsily saying that we have failed to reject the hypothesis, we say that we accept the hypothesis. Rejecting a null hypothesis is equivalent to accepting the alternative hypothesis and rejecting an alternative hypothesis is equivalent to accepting the null hypothesis.

4.14 Errors In Testing

The decision to accept or reject the null hypothesis H0 is made on the basis of the information supplied by the observed sample observations. The conclusion drawn on the basis of a particular sample may not always be true with respect to the population. For instance, in the above mentioned example we have a 5.0% chance of rejecting a true hypothesis in the above mentioned example.

In table 4.14, four cases are presented. When the alternative hypothesis is true, it means that the null hypothesis is false. Using this concept we can deduce that the cases are accepting a true null hypothesis and rejecting a false null hypothesis from the table it is clear that in any testing problem we are liable to two types of errors.

Type-I error: Rejecting a true null hypothesis is called a Type-I error. It is compared to convicting an innocent person. This is considered a serious error and researchers generally try to minimize its occurrence as much as possible.

The probability of rejecting a true null hypothesis in the above example is 5%. This indicates the probability of a type I error. It is denoted by α.

Here, α = 0.05, or 5%

The region between the acceptance and rejection region is called the critical value. In the above problem the critical values are Rs. 1470 and Rs. 1530 at a given significance level of 5%. Alternatively, for a given significance level we can calculate the critical values above or below which a hypothesis can be rejected or accepted.

Type-II error: Accepting a false null hypothesis is called a Type II error and is compared to acquitting a guilty person. It is difficult to detect such an error. It is denoted by β. And this

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error depends on (1) the true value of the parameter, (2) the α level we have selected, (3) the nature of the test used (one or two-tailed) to evaluate the hypothesis, (4) the sample standard deviation, and (5) the size of the example.

Let us assume that the mean has actually moved from 1500 to 1470. Our null hypothesis is that the average purchase is 1500. This is false. The probability of not finding this out, which is nothing but assuming that the given hypothesis is correct, is (β) 95%. For a different population mean the value of β will be different. Ideally, a zero β indicates an error free test. This means that ideally 1- β must be equal to 1. The closer this value is to 1, the better is the test. 1- β is considered as the power if a hypothesis test for it is the probability of rejecting a false null hypothesis.

Accept H0 Reject H0

H0 is true Correct Wrong – Type-I errorHA is true Wrong – Type-II error Correct

4.15 Selecting A Test

Three questions should be raised when choosing between various tests.

How many samples does the test involve? One, two or K? If moor than one sample is involved, are they related or not? What is the type of data? Nominal, ordinal, interval, or ratio?

Questions like the size of the sample, the quality of the sample size and weighted data can be raised. These questions will be answered in advanced statistics books and researchers should make use of them when required.

Two samples are often used when there are two different products. Two samples, one for each product, are taken and tested to find out whether they belong to the same population.

Table 4.1 lists the various statistical techniques appropriate for different measurement levels and test situations. ANOVA is discussed in the text, but in a separate chapter. Only the most commonly used tests are surveyed in the following sections. Non-parametric tests except chi square tests, call for an involved discussion and so are not discussed here. Refer to advanced spastics books for studying these methods in detail.

4.2 CHI SQUARE (χ2) ANALYSIS

This is the most widely used non-parametric test, particularly for nominal data, but it can also be used for higher scales. It is used for actual values rather than percentages. It is used to find if difference between the

χ2 = ∑

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(Oi – Ei)2

Ei

k

i=1

observed distribution of data among categories and expected distribution is significant.

One sample Test

In this test, we first note the expected (hypothesized) frequencies in each of the categories. Then the values of actual frequencies are compared with the hypothesized frequencies. The value χ2 is a measure that expresses these differences in the form of a mathematical value. The larger this difference, the larger this difference, the larger is the χ2 value. The formula for χ2 is given as

Where

Oi = Observed number of cases categorized in the ith categoryEi = expected number of cases in the ith categoryK= The number of categories χ2 is unique for each degree of freedom. The degrees of freedom involved in a category are equal to K-1.

Care should be taken in using the chi square method in the following cases:

When d.f. =1,each expected frequency should be at least 5 in size. If d.f.>1, then the χ2 test should not be used if more than 20 percent of the expected

frequencies are smaller than 5, or when any expected frequency is less than 1.

Let us take an example. A survey was conducted in Delhi to measure the intent of purchasing a second car. A sample of 200 people was taken. We would like to analyze the data based on the profession of the respondents. Is the intent dependent on the profession or not?

We assume that these categories have no effect on the income. Now we proceed with the procedure recommended earlier.

Hypothesis : H0: O1 – Ei. The proportion of the population that intends to buy independent of their professional categories as given.

Alternative hypothesis is

HA:O1<> Ei

Statistical test: The responses are divided into nominal categories and so we should use Chi square analysis

Calculated value:

Using the Table 4.2 we have calculated the value chi-square to be χ2= 12.68

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Degrees of freedom are 4-1=3Critical value: From the tables we get a critical value of 7.82 for a significance of 5%.

Decision: here the calculated value is greater than the critical value and so we reject the null hypothesis conclude that the categories do have an effect on the intent to purchase a new car.

Table 4.2: The Data and Calculations for Chi-Square with Single Sample Problem

Profession Intendent to buy Oi

Number interviewed

Percent (No.

Interviewed/ 200)

Expected Frequencies (Percent x

60) Ei

(Oi-Ei)2

Self employed (like doctors, lawyers)

14 90 45 27 3.26

Front Line workers 17 40 20 12 2.08Administrative 14 40 20 12 0.33Academic 15 30 15 9 4.00Total 60 200 100 60 12.68

Two Sample Test

The basic methodology is same as in the one sample test but the formula involved is as follows:

Here the data is categorized and so is placed in a two

χ2 = ∑ ∑

Dimensional matrix. The subscript ij refers to ijth cell.

The degree of freedom are given as (r-1)(c-1).

4.3 ONE AND TWO TAILED HYPOTHESIS

There could be two types of situations, based on which hypothesis is classified as one sided or one tailed and tow sided or two tailed.

When alternate hypothesis HA is defined as only more than or less than hypothesized mean (μ) i.e. HA> μ i.e. HA > μ or HA < μ is called one tailed hypothesis. On the other side when alternate hypothesis is stated as not equal to hypothesized mean (μ) i.e. HA ≠ μ, it means HA

could be less than μ or more than μ. Hence this is called as two sided or two tailed hypothesis.

4.4 LEVEL OF SIGNIFICANCE AND CRITICAL VALUE OF Z

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i j Eij

(Oij - Eij)

Level of ignorance (α)

Critical value of ZOne tailed test (zα) Two tailed test (zα)

10% 1.28 1.645% 1.64 1.961% 2.33 2.58

4.5 ILLUSTRATIONS

Case (i) Two tailed test

Problem : Nicrome Metal works, a leading name in Packaging Industry, has designed automatic milk packing mache ‘Fill-Pack’ to fill plastic pouch with 1 litre of milk with a standard deviation of 0.01 litre. A sample of 100 pouches was examined and then the average volume / quantity of milk found was 0.98 litre. Can we say with 95% confidence that the machine is working property?

Null Hypothesis = H0 = 1 lit.

Alternate Hypothesis = HA ≠ 1 Lit

X - μTest Statistics = t/z = -----------

S / √

Data : x = 0.98 lit., μ = H0=1 lit, n = sample size = 100, standard deviation = s = 0.01 lit. 0.98 -1 - 0.02

Hence t/z = ---------------- = --------- = 20 0.01/ 100 0.01

For 95% confidence level, corresponding level of significance is 5%, and the value of z for two tailed test is 1.96. As such calculated value of z i.e. 20 is more than actual value of z i.e. 1.96. Hence null hypothesis is rejected. Conclusion – Packing machine is not working properly.

Case (ii): One tailed test

A sample of 1000 spherical roller bearing is found to have average weight of 50 grams. Sample population standard deviation is 5 gm. One bearing, randomly selected was found of 60 gm. What is the guarantee that balance bearing will be of correct weight?

Null Hypothesis = H0=μ – 50 gmAlternate Hypothesis = H1: μ< 50 gm

x - μTest statistics = t/z = ---------

S / √n

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n

Data: x=60 gm, μ = 50gm, S=5 gm, n = sample size =1000

60-50 10 10 t/z = ----------- = --------- = ----------- = 63.29

5√1000 5 / 31.62 0.158

Assume level of significance 1% hence value of z for one tailed test is 2.33. Since calculated value of Z (63.29) is much more than actual value of z (2.33) null hypothesis is rejected.

Conclusion: There is no guarantee that remaining bearings will be of correct weight of 50 gm.

Illustration – Lux Soap and changing consumer behaviour

Research Problem

1) Whether Shah Rukh Khan is the right choice as a male ambassador for Lux.To test this we will have to find out whether people associate Shah Rukh Khan’s qualities with Lux.

2) We shall also analyze whether Lux needs to target the male consumers also. We shall test by finding out whether men really have a say in the purchase decision for soaps.

RESEARCH OBJECTIVE

Primary Objective

To find whether there is an image mismatch between the image of Shah Rukh Khan and Lux

Secondary Objective

- To find out whether the new improved positioning of Lux (targeting men also) is required?

- To find out which male celebrity (if any) is the most appropriate for Lux.

RESEARCH HYPOTHESIS

H0 : There is no mismatch between the image of Lux and Shah Rukh Khan.

HA : Tehre is a mismatch between the image of Lux and Shah Rukh Khan.

FINDINGS

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Qualities Rank for Lux Rank for SRK1 Masculine 5 6 02 Feminine 1 2 13 Status 3 5 24 Sophisticated 4 3 15 Cool / Hep 5 4 16 Glamorous 2 1 1

Summary of Findings

Correlation1 Glamour There is not a significant Difference2 Feminist There is a significant Difference3 Mascutinity There is a significant Difference4 Status There is a significant Difference5 Sophistication There is not a significant Difference6 Cool / Hap There is a significant Difference

ANALYSIS

Market Share of Various Soaps:

Out fo the total 21 men interviewed 5 of them use Lux, 3 use Cinthol and 3 use dove. 9 of them use soaps other than those mentioned here.

From among the 73 women interviewd, 18 of them use Lux i.e. around 25% of them use Lux, 13% use dove and pears and around 17% use Cinthol. A very small share goes to Dettol, i.e. around 2.7% whereas around 25% of the female respondents prefer to use other soaps like Chandrika and other medicated soaps.

Men and Buying Decision:

From, the data collected, we have found that out fo all the men interviewed only 21% either buy the soap themselves or ask someone else to buy the brand they specify. This means that the men do not have any influence on the buying decision for any brand of soap. Thus the strategy of Lux of trying to capture the male segment of the society by targeting them would not work as men do not influence the buying decision.

Reason for buying a Soap:

From what we have collected, we find that just 4% of the respondents buy soap because a celebrity endorses it. Majority of the respondents buy it for medical reasons (31%) or because of its attractive packaging, shape or scent (24%). Not even one of these respondents claims to be buying their brand of soap due to influence by friends or peers. A good 14% of the respondents buy their brand of soap because they think their brand is value for money. Thus we can infer that the buying decision for a particular brand of soap largely depends upon

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medical reasons. Thus Lux would be better off trying to capture more market share by using that strategy.

Lux and Male Celebrity?

When the respondents were asked about their opinion on Lux using a male celebrity to endorse it, only 23% said that they liked the idea, 20% said that it does not matter to them whether Lux uses a male or a female celebrity, whereas 57% of them did not like the idea. This shows that the Ad has not been absolutely accepted by the general public. The Ad might have created a stir in the market, but that according to us will not attract many customers and may be not improve the market share for the soap.

Preferred male Star in Lux Ad:

I fhte company plans to continue with a male celebrity in its ads, it should take Saif Ali Khan as majority of the respondents (31%) though he is more of a metro sexual man as compared to SRK. He was followed by Shahid Kapoor with 19% votes. This supports our research as it shows that the public have not been able to connect to the Lux-Shah Rukh Khan partnership and the company would been better off if they would have chosen either Saif Ali Khan or Shahid Kapoor for the same. Both Saif and Shahid are on the up in their careers and are thus very much in the news. The metro sexuality quotient is very high in both of them and thus very much liked by youth of today.

LUX AD FEATURING SHAH RUKH KHAN:

Nearly 1 out of every 2 people asked did not like the Lux Advertisement featuring Shah Rukh Khan. Also, the number of people who liked the Advertisement is a 19%. Most of the people were of the opinion that the advertisement was not only unaesthetic but also that it could have been shot in a better manner as it was the first time that Lux was experimenting with a male celebrity. The advertisement has created a stir in the minds of the consumers, but has not necessarily helped the company in increasing its market share. The advertisement is in the news, but not for the reasons the company must have wanted it to be in. this has lead to confusion in the minds of the consumers.

TESTING OF HYPOTHESIS

To test our stated hypothesis, we wanted to see if there was a correlation between the qualities associated with Lux and those associated with Shah Rukh Khan. There is significant difference in the rankings given to the qualities for each Shah Rukh Khan and Lux.

1. There is no significant Difference in the Glamour quotient of Shah Rukh Khan and Lux.

2. There is a significance Difference in the Feminine quotient of Shah Rukh Khan and Lux.

3. There is no significance Difference in the Sopyhistication quotenti of Shah Rukh Khan and Lux.

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4. There is a significance Difference in the Masculinity quotient of Shah Rukh Khan and Lux.

5. There is a significance Difference in the Status quotient of Shah Rukh Khan and Lux.

Thus, we find that there is a significant difference in the quotients of 4 of the 6 qualities used to describe Shah Rukh Khan and Lux. Thus we conclude that there is an image mismatch to some extent.

Therefore, we reject the null hypothesis (that there is no mismatch between the image of Lux and Shah Rukh Khan), thus concluding that,

There is an image mismatch between Lux and Shah Rukh Khan.

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CHAPTER 5SAMPLING

5.0 INTRODUCTION

An important step in the data collection process is sampling. Sampling is the process of selecting a representative part of a population, studying it and thereby drawing conclusions about the population itself. The most commonplace examples of the sampling technique are tasting a small part of a dish to determine its taste, testing the temperature of water in a bathtub by dipping a finger, glancing through a book before buying it, etc. Sampling is a very important aspect of marketing research and due care has to be taken to arrive at the right sample to be studied. Often it is impossible or too expensive to study the entire population for the decision-maker to understand the market. In this chapter we will discuss the basic concepts of sampling, types of sample designs and calculation of sample size.

5.1 THE SAMPLING TERMINOLOGY

The terminology of sampling has evolved over the period of its existence. Knowledge of these terms is necessary for understanding sampling. Let us examine these terms through the hypothetical case of Wild goose, a marketing research firm. This firm wants to find out the types of movies the owners of VCD players in India would like to watch.

5.11 Element

An element is a unit of study, which is measured for the purposes of research. This can be an individual or an organization or even inanimate objects like soaps manufactured in a production line. In the example mentioned above, the families owning VCD players constitute the elements.

5.12 Population

The total collection of elements under investigation is known as the population. In the study conducted by Wild Goose, all the families owning VCD players form the population of the study or (a) All members who buy branded baby products (b) all teenagers who watch MTV.

5.13 Sample

The subset of the elements of the population chosen for study is called the sample or the study sample. The characteristics of a good sample are discussed later in the chapter. Wild Goose may choose a few cities for sampling and within these cities it may further select a few families. The list of the families, thus selected, forms the sample used in the study.

5.14 Sampling Units

Sampling units are non-overlapping elements from a population. A sampling unit can be an individual element or a set of elements based on the sampling process used. If Wild Goose

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uses simple random sampling, it considers a single family as a unit. If it uses cluster sampling, it views each cluster of families as its primary sampling unit, while the individual family becomes the secondary-sampling unit.

5.15 Sampling frame

The sampling frame refers to a complete enumeration/list of the population as specified by the research problem. It is a list of all the sampling units. For example, a list of all the people in the country owning VCD players constitutes a sampling frame. One should be careful in designing and selecting the sampling frame. Wild Goose may obtain its sampling frame from all the manufacturers of branded VCD players, but this frame will not be exhaustive, as it will not include the people who made their purchase from the unorganized sector. Example (a) Telephone directory of any city (b) List of Income Tax Payers.

5.16 Sampling Errors

There are two types of errors (i) imprecision inherent in using statistics to estimate parameters and, (ii) errors associated with applying a decided sampling procedure. If probability samples are used, sampling theory can estimate the degree of imprecision that may be associated with a sampling design.

5.17 Sampling Plan

A sampling plan is a formal method for specifying the sampling process of a particular study.

5.2 THE NEED FOR SAMPLING

If one measures each and every element of a population for some characteristics of interest, the study is referred to as a census. But if one selects a small subset of the population for the study and then generalizes the results to the entire population, then it referred to as sampling. Sampling is an attractive alternative to the census method as can be seen from the following discussion.

5.3 CHARACTERISTICS OF A GOOD SAMPLE

A good sample should be accurate. Accuracy is a measure of the absence of bias or the absence of systematic variance. Systematic variation is the variation of a measure in one direction due to some known factors. The following example illustrates this definition.

A good sample should be precise, that is, it should have a low standard error of its estimate.

A good sample should be able to specify the accuracy and precision associated with it.

It should enable researchers to specify the degree of confidence that can be placed in its parameter estimate.

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5.4 SAMPLING DESIGNS

Sampling Designs are of two major types: probability and non-probability methods. These two types are presented in Table 5.3 along with five important considerations. Each of these two types comprises a variety of methods of sampling. The characteristics given in the table are not accurate to describe each of the sub designs, but only give the overall characteristics of the probability and non-probability designs.

Probability samples are considered to be more costly because they need a sampling frame of the entire population. Moreover, in probability sampling, the selected sample units may be located at inconveniently distant places, thus entailing higher expenses for covering them. This type of sampling also takes more time because it is systematic. But this method is very accurate due to its known probability distribution. Since the properties of the sample are well defined and predictable, they are generally accepted. Due to their strong theoretical base, these studies are replicable and their results are generalisable.

Before we deal with complex sampling methods, we shall study basic sampling concepts.

Table 5.3 : Sampling Design Choice Considerations

Consideration Design TypeProbability Non-Probability

Cost More Costly Less Costly

Accuracy More Accurate Less AccurateTime More Time Less TimeAcceptance of results Universal acceptance Reasonable acceptanceGeneralisability of results Good Poor

Table 5.6 : Sampling Methods or Types of Sampling designs

Type of Sampling

Brief Description Advantages Disadvantages

Probability designsA. Simple random

Assign to each population member a unique number; select sample items by use of random numbers.

1. Requires minimum knowledge of population in advance.

2. Free of possible classification errors.

3. Easy to analyze data

1. Does to make use of knowledge of population that researcher may have.

2. Larger errors for same sample size

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and compute errors than in stratified sampling.

b. Systematic Use natural ordering or order population; select random starting point between1 and the nearest integer to the sampling ratio (N/n); select items at interval of nearest integer to sampling ration.

1. If population is ordered with respect to pertinent property, gives stratification effect and hence reduces variability compared to A.

2. Simplicity of drawing sample, easy to check

1. If sampling is related to periodic ordering of the population, increased variability may be introduced.

2. estimates of error likely to be high where there is stratification

C. Multistage random

Use a form of random sampling in each of the sampling stages where there are at least two stages

1. Sampling lists, identification and numbering required only for members of sampling units selected in sample.

2. If sampling units are geographically defined, cuts down field costs (i.e. travel)

1. Errors likely to be larger than in A or B for same sample size

2. Errors increase as number of sampling units selected decreases

With probability proportionate to size

Select sampling units with probability proportionate to their size

reduces variability Lack of knowledge of size of each sampling unit before selection increases variability

D. Stratified1. Proportionate

Select from every sampling unit at other than last stage, a random sample proportionate to size of sampling unit.

1. Assures representativeness with respect to property that forms basis of classifying units; therefore, yields less variability than A or C,

2. decreases chance of failing to include members of population because of classification process

3. characteristics of each stratum can be estimated and hence

1. Requires accurate information on proportion of population in each stratum; otherwise increases error.

2. If stratified lists are not available, may be costly to prepare them; possibility of faulty classification and hence increase in variability

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comparisons can be made.

2. Optimum allocation

Same as D1 except sample is proportionate to variability within strata as well as their size.

Less variability for same sample size than D1.

Requires knowledge of variability of pertinent characteristic within strata

3. Disproportionate

Same as D1 except that size of sample is not proportionate to size of sampling unit but is indicated by analytical considerations or convenience

More efficient than D1 for comparison of strata or where different errors are optimum for different strata.

Less efficient than D1 for determining population characteristics i.e. more variability for same sample size.

E. Cluster Select sampling units by some form of random sampling; ultimate units are groups; select these at random and take a complete count of each.

1. If clusters are geographically defined, yields lowest field costs.

2. Requires only listing of individuals in selected clusters.

3. Characteristics of clusters as well as those of population can be estimated.

4. Can be used for subsequent samples, since clusters, not individuals, are selected and substitution of individuals may be permissible

1. Larger errors for comparable size than other probability samples.

2. Requires ability to assign each member of population uniquely to a cluster, inability to do so may result in duplication or omission of individuals

F. Stratified cluster

Select clusters at random for every sampling unit.

Reduces variability of plain cluster sampling

1. Disadvantages of stratified sampling added to those of cluster sampling.

2. Since cluster properties may change, advantage of stratification may be reduced and

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make sample unusable for later research.

G. Repetitive: multiple or sequential (doubt)

Two or more samples of any of the above types are taken, using results from earlier samples to design later ones or determine if they are necessary.

Provides estimates of population characteristics that facilitate efficient planning of succeeding sample; therefore, reduces error or final estimate.

1. Complicates administration of field work.

2. More computation and analysis required than in non-repetitive sampling.

3. Sequential sampling can be used only where a very small sample can approximate representativeness and where the number of observations can be increased conveniently at any stage of the research.

Non-probability DesignsJudgment

Select a subgroup of the population that, on the basis of available information, can be judged to be representative of the total population; take a complete count or sub sample of this group.

Reduces cost of preparing sample and fieldwork, since ultimate units can be selected so that they are close together.

1. Variability and bias of estimates cannot be measured or controlled.

2. Requires strong assumptions of considerable knowledge of population and subgroup selected.

Quota Classify population by per income properties; determine desired proportion of sample from each class; fix quotas for each observer.

1. Same cost considerations as Judgment (Advantage)

2. Introduces some stratification effect.

Introduces bias of observers’ classification of subjects and non random selection within classes

1. Convenience Select units of analysis in any

Quick and inexpensive Contains unknown amounts of both

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convenient manner specified by the researcher

systematic and variable

2. Snowball Select units with rate characteristics; additional units are referred by initial respondents.

Only highly specific application.

Representativeness of rare characteristic may not be apparent in sample selected.

5.7 ANOTHER APPROACH TO SAMPLE SIZE CALCULATION

(Z S)Sample size = n = e wheren = sample size, Z = standard normal distribution for certain confidence level, S = population standard deviation and e = Tolerable error in estimating the variable.

The value of S is calculated as follows Maximum Value – Minimum Value

S = population standard deviation = 6

The denominator is 6 because 99.7% of the values of the variables would lie within ± 3 x standard deviation i.e. 3 σ

Illustration

Whirlpool conducted Customer Satisfaction Survey during December 2005 for Washing Machines. It intended to measure customer satisfaction on a scale of 1 to 10, where 1 means not at all satisfied and 10 means completely satisfied. Assume level of significance 5% and tolerable error 0.5.

Solution :First we compute S

Max Value of Cust Satisfaction – Minimum Value of Cust SatisfactionHere S = 6

= 10-1 6

= 1.5Value of Z for 5% significance level is 1.95

Hence Sample size = n = (ZS) 2 = (1.95 x 1.5) 2 = 35 E 0.5

5.8 SAMPLING TECHNIQUES

Probability Sampling Non-probability Sampling

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1) Each sampl4e unit in sample frame has equal or know chance of being included as sample

1) The chance of each sample unit from sample frame being included as sample cannot be estimated.

2) Samples are selected at random from sample frame.

2) Samples are selected w.r.t. prior Experience or judgement of the researcher

3) Whenever large sample size is involved, this method is used.

3) For accessing small sample size this method is used.

4) When highly accurate decisions of known errors are intended regardless of cost, this method is useful.

4) Whenever time and cost constraints are inevitable (like exploratory Research), this method is used.

5) Normally used for consumer goods survey. 5) Normally used for industrial goods survey.5.9 ILLUSTRATION

Emami wants to launch ‘Madhuri’ and ‘Ishwarya’ range beauty creams, say in Pune. How should it do sample design.

Solution:

Sample Population : All women in Pune.Sample Frame : All women between age group 10-50Sampling Method : Stratified.Sampling Plan “ Sample frame is divided into 4 groups as follows :

Group 1 – School-going girls between 10-16Group 2 – College –going girls between 17-23Group 3 – Working ladies between 24 – 35Group 4 – Housewives and working ladies between 36-50.

Samples can be drawn from schools, colleges, offices, societies, etc.

Justification : Beauty creams are costly and hence stratified sampling will ensure the income i.e. affordability. It is seen that at higher secondary school level, the girls are more cautious about looks. Hence, the age limit begins with 10. At the age 50, the ladies might value natural beauty. Four groups are formed to understand in depth the consumer profile and its preferences.

Sample size : 1% from each group. (Sample frame for Pune contains 8 lacs ladies)

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CHAPTER 6MEASUREMENT IN RESEARCH

6.0 INTRODUCTION

The task of measurement starts when the research problem is specified and a particular type of design is chosen. The importance of measurement should be understood by both the client and the researcher alike because it is the means by which reality is presented. A wrong measurement system or an incomprehensible measurement will make the research inadequate and sometimes invalid. In short, the measurement should be well understood by the people associated with the research process, and the measurement system followed should be accurate and uniform. In this chapter we will discuss the concept of measurement and the components of measurement.

6.1 THE CONCEPT OF MEASUREMENT

Measurement can be defined as the assignment of numbers to the characteristics of objects, persons, states, or events according to certain rules. An object may be measured by its shape, length height, width or some other characteristics. Similarly, a study to determine whether children or adults consume a particular product more often measures the child-adult and purchaser-non-purchaser attributes of the persons sampled. The symbols used (referred to as numbers in the definition of measurement) depend on the nature of the characteristics they are to represent and how they are to represent them.

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CHAPTER 7SCALING TECHNIQUES FOR ATTITUDE MEASUREMENT

7.0 INTRODUCTION

'Attitude' has been defined differently by many other authors. Some of the definitions are given below:

"Attitudes are individual mental processes which determine both the actual and potential responses of each person in a social world. Since an attitude is always' directed towards some object, it may be defined as "the state of mind of the individual towards a value.'"

7.3 CLASSIFICATION OF SALES

The number assigning procedures or the scaling procedures may be broadly classified on one or more of the following bases: (i) subject orientation (ii) response form; (iii) degree of subjectivity; (iv) scale properties; (v), number of dimensions and (vi) scale construction techniques. We take up each of these separately.

(i) Subject orientation: Under it a scale may be designed to measure characteristics of the respondent who completes it or to judge the stimulus object which is presented to the respondent. In respect of the former, we presume that the stimuli presented are sufficiently homogeneous so that the between- stimuli variation is small as compared to the variation among respondents. In the later approach, we ask the respondent to judge some specific object in terms, of one or more dimensions and we presume mat the between-respondent variation will be small as compared to the variation among the different stimuli presented to respondents for judging.

(ii) Response form: Under this we may classify the scales as categorical and comparative. Categorical scales are also known as rating scales. These scales are used when a respondent scores some object without direct reference to other objects. Under comparative scales, which are also known as ranking scales, the respondent is asked to compare two or more objects, In this sense the respondent may state that one object is superior to the other or that three models of pen rank in order 1, 2 and 3. The essence of ranking is, in fact, a relative comparison of a certain property of two or more objects.

(iii) Degree of subjectivity: With this basis the scale data may be based on whether we measure subjective personal preferences or simply make non-preference judgements. In the former case, the respondent is asked to choose which person he favours or which solution he would like to see employed, whereas in the latter case he is simply asked to judge which person is more effective in some aspect or which solution will take fewer resources without reflecting any personal preference.

(iv) Scale properties: Considering scale properties, one may classify the scales as nominal, ordinal, interval and ratio scales. Nominal scales merely classify without indicating order, distance or unique origin. Ordinal scales indicate magnitude relationships of 'more than' or 'less than', but indicate no distance or unique origin.

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Interval scales have both order and distance values, but no unique origin. Ratio scales possess all these features.

(v) Number of dimensions: In respect of this basis, scales can be classified as 'unidimensional' and 'multidimensional' scales. Under the former we measure only one attribute of the respondent or object, whereas multidimension al scaling recognizes that an object might be described better by using the concept of an attribute space of 'n' dimensions, rather than a single-dimension continuum.

(vi) Scale construction techniques: Following are the five main techniques by which scales can be developed.

(a) Arbitrary approach: It is an approach where scale is developed on ad hoc basis. This is the most widely used approach. It is presumed that such scales measure the concepts for which they have been designed, although there is little evidence to support such an assumption.

(b) Consensus approach: Here a panel of judges evaluate the items chosen for inclusion in the instrument in terms of whether they are relevant to the topic area and unambiguous in implication. For example Thurstone scale.

(c) Item analysis approach: Under it a number of individual items are developed into a test which is given to a group of respondents. After administering the test, the total scores are calculated for every one. Individual items are then analyzed to determine which items discriminate between persons or objects with high total scores and those with low scores. For example Likert Scale.

(d) Cumulative scales are chosen on the basis of their conforming to some ranking of items with ascending and descending discriminating power. For instance, in such a scale the endorsement of an item representing an extreme position should also result in the endorsement of all items indicating a less extreme position.

(e) Factor scales may be constructed on the basis of intercorrelations of items which indicate that a common factor accounts for the relationship between items. This relationship is typically measured through factor analysis method. For example Semantic Differential scale.

7.4 SCALING TECHNIQUES.

We now take up some of the important scaling techniques often used in the context of research especially in context of social or business research.

7.41 Rating scales: The rating scale involves qualitative description of a limited number of aspects of a thing or of traits of a person. When we use rating scales (or categorical scales), we judge an object in absolute terms against some specified criteria i.e., we judge properties of objects without reference to other similar objects. These ratings may be in such forms as "like-dislike", "above average, average, below average", or other classifications with more categories such as "like very much-like some what-neutral-dislike somewhat-dislike very

84

much"; "excellent-good-average-below average-poor", "always-often-occasionally-rarely-never", and so on. There is no specific rule whether to use a two-points scale, three-points scale or scale with still more points. In practice, three to seven points scales are generally used for the simple reason that more points on a scale provide an opportunity for greater sensitivity of measurement.

Rating scale may be either a graphic rating scale or an itemized rating scale.

7.411 The graphic rating scale is quite simple and is commonly used in practice. Under it the various points are usually put along the line to form a continuum and the rater indicates his rating by simply making a mark (such as ) at the appropriate point on a line' that runs from one extreme to the other. Scale-points with brief descriptions may be indicated along the line, their function being to assist the rater in performing his job. The following is an example of five-point graphic rating scale when we wish to ascertain people's liking or disliking any product:

This type of scale has several limitations. The respondents may check at almost any position along the line which fact may increase the difficulty of analysis. The meanings of the terms like "very much" and "some what" may depend upon respondent's frame of reference so much so that the statement might be challenged in terms of its equivalency. Several other rating scale variants (e.g., boxes replacing line) may also be used.

7.412 The itemized rating scale (also known as numerical scale) presents a series of statements from which a respondent selects one as best reflecting his evaluation. These statements are ordered progressively in terms of more or less of some property. An example of itemized scale can be given to illustrate it.

Suppose we wish to enquire as to how well does a worker get along with his fellow workers? In such a situation we may ask the respondent to select one, to express his opinion, from the following:

He is almost always involved in some friction with a fellow worker. He is often at odds with one or more of his fellow workers. He sometimes gets involved in friction. He infrequently becomes involved in friction with others. He almost never gets involved in friction with fellow workers.

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How do you like the product? (please check)

Like Very much

Like some what

Neutral Dislike some what

Dislike very much

The chief merit of this type of scale is that it provides more information and meaning to the rater, and thereby increases reliability. This form is relatively difficult to develop and the statements may not say exactly what the respondent would like to express.

Rating scales have certain good points. The results obtained from their use compare favourably with alternative methods. They require less time, are interesting to use and have a wide range of applications. Besides, they may also be used with a large number of properties or variables. But their value for measurement purposes depends upon the assumption that the respondents can and do make good judgements. If the respondents are not very careful while rating, errors may occur. Three types of errors are common viz., the error of leniency, the error of central tendency and the error of hallo effect. The error of leniency occurs when certain respondents are either easy raters or hard raters. When raters are reluctant to give extreme judgements, the result is the error of central tendency. The error of hallo effect or the systematic bias occurs when the rater carries over a generalized impression of the subject from one rating to another. This sort of error takes place when we conclude for example, that a particular report is good because we like its form or that someone is intelligent because he agrees with us or has a pleasing personality. In other words, hallo effect is likely to appear when the rater is asked to rate many factors, on a number of which he has no evidence for judgement.

7.42 Ranking scales: Under ranking scales (or comparative scales) we make relative judgements against other similar objects. The respondents under this method directly compare two or more objects and make choices among them. There are two generally used approaches of ranking scales viz.

(a) Method of paired comparisons: Under it the respondent can express his attitude by making a choice between two objects, say between a new flavour of soft drink and an established brand of drink. But when there are more than two stimuli to judge, the number of judgements required in a paired comparison is given by the formula:

n(n-1)N = ------------

2

where N = number of judgements n = number of stimuli or objects to be judged.

For instance, if there are ten suggestions for bargaining proposals available to a workers union, there are 45 paired comparisons that can be made with them. When N happens to be a big figure, there is the risk of respondents giving ill considered answers or they may even refuse to answer. We can reduce the number of comparisons per respondent either by presenting to each one of them only a sample of stimuli or by choosing a few objects which cover the range of attractiveness at about equal intervals and then comparing all other stimuli to these few standard objects. Thus, paired-comparison data may be treated in several ways. If there is substantial consistency, we will find that if X is preferred to Y, and Y to Z, then X

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will consistently be preferred to Z. If this is true, we may take the total number of preferences among the comparisons as the score for that stimulus.

It should be remembered that paired comparison provides ordinal data, but the same may be converted into an interval scale by the method of the Law of Comparative Judgement. This technique involves the conversion of frequencies of preferences into a table of proportions which are then transformed into matrix by referring to the table of area under the normal curve. The method is known as the Composite Standard Method and can be illustrated as under:

Suppose there are four proposals which some union bargaining committee is considering. The committee wants to know how the union membership ranks these proposals. For this purpose a sample of 100 members might express the views as shown in the following table:

Table 7.42: Response Patterns of 10o samples Paired Comparisons 4 Brands

SuggestionA B C D

A = close up - 65* 32 20*B = Pepsodent 40 - 38 42C = Aquafresh 45 50 - 70D = Colgate Total 80 20 98 -TOTAL 165 135 168 132

* Read as 20 samples preferred Brand D to Brand A.

Rank Order 2 3 1 4Mc 0.5375 0.4625 0.5450 0.4550Zj 0.09 (-)0.9 0.11 (-).11Rj 0.20 0.02 0.22 0.00

Comparing me total number or preferences for each or the four brands, we find that C is the most popular, followed by A, B and D respectively in popularity. The rank order shown in the above table explains all this.

By following the composite standard method, we can develop an interval scale from the paired- comparison ordinal data given in the above table for which purpose we have to adopt the following steps in order:

(i) Using the data in the above table, we work out the column mean with the help of the formula given below:

C + .5 (N) 165 + .5 (100)Mc = --------------- = --------------------- = 5.375

nN 4(100)

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where Mc = the mean proportion of the columns C= the total number of choices for a given suggestion n = number of stimuli (proposals in the given problem)/brands N = number of items in the sample.

The column means have been shown in the Mc row in the above table.

(ii) The Z values for the Mc are secured from the table giving the area under the normal curve. When the Mc value is less than .5, the Z value is negative and for all Mc values higher than .5, the Z values are positive. These Z values are shown in Z j

row in the above table. (iii) As the Zj values represent an interval scale, zero is an arbitrary value. Hence we

can eliminate negative scale values by giving the value of zero to the lowest scale value (this being (-).11 in our example which we shall take equal to zero) and then adding the absolute value of this lowest scale value to all other scale items. This scale has been shown in Rj row in the above table. Graphically we can show this interval scale that we have derived from the paired-comparison data using the composite standard method as follows:

Fig. 7.42

(b) Method of rank order: Under this method of comparative scaling, the respondents are asked to rank their choices. This method is easier and faster than the method of paired comparisons stated above. For example, with 10 items it takes 45 pair comparisons to complete the task, whereas the method of rank order simply requires ranking of 10 items only. The problem of transitivity (such as A prefers to B, B to C, but C prefers to A) is also not there in case we adopt method of rank order. Moreover, a complete ranking at times is not needed in which case the respondents may be asked to rank only their first, say, four choices while the number of overall items involved may be more than four, say, it may be 15 or 20 or more. To secure a simple ranking of all items involved we simply total rank values received by each item. There are methods through which we can as well develop an interval scale of these data. But then there are limitations of this method. The first one is that data obtained through this method are ordinal data and hence rank ordering is an ordinal scale with all its limitations. Then there may be the problem of respondents becoming careless in assigning ranks particularly when there are many (usually more than 10) items.

7.43 Direct Response Attitude Scales

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0.0 0.1 0.2 0.3 0.4

Attitude scales help a researcher to directly measure an individual's attitude or a component of the attitude. In this technique, a consumer is required to respond to the scale being used, by explicitly stating his or her attitude towards a particular brand or brands, For example, if a consumer is asked to rate the taste of Nescafe after being given a number of alternatives like, very good, good, neither good nor bad, bad and very bad, then it is said that a consumer is given a scale to measure the attribute. These responses are then evaluated as direct reflections of attitudes or a few components of attitudes. These scaled responses are termed as 'direct response attitude scales'.

Direct response attitude scale is divided into two categories based on the number of attitude components measured. The first category is called 'rating scales' where a single dimension of components of attitudes is considered. For example, pleasantness of a taste. The second category is called attitude scales, where several or all aspects of an individual's attitude towards a product (object) are measured. So, we can infer that attitude scale is a combination of rating scales.

7.43(a)Rating Scales

In this method, the respondent places the attitude of the product (object) that is being rated at some point along a numerical series.

The emphasis of the rating scales is on a) An overall attitude towards an object, such as Diet Coke. b) The existence level of a particular attribute in an object, like sweetness. c) An individual's feelings towards an attribute of an object, such as liking the taste, or d) The importance associated .with an attribute, as the absence of caffeine.

As a brand's reputation may influence an individual's preference towards a particular product attribute, such as taste, color, etc. the measurement of such products is usually carried out by removing the brand name. These tests are thus termed as blind tests.

7.7 ILLUSTRATIONS

(1) Construct following scales of attitude measurement (i) Nominal (ii) Ordinal (iii) Interval (iv) Ratio (v) Thurstone

(i) Nominal Scale : Symbols or numbers are assigned to brand names, geographic territory, sex, user status, etc.

Illustration : (a) Nominal scale to identify potential of cellular phone (WLL) w.r.t. territory

Following data is provided on WLL Mobile Telephone Users – city wise

City Mobile Telephone Users

Symbol City Mobile Users

Symbol

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1. Pune 80000 W 7. Puri 50000 E2. Mumbai 300000 W 8. Hyderabad 180000 S3. Nasik 50000 W 9. Bangalore 270000 S4. Delhi 250000 N 10. Chennai 275000 S5. Lucknow 60000 N 11. Cochin 40000 S6. Calcutta 200000 E 12. Punjim 50000 W

We can put the respective city in respective region like west, east, south and north and put the first word of region against each city name. We now add potential users under W,E,S & N and represent as follows:

North Territory West Territory South Territory East Territory310000 480000 725000 250000

Conclusion :- Attitude formed is, south territory has highest potential.

Illustration (b) Godrej Agrovet have provided following data for its ‘cattle fee’ product Brand ‘Milk More’. Construct Nominal scale.

District Region Sales per day in

Qtl.

District Region Sales per day in

Qtl.1. Nagpur Vidharbha 300 7. Solapur South

Maharashtra260

2. Akola Do 200 8. Baramati

Do 340

3. Wardha Do 400 9. Sangli Do 2804. Aurangabad Marathwada 150 10. Pune Wet Maharashtra 5005. Jalna Do 105 11. Nasik Do 3806. Parbhani Do 125 12. Satara Do 400

Let us regroup regionwise sales and rank them

Sr. No. Regiona Total sale in Qtls. Per day

Rank

1 Vidharbha 950 II2 Marathwada 380 IV3 South Maharashtra 880 III4 West Maharashtra 1330 I

Conclusion : Attitude formed is Western Maharashtra Region is having highest sales potential.

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(ii) ordinal Scale : In marketing research, ordinal scales are used to ascertain consumer’s perception on a brand, service, etc. Illustration a) Mobile user’s brand preference for handset manufacturers

Attributes Light

weight

Price Design / style

High technolo

gy

Battery life

Durability

Reliability

Voice qualit

y

Caring compa

ny

Total

score in %

Nokia 99%Sony Ericson

99%

Motorola 97%LG 93%Samsung 82%Panasonic

80%

Philips 68%Siemens 66%Mitsubhishi

30%

Alcatel 13%TCL 5%

Illustration (b): Microwave ovens manufactures wants to know the brand ranking perceived by customers. Design ordinal scale.

Attributes Price(10) Weight (10)

Antibacterial properties

(10)

Nutritive food (10)

After sales service (10)

Total service

out of 50

Rank

LG 9 9 10 10 10 48 1BPL 8 8 8 8 8 40 4Kenstar 10 10 7 8 10 45 3Samsung 8 8 10 10 10 46 2Electrolux 6 9 9 8 6 38 5Panasonic 6 8 8 8 6 36 6National 7 7 7 7 7 35 7Whirlpool 7 7 7 7 7 35 7Bajaj 8 7 10 10 10 45 3

(iii) Interval scale: In marketing research, this scale is used to measure intensity by which attitude towards a brand varies o any marketing stimuli.

Illustration: (a) Mobile telephone users may express Nokia brand in follows:Global brand Nokia cellular I liked by me the most, I neither like nor dislike Nokia cellular, I dislike Nokia cellular, I dislike Nokia cellular the most.

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Illustration (b) Consumers want to express the tastes (likes / dislikes) of Pizzas and burgers supplied by Pizza Hut, McDonalds and domino Pizza. Design Interval Scale.

Attributes Like the most

Like Neither like nor Dislike

Dislike Dislike the most

P D M P D M P D M P D M P D MTasteCheese QualityBrisknessThicknessSpice Price

Conclusion: McDonald’s Burgers is liked by most of the consumers. (Amul Pizza is not considered because only in Gujarat, it is served in ready to eat fashion whereas in other part of the country it is served in frozen condition, which requires further processing).

(iv) Ratio scale : This scale is used to measure attitude on quantity sold, number of consumers, profitability, probability of purchase, etc.

Illustration : (a) IT customers handled by the Telecom companies. – Number of IT consumers handled by Tata Indicom is one tenth of that handled by Reliance Infocom.

Illustration (b) A automobile dealer wants to get knowledge on profitability on consumer base of hero Honda and TVS Victor. Design ratio scale:

Vehicle Quantity sold in one

year

Price /each in Rs.

Total sale Rs.

Commission earned per

vertical

Total profit Rs.

Hero Honda Passion 230 46000 10580000 4000 920000TVS Victor GL 205 45000 9525000 4500 922500

Conclusions: (1) No. of consumers handled by Hero Honda Dealer are 1.12 times more than TVS victor dealer(2) Total profitability of TVS Victor dealer is 1.0027 times more than Hero Honda Dealer.(v) Thurstone scale : This is eleven point scale to express varying degree of attitude from unfavourable to favourable.

BA F K

C D E G H I JUnfavourable Neutral Favourable

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Illustration (a) suppose a statement is made like, ‘Enron Power Project is beneficial to India’, the response from the consumers, politicians and govt. could vary from 100% unfavourable to 100% favourable.

Illustration (b) Design Thurstone scale for ‘Saas-Bahu’ TV serials being run on most of the prime channels (SCHMHRD May 20055)

Solution : Following statements (from A to K) could be made.

(a) All these Saas-Bahu serials build up negative value system by depicting disputes in the family.

(b) All Saas-Bahu serials portray an irrational depiction of characters.(c) Telecast time of Saa-Bahu serials clashes with important programmes like News,

etc.(d) All Saas-Bahu serials are monotonous.(e) Most of the Saas-Bahu serials are complete waste of time.(f) I have no positive or negative feelings about Saas-Bahu serials,(g) Saas-Bahu serials provide good entertainment after a hard days work.(h) Most of the key characters of Saas Bahu serials become trend setters in respect to

clothings, jewelry and other accessories.(i) Most of the Saas Bahu serials bring the whole family together(j) Saas-Bahu serials help to understand, analyse and solve the domestic crisis.(k) The Saas-Bahu serials are a good ways to instill family values (obedience respect)

(2) Compare rating and ranking scales (SCHMHRD Dec. 2005)

Solutions:

Rating Scale Ranking Scale1. Attitude is measured from the point of view of intensity of the likes and dislikes

1. Attitude is measured from the point of view of intensity of preferring one product over other.

2. Interval data is needed 2. Ordinal data is needed3. It is absolute 3. It is relative4. Examples – Interval Scale 4. Example – Ordinal scale, Semantic differential

scale

(3) Construct Likert Scale, Perceptual Map and Semantic Differential Scale

(i) Likert Scale to study consumer satisfaction with tyre-brands

Score out of +2 +1 0 -1 -2Attributes Strongly

AgreeAgree Neither agree

nor DisagreeDisagree Strongly

disagreeCost friendlyGrip

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Wear & tearRubber – qualitylongevity

Brand Sign ScoreMRF 2+2+2+2+2 = 10 JK 2+1+2+1+2=8

Apollo 2+1+0+0+0=3

(ii) Semantic differential scale to understand the images in the mind of consumers for washing machine manufacturers

Remark Excellent Better Good Average Poor More poor

Worst

Score +3 +2 +1 0 -1Attributes W V LG AttributesProgressive TraditionalReliable UnreliableStrong WeakCust-focused

Non cust-focused

Responsive Non response

Brand Sign ScoreWhirlpool W +3+2+2+3+2 = 13 Videoon V +2+2+3+2+2=11

LG LG +3+2+2+2+2=11(iii) Following data is given for three Telecom companies. Prepare Semantic Differential Scale

Attributes Reliability Tangibility Responsiveness Assurance EmpathyRIM +3 +2 +1 +1 +1BHARATI +3 +2 +2 +3 +3TATA -2 -3 0 +1 +1

Solution :

+3 +2 +1 0 -1 -2 -3ReliabilityTangibility

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ResponsivenessAssuranceEmpathy

Brand Sign Score

RIM BA +3+2+1+1+2 = 8 Bharati BH +3+2+2+3+3=13

Tata TA -2-3+0+1+1=-3

(iv) Perceptional Map

Following data is given on Indian Refrigerator Industry (Size 165 lit. to 180 lit.)

Brand Technology Price :Rs.BPL Direct cool 9290Godrej Do 8000Kelvinator Do 9990Samsung Do 8490Whirlpool Do 9100LG Do 9000Electrolux Frost free 11000Videocon Direct cool 8890Allwyn Do 8290Voltas Do 8110Daewoo Frost free 10500

Prepare Perceptual map.

Solution: Construction of Perceptual Map

95 Kelvinator

High price (Rs.)

Frost freeDirect cool 9500

10500

11000

11500

12000

96

Low Price

8000

8500

9000LG

BPL

Voltas

Samsung

Alwyn

Whirlpool

Gordrej

CHAPTER 8OBSERVATION RESEARCH

8.7 HOW OBSERVATION RESEARCH IS USED?

(i) The major factor that influence the sale of branded goods is its availability. Hence marketer may depute observer to some of stores to find out how frequently the product is out of stock.

(ii) Marketer can also observe the prices charged by competitors.(iii) Marketer can use people’s meter to understand reaction of consumers to brand

features.

8.71 Methods of observation

(i) Structured – It is used when the research problem is already formulated precisely and the observers have been told specifically what is to be observed.

(ii) Unstructured – Observers are free to observe whatever they think is relevant and important

(iii) Disguised – Samples do not know that they are being observed.

(iv) Undisguised – Samples are informed about motives of observers

(v) Direct – Behaviour of sample is observed as it occurs

(vi) Indirect – Some record of past behaviour is observed. In other words, the behaviour itself is not observed rather its effects are observed.

8.72 Advantages of observation Research

(i) Since samples are observed directly, there is no chance of bias on account of predisposition of any kind (ii) The willingness of sample is not essential, as such lot and time is saved in seeking permissions (iii) Rural samples are difficult to top due to communication problem, for whom this method is not suited.

8.73 Disadvantages

(i) The action being observed of the samples may not provide any reasons for his behaviour, attitude and opinion (ii) The particular action observed might not be in actual circumstances but may be only on one particular day. (iii) The data collected dependent on skill of the observer and the manner in which he needs and interprets it. Same results might not hold with different set of observers. Hence this method is not very reliable. (iv) Very few samples can be observed due to time constraint

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CHAPTER 9SCHEDULE AND QUESTIONNAIRE

Sampling MethodsFor probability sampling technique

(i) Random Sampling(ii) Systematic Sampling(iii) Stratified Sampling(iv) Multistage Sampling(v) Area Sampling

For non-probability technique

(i) Purposive Sampling (ii) Quota Sampling

Questionnaire Design and Drafting

Following elements in balance manner make good questionnaire

(i) The questions must be relevant to subject matter and a set of questions must be able to cover the entire topic of the research (illustration of Chaitanya Health Clubs questionnaire)

(ii) The question should not indicate specific answers. (example of Amul’s Masti curd and HLL’s study on Surf Wash Boosters)

(iii) Lengthy and difficult questions would lose customer attention and hence short and easy questions to be posed.

(iv) Each and every question should create interest in the minds of samples so that samples also feel importance of question being asked and hence likely to give accurate answers seriously.

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Appropriate Layout

Types of Questions

(1) Open-ended question What do you think of the test of brand X cola?

(2) Dichotomous questions(a) Are you user of X toilet soap? Yes / No.

(sometimes one more answer is provided like “any other please specify”)(3) Multiple Choice questions

Which of the following factors made you by this brand of car:(a) Reasonable price(b) Great looks (appearance)(c) Fuel economy(d) Easy availability of service(e) Any other, please specify.

(4) Ratings or RankingsRating questions(a) Please rate the following detergent rank on

a scale of 1- 7 in their ability to clean clothes

Brand A 1 2 3 4 5 6 7Brand B 1 2 3 4 5 6 7

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Information Needed (Secondary or Primary data)

Method of data collection, PI, TI or Observation

Sampling technique and

methods

Questionnaire Layout Design

Decide on content of each questionDecide on type of questions

Decide on wording or questionsDecide sequence of questions

Decide pre-testing of questionnaire

Final testing of revised questionnaire

Brand X 1 2 3 4 5 6 7(b) Please rank for following detergents on their ability to clean the clothes

Brand A 1 2 3 4 5 6 7Brand B 1 2 3 4 5 6 7Brand X 1 2 3 4 5 6 7(1 means best, 2 means better, 3 means good, -------, 7 means worse)

(5) Indirect questions(a) Most of the people in India smoke Non-Filter Cigarettes because

----------------(b) Jo Bibi Se kare pyar wo -------- se kaise kare inkar?

Illustration: Construct a questionnaire for understanding buyer behaviour in Selection of television set for household segment

Objectives:(i) What features buyers are looking for in a TV set (ii) How important the price to the buyer(iii) What are the methods of payment (iv) The selection process of the buyer

Questionnaire:(1) a. Do you own a television? Yes / No

b. If yes, which brand / company namec. If no, go to question 7.

(2) While buying a TV what are the features you look for. (3) Given below some of the features of the TV. How important is each one to you,

please tick mark. Features Extremely

importantImportant Some what

importantNot very important

Not important at all

(i) Looks(ii) Portability(iii) Cabinet - Moulded - Wooden(iv) Size of the screen(v) No. of channels(vi) No. of speakers(vii) Auto control monitor(viii) Manufactures reputation(ix) Video adaptability(x) Integral DVD

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(xi) Foreign collaboration(xii) Guarantee offered(xiii) Servicing arrangement(xiv) Price(xv) Child lock(xvi) Games

(4) a. If a price of TV is classified as high, medium and low then where your TV model belongs to?

b. How do you judge price of a TV with respect to the features of a TV?c. Which payment option do you prefer? Cash / Installment

(5) At the time making brand choice decision, from whom among other following sources did you take the advice?Family membersFriends / neighboursDealersAdvertisementAny other source, please specify

(6) A set of statements are given below. Please indicate your opinion, to be recorded a scale ranging from strongly agree to strongly disagree

Statements Strongly Agree

Agree Can’t say Dis-agree Strongly disagree

(i) Possessing TV set is a status symbol (ii) Observing TV is passing time(iii) DDs TV programmes are dull whereas C & S’s programmes are attractive(iv) TV affects children education (v) Indian TV programmes are educative (vi) TV is best source of entertainment (vii) TV is low cost entertainment (viii) Government’s decision on expanding TV network through DTH and dish TV is appreciable (ix) TV is best gift item (x) people are confined to dairy homes due to TV viewing

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(7) Classify data (a) Age (b) Education (c) Occupation(d) Annual income of the family

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CHAPTER 10INTERVIEW

10.0 INTRODUCTION

In this chapter we will discuss how primary data is collected. Primary data can be collected either by observation or by communicating with people. Survey research is one of the two alternatives for gathering primary data. It is the process of systematic gathering of information from people for the purpose of understanding various aspects of the population being studied. It involves questioning the concerned people (a sample) and recording their responses. These responses are further analyzed to arrive at a conclusion. Except in a few instances, this method has proved to be the most efficient way of collecting data. Information regarding the attitudes and behavior of consumers can be best collected through surveys. The main issues concerning a survey research are sampling, questionnaire design, and questionnaire administration and data analysis. However, this method has some drawbacks. Through this chapter we attempt to make a comprehensive study of different types of survey research, its merits and demerits.

10.1 TYPES OF INTERVIEWS

The interview is one of the most important components of marketing research. Interviews can be classified into structured and unstructured and direct and indirect.

10.2 TYPES OF SURVEYS OR PRIMARY DATA COLLECTION METHODS

There are mainly four types of surveys: Personal, Telephone, Mail and Computer.

10.21 Personal Interviews / Mail Method / Telephone Interview Method

Methods of data collection in field research

Element of differentiation

Personal interview method

Mail method Telephone interview method

No of samples Not very high due time constraint

Large no samples can be contacted

Much more sample can be contacted as less time required

Time Is used when adequate time is available

Used when considerable time is available

Used when very short is available

Cost Highest Lowest Moderately high as compared to MM

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Accuracy Highest due to personal interaction and data recording with right understanding

Not very high due to a. Response rate not more than 20 to 25%b. Wrong interpretation of Qus. can not be sorted out.

Fairly high but depends on skill of interviewer in sorting out misinterpretation of Qus.

Use Not much useful when large geographic area is to be cover due to cost constraint

For geographically scattered samples this is best suited

For outstation samples the cost could be prohibitive hence useful local surveys only.

Infrastructure Huge infrastructure in form of project leader, research officer and investigators required

Almost negligible In terms of skilled telephone operator and data base

Type of samples Useful for ignorant and illiterate samples

Suitable for samples who can read and write

Suitable if samples can properly communicate

Questionnaire Samples loose interest with lengthy questionnaire

lengthy questionnaire is no prob. bcozSample feel it at his convenient time

Legthy questionnare wont do bcoz sample is not directly seen

Interviewer Skilled Interviewer can improve accuracy

Skilled o not skilled Interviewer r does not

affect accuracy

Skilled Interviewer can improve accuracy

Type of Qus Suitable for spontaneous Ans since samples do not like to

tax their memories

Suitable for spontaneous as well as

well thought Ans

Suitable only for Spontaneous Ans

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Drawbacks/Limitations If investigators are not trend, he himself might

fill up Questionnaire

Questionnaire might not be filled up by intended

person

It is impossible to judge person contacted is desired person as such

the 1st name of sample must be known

Internet Interviewing

Web interviewing Email survey• Applications • All type of Exam marketing surveys• CAT• Admissions

Advantages of Internet interviewing

• Fast set up, Execution and completion• Visual stimuli can be evaluated ( in case of web cap)• Stimuli presentation can be controlled allowing for pre and post questions unlike

traditional mail.• Question presentation is consistent and eliminates interviewer's bias• Questionnaire skip pattern can be controlled• Less instructive process, allowing respondents to ans as per their convenience• Accurate responses possible since it is self administered.• Eliminates cost of an interviewer• Permits real time data• Much cheaper than traditional research procss.

Sampling

• Web interviewing can be generated provided sample are accessible. Hence samples to b chosen from

a. Visitors to a websiteb. E-commerce customersc. Users of certain compuer hardware or softwared. Employees of a company that provides web access for 24 hourse. Regular web surfers at net cafes f. Wap users

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Survey design characteristics

• Screen look and feel• Question layout• Word dynamics for onscreen questionnaires’• Placement of graphics• Randomization of ans or stimuli• Richer open end responses

Applications

• Study of competitor's product where PI/MM/TI may not be possible due to cost, time Ect.

• Marketer can heir a .com company to study competitor's actionsLimitations

• No. of PC owner/internet users are limited• This type of survey can not be for masses but for classes• Sampling is complex due to problems in identifying in sample frame.

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CHAPTER 12PROCESSING AND ANALYSIS OF DATA

Data Analysis and Processing

1) Data Processing : It involves editing, coding, classification and tabulation of collected raw data so as to facilitate analysis.

2) Data Analysis: To identify the relationships in the processed data to statistical tests of significance in order to establish the validity of these relationships, either to support or reject research hypothesis.

Data Processing

1) Editing : It is a process of examining the collected raw data to detect errors and omissions and to correct these when possible. It involves a careful scrutiny of completed questionnaire. It is also done to assure that the data is accurate, consistent, complete and homogeneous

a) Field Edition – Correcting the abbreviations or illegible questionnaire required from field at researcher’s office.

b) Central Edition – Editing questionnaires which are filled at investigator’s office by the investigators.

2) Coding : It is the process of assigning numbers or other symbols to answers so as to transform the qualitative data into quantitative terms.

Example

Question Possible Answer CodesWhat is your material status? No Answer 1

Married 2Un-Married 3Widow / Widower 4Divorcee 5

12.4 CLASSIFICATION

It is the process and arranging the data according to points of similarities and dissimilarities:

Example

(1) General Education : (a) Classification as per attributes

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Less than HSC High school HSC completed

Graduate College Post Graduate

Doctorate(2) Professional Education

Social Science (MSW)* Discipline Medicine

FinanceTechnology / Engg.

Bachelor’s level* Proficiency Master’s level

Doctorate level

(b) Classification as per class-intervals

- Data relating to income, production, age, weight, etc.

Age Income

10-20 – 10 and under 20 SEC A Monthly income Rs. 20,000+20-30 – 20 and under 30 SEC B Monthly income Rs. 15000+30-40 – 30 and under 40 SEC C Monthly income Rs. 10000+

12.5 TABULATION AND TYPES AND TABLES

Definition : A statistical table is the logical listing and quantitative data in various rows and column with self explanatory title, row headings column headings and notes regarding source of data, context, etc.

Objectives : Simplifying data, avoid unnecessary data, facilitate comparison

12.51 Types of tables

12.511 One way Table – Single characteristics

Table No. 2Heading : Classwise distribution of No. of students in ISMS and IIM

108

Class No. of studentsISMS(a) PG(b) Masscom

600120720

IIM(a) MBA(b) MCA(c) MCM(d) BCA

1986090100448

Grand Total 1168Source: Registrar / ISMS / Year 2005

12.512 Two-way Table – Two characteristics

Table 3Heading: Class wise and sex wise distribution of no. of students in ISMS and IIM

SexClass

Male Female Total

PG 500 100 600MBA 190 08 198

Source: Registrar ISMS (2005)

12.513 Three way table : Three characteristics

Heading : Class wise, Faculty wise and sex wise distribution of no. of students in Indira College

Class FacultyPG Medical Computer Mass Comm.

M F T M F T M F T M F T1st year2nd year3rd year4th year

Foot note: M-Male, F-Female, T-Total Source : Registrar – ISMS / year 2005

12.6 GRAPHICAL REPRESENTATION OF STATISTICAL DATA : HISTOGRAM

109

Column Heading

Row Heading

It represents frequency of distribution in series and adjacent rectangles erected on X axis with class interval as base, hence width of rectangle is equal to class width. Area of rectangle is taken as proportional to class frequency.

Illustration : Give below data on land holding by farmers in Haveli Taluka

Size of farm in hectares

1-20 21-40 41-60 61-80 81-100 101-120

No. of farms 12 38 16 5 3 1

Illustration : Represent following data by Histogram

Age Group (in yrs.) 0-5 5-20 20-30 30-40 40-60 60-70Population 500 2100 2200 2000 1600 400

Since classes are of unequal width, convert the data by computing frequency density.

Age Group (in yrs.) 0-5 5-20 20-30Population 500/5=100 2100/15 = 140 2200/10 = 220

110

ScaleX axis : 1 unit = 20 hect.Y axis : 1unit = 5 farms

12.7 POLYGON (frequency polygon):

In this representation, mid values of frequency / data are taken on X axis and frequency o the Y axis

Monthly House Rent

100-300 300-500 500-700 700-900 900-1100 1100-1300

No. of families 6 16 24 20 10 4

Mid values will be 200 400 600 800 1000 1200 (X axis)Frequency will be 6 16 24 20 10 4 (Y axis)

12.8 BAR DIAGRAM

Width of bar indicate specific attribute in case of one-dimensional Bar diagram

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ScaleX axis : 1 unit = 5 yrs. Y axis : 1unit = 5 units of frequency density

Dividend % by Inforsys

750% 800%

1000%

1200%

0%

200%

400%

600%

800%

1000%

1200%

1400%

01-02 02-03 03-04 04-05

Years

7000

5000

3000

9700

8000

5500

3000

1600

0 2000 4000 6000 8000 10000 12000

2002-03

2003-04

2004-05

2005-06

Given below dividend declared by Infosys construct bar chartYear 05-06 04-05 03-04 02-03Dividend % 1200% 1200% 1000% 800%

Sub divided bar diagram

Multiple bars : (a) Sales Turn-over Vs Targeted Sales for LG

(b) Sales, GP, NP

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I$130bn

E$92bn

I$100bn

$80 bnE

RM50%

OH 20%

30% MARGIN

$250 bn

$200bn

$150bn

$100bn

$50bn

Cost

and

pro

fit

LG, 26%

Onida, 22%

Videcon, 20%

Samsung, 16%

Others, 16%

12.9 PIE DIAGRAM

Given below market share for CT construct pie diagram

LG 26% 26 x 3.6 93.6Onida 22% 22 x 3.6 79.2Videcon 20% 20 x 3.6 72Samsung 16% 16 x 3.6 57.6Others 16% 16 x 3.6 57.6

The angle at centre is given by –

Percentage--------- ------- x 360 = percentage

x 3.6 100

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Number of cities covered by different airlines

0

10

20

30

40

50

spicejet air deccan jet airway

Airlines

No

. of

citi

es

cov

ere

d

12.901 Statistical Maps : These are nothing but the figures like pie chart, bar charts, histograms, etc. generated by using SPSS software.

12.91 Illustrations

Graphs of frequency distributions

A frequency distribution can be presented graphically in any of the following ways:

Histogram Frequency polygon Smoothed frequency curve and Ogives or cumulative frequency curves

Histogram

Out of several methods of presenting a frequency distribution graphically, histogram is the most popular and widely used in practice. A histogram is a set of vertical bars whose areas are proportional to the frequencies represented.

While constructing histogram the variable is always taken on the X-axis and title X-axis and the frequencies depending on it on the Y-axis. Each class is then represented by a distance on the scale that is proportional to its class-interval. The distance for each rectangle on the X-axis shall remain the same in case the class-intervals are uniform throughout. If they are different they vary. The Y-axis represents the frequencies of each class which constitute the height of its rectangle. In this manner we get a series of rectangles each having a class-interval distance as its width and the frequency distance as its height. The area of the histogram represents the total frequency as distributed throughout the classes.

The histogram should be clearly distinguished from a bar diagram. The distinction lies in the fact that where a bar diagram is one dimensional. i.e. only the length of the bar is material and not the width. A histogram is two-dimensional that is in a histogram both the length as well as the width are important.

The histogram is most widely used for graphical presentation of a frequency distribution. However we cannot construct a histogram for distribution with open-end classes. Moreover a histogram can be quite misleading if the distribution has unequal class-intervals and suitable adjustments in frequencies are not made.

The technique of contracting histogram is given below (i) for distributions have equal class-intervals and (ii) for distributions having unequal class-intervals.

When class-intervals are equal, take frequency on the Y-axis, the variable on the X-axis and construct adjacent rectangles. In such a case the height of the rectangles will be proportional to the frequencies.

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HISTOGRAM

812

22

3540

6052

40

30

5

0

10

20

30

40

50

60

70

0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

No. of students

Ma

rks

When class-intervals are unequal, a correction for unequal class intervals must be made. The correction consists of finding for each class the frequency density or the relative frequency density. The frequency density is the frequency for that class divided by the width of that class. A histogram or frequency density polygon constructed from these density values would -have the same general appearance as the corresponding graphical display developed from equal class intervals.

For making the adjustment we take that class which has lowest class-interval and adjust the frequencies of other classes in the following manner. If one class-interval is twice as wide as the one having lowest class-interval we divide the height of its rectangle by two, if it is three times more we divide the height of its rectangle by three etc. i.e. the heights will be proportional to the ratio of the frequencies of the width of the class. The following illustration would clearly indicate the manner in which the adjustment has to be made:

Illustrations : Represent the following data by a histogram:

Marks No. of students Marks No. of students0-10 8 50-60 6010-20 12 60-70 5220-30 22 70-80 4030-40 35 80-90 3040-50 40 90-100 5

Solution: Since class-intervals are equal throughout no adjustment in frequencies is required.

Illustration: Represent the following data by means of a histogram (SIBM May 2005)

Weekly wages (in Rs.)

No. of workers Weekly wages (in Rs.)

No. of workers

10-15 7 30-40 1215-20 19 40-60 1220-25 27 60-80 8

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HISTOGRAM

11

28

36

49

33

20

8

0

10

20

30

40

50

60

100-110 110-120 120-130 130-140 140-150 150-160 160-170

Variable

fre

qu

en

cy

HISTOGRAM

7

19

27

1512 12

8

0

5

10

15

20

25

30

10-15 15-20 20-25 25-30 30-40 40-60 60-80

Weekly wages (in Rs.)

25-30 15

Solution: Since the class-intervals, are unequal, frequencies must be adjusted otherwise the histogram would give a misleading picture. The adjustment is done as follows. The lowest class interval is 5. The frequencies of the clas30-40 shall be divided by two since the class interval is double, that of 40-60 by 4, etc.

Construction of Histogram when only mid-points are given. When only mid-points are given, ascertain the upper and lower limits of the various class and then construct the histogram in the same manner.

Illustration: Draw the histogram for the following data : (SCMHRD May 2005)

Variable Frequency Variable Frequency100-110 11 140-150 33110-120 28 150-160 20120-130 36 160-170 8130-140 49

Frequency Polygon

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A frequency polygon is a graph of frequency distribution. It has more than four sides. It is particularly effective in comparing two or more frequency distributions. There are two ways in which a frequency polygon may be constructed:

We may draw a histogram of the given data and then join by .straight lines the mid-points of the upper horizontal side of each rectangle with the adjacent ones. The figure so formed is called frequency polygon. It is an accepted practice to close the polygon at both ends of the distribution by extending them to the base line. When this is done two hypothetical classes at each end would have to be included-each with a frequency of zero. This extension is made with the object of making the area under polygon equal to the area under the corresponding histogram. The readers are advised to follow this practice.

Another method of constructing frequency polygon is to take the mid-points of the various class-intervals and then plot the frequency corresponding to each point and to join all these points by straight lines. The figures obtained would exactly be the same as obtained by method No.1. The only difference is that here we have not to construct a histogram.

By constructing a frequency polygon the value of mode can be easily ascertained. If from the apex of the polygon a perpendicular is ' drawn on the X-axis, we get the value of mode. Moreover frequency polygons facilitate comparison of two or more frequency distributions on the same graph.

Frequency polygon has certain advantages over the histogram:

The frequency polygons of several distributions may be plotted on the same axis, thereby making certain comparisons possible, whereas histograms cannot be usually employed in the same way. To compare histograms we must have a separate graph for each distribution. Because of this limitation for purposes of making a graphic comparison of frequency distributions, frequency polygons are preferred.

The frequency polygon is simpler than its histogram counterpart. . It sketches an outline of the data pattern more clearly.

The polygon becomes increasingly smooth and curve-like as we increase the number of classes and the number of observations.

In the construction of frequency polygon the same difficulties are faced as with histograms i.e. they cannot be used for distributions having open-end classes and suitable adjustment as in case of histogram, it is necessary when there are unequal class-intervals.

(B.Com., Pune univ., 2006)

Marks No. of students Marks No. of students0-10 4 50-60 1410-20 6 60-70 820-40 14 70-90 16

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40-50 16 90-100 5

Smoothed Frequency Curve

A smoothed frequency curve can be drawn through the various points of the polygon. The curve is drawn freehand in such a manner that the area included under the curve is approximately the same as that of the polygon. The object of drawing a smoothed frequency curve is to eliminate as far as possible accidental variations that might be present in the data. While smoothing a frequency polygon the fact that it is really derived from the histogram should always be kept in mind. This would imply that the top of the curve would overtop the highest point of the polygon particularly when the magnitude of class-interval is large. The curve should look as regular as possible and sudden turns should be avoided. The extent of smoothing would however depend upon the nature of the data. If it is a natural phenomenon like tossing of coin, smoothing may be freely resorted to as such phenomenon normally has symmetrical curves. But if the phenomenon is social or economic the curve is generally skewed and as such smoothing cannot be carried, too far.

For drawing a smoothed frequency curve it is necessary to first draw the polygon and then smooth it out. As discussed earlier, the polygon can be constructed even without first constructing histogram by plotting the frequencies at the mid-points of class-intervals. This may save some time but the smoothing of the polygon cannot be done properly without a histogram. Hence it is desirable to proceed in a sequence i.e. first to draw a histogram then a polygon and lastly to smooth it to obtain the smoothed frequency curve. This curve should

118

begin and end at the base line and as a general rule it may be extended to the mid-points of the class-intervals just outside the histogram. The area under the curve should represent the total number of frequencies in the entire distribution.

The following points should be kept in mind while smoothing a frequency curve:

Only frequency distributions based on samples should be smoothed. Only continuous series should be smoothed. The total area under the curve should be equal to the area under the original

histogram or polygon.

Illustration : Represent the following frequency distribution by means of a Histogram d superimpose thereon the corresponding frequency polygon and frequency curve:

Salary (Rs.) No. of employees Salary (Rs.) No. of employees300-400 20 700-800 115400-500 30 800-900 100500-600 60 900-1000 60600-700 75 1000-1200 40

Cumulative Frequency Curves or Ogives

At times we are interested in knowing how many workers of a factory earn less than Rs. 700 per month or 'how many workers earn more than Rs. 1,000 per month' or 'percentage of students who have failed', etc. To answer these questions, it is necessary to add the frequencies. When, frequencies are added, they are called cumulative frequencies. These frequencies are then listed in a table called a cumulative frequency table. The curve obtained

119

0

20

40

60

80

100

120

140

300-400 400-500 500-600 600-700 700-800 800-900 900-1000 1000-1200

salary (Rs.)

No

. o

f E

mp

loye

es Frequency Polygon

Frequency curve

Histogram

by plotting cumulative frequencies is called a cumulative frequency curve of an Ogive (pronounced Ojive).

There are two methods of constructing Ogive, namely:

(a) The 'less than' method, and (b) the 'more than' method.

(a) 'Less than' method. In the 'less than' method we start with the upper limits of the classes and go on adding the frequencies. When these frequencies are plotted we get a rising curve.

(b) 'More than' method. In the 'more than' method we start with the lower limits of the classes and from the frequencies we subtract the frequency of each class. When these frequencies are plotted we get a declining curve.

The following frequency distribution is converted into a cumulative frequency distribution first by the 'less than' method and then by the more than method:

Marks No. of students Marks No. of students10-20 4 40-50 2020-30 6 50-60 1830-40 60-70 2

Cumulative Frequency Distributions

Marks ‘Less than’

No. of students Marks ‘More than

No. of students

20 4 10 6030 10 20 5640 20 30 5050 40 40 4060 58 50 2070 60 60 2

70 0

From the above distribution one can read at once the number of students who have obtained marks less than a particular value or more than a particular value. Thus there are 20 students who have obtained marks less than 40 and 50 students who have obtained marks more than 30.

Sometimes instead of writing 'Less than' and 'More than' we write 'or less' and 'or more'. The implication is different in the two cases, Thus marks less than 20 would exclude 20 whereas marks 20 or less' would include 20.

Similarly, marks more than 30 would exclude 30 whereas marks '30 or more' would include 30, One has to be very clear about the object in mind before these terms are used.

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Ogive by 'less than' method.

0

20

40

60

80

100

120

0 20 40 60 80 100 120

profits (Rs. lakhs)

No

. o

f C

os.

Utility of Ogives

From the stand-point of graphic presentation, the ogive is especially used for the following purposes:

To determine as well as to portray the number or proportion of cases above or below a given value,

To compare two or more frequency distributions. Generally there is less overlapping when comparing several ogives on the same grid than when comparing several simple frequency curves in this manner.

Ogives are also drawn for determining certain values graphically such a.s median, quartiles, deciles, etc,

Despite the great significance of ogives, it should be noted tht they are not as simple to interpret as one may feel and hence the reader must be careful while using them.

Illustration : Draw less than and more than ogives from the data given eblow: (BV 2005)

Profits (Rs. Lakhs) No. of Cos. Profits (Rs. Lakhs) No. of Cos.10-20 6 60-70 1620-30 8 70-80 830-40 12 80-90 540-50 18 90-100 250-60 25

Solution : Less than ogive. In order to draw less than ogive we start with the upper limit of the classes as shown below:

Profit less than (Rs. Lakhs)

20 30 40 50 60 70 80 90 100

No. of Cos. 6 14 26 44 69 85 93 98 100

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Ogive by 'More than' method.

0

20

40

60

80

100

120

0 20 40 60 80 100 120

profits (Rs. lakhs)

No

. o

f C

os.

More than ogive: in order to draw more than ogive, we start with the lower limits of the various classes:

Profit less than (Rs. Lakhs)

10 20 30 40 50 60 70 80 90

No. of Cos. 100 94 86 74 56 31 15 7 2

Illustration : Draw a percentage curve for the following distribution of marks obtained by 700 students in an examination.

Marks No. of students Marks No. of students0-9 9 50-59 102

10-19 42 60-69 7120-29 61 70-79 2330-39 140 80-89 240-49 250

Find from the graph (i) the marks at the 20th percentile, and (ii) percentile equivalent to a mark of 65.

Solution: A percentile curve is a cumulative curve drawn on a percentage basis. Hence for drawings such a curve three steps are required:

Find the cumulative frequencies of the given data by the ‘less than’ method Covert these cumulative frequencies in percentage of the total Take these percentages on the Y-axis and the variable on the X-axis and plot the

various points and join them by straight lines. The curve so drawn is known as the percentile curve.

Marks less than

Frequency Cumulative frequency Percentages

9.5 9 9 1.319.5 42 51 7.329.5 61 112 16.0

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39.5 140 252 36.049.5 250 502 71.759.5 102 604 86.369.5 71 675 96.479.5 23 698 99.789.5 3 700 100.0

It is clear from the graph that at 20th percentile marks are 31.5 and corresponding to 65 marks, the percentile is 90.

Illustration : The following table gives the wages of the workers in a certain factory:

Daily wages (Rs.) No. of workers Daily wages (Rs.) No. of workers20-25 21 60-65 3625-30 29 65-70 4530-35 19 70-75 2735-40 39 75-80 4840-45 43 80-85 2145-50 94 85-90 1250-55 73 90-95 555-60 68

Draw a histogram and a frequency curve for the data given above. Find the number of workers whose wages lie between Rs. 57 and Rs. 77.

Solution : The histogram of the above data is given.

In order to find out the number of workers getting wages between Rs. 57 and Rs. 77 we will draw a less than ogive.

Daily wages (Rs.) No. of workers Daily wages (Rs.) No. of workersLess than 25 21 Less than 65 422

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Less than 30 50 Less than 70 467Less than 35 69 Less than 75 494Less than 40 108 Less than 80 542Less than 45 151 Less than 85 563Less than 50 245 Less than 90 575Less than 55 318 Less than 95 580Less than 60 386

Limitations of Diagrams and Graphs

Although diagrams and graphs are a powerful and effective media for presenting statistical data they are not under all circumstances and for all purposes complete substitute for tabular and other forms of presentation. The well trained specialist in this field is one who recognizes not only the advantages but also the limitations of these techniques. He knows when to use and when not to use these methods and from its repertoire is able to select the most appropriate form for every purpose. Julin has beautifully said, “Graphic statistics has a role to play of its own; it is not the servant of numerical statistics, but it cannot pretend, on the other hand, to precede or displace the latter”.

The main limitations of diagrams and graphs are:

They can present only approximate values. They can approximately represent only limited amount of information. They are intended most to explain quantitative facts to the general public. From

the point of view of the statistician, they are not of much help in analyzing data. They can be easily misinterpreted and, therefore, can be used for grinding one’s

axe during advertisement, propaganda and electioneering. As such diagrams should never be accepted without a close inspection of the bonafides because things are very often not what they appear to be.

The two-dimensional diagrams and the three-dimensional diagrams cannot be accurately appraised visually and, therefore, as far as possible their use should be avoided.

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frequency curve

0

100

200

300

400

500

600

700

25 35 45 55 65 75 85

wages (Rs.)

No. of workers between 55 & 77 = (515-345)=170

5J5

345

57 77

Pictographs

Data recorded / presented on the picture is called pictographs

Illustration : Market shares of Hand-set manufacturers (March, 2006)

Subdivided Bar diagram: find the total of the components and draw simple bar diagram for the total. Each of the bars is divided into the no. of components added together is proportion. It helps to compare the components of bars at the same time.

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70% Nokia

1 2 34 5 67 8 9

* 0 *

20% LG

10% others

Cartograph: Destination for Air Deccan Flights

Ex. :Show the following information by subdivided bar diagram. (Scale 1 cm to = 10 students)

Class No. of students Students passed in EnglishA 55 44B 56 42C 54 27D 50 23

1260

20

40

60

80

100

120

Class A B C D

Class

% o

f st

ud

ents

Failed PassedScale : On Y axis

1 cm = 10%

0

100

200

300

400

500

600

A B C D E

Village

No

. o

f w

om

en

Literate women Illiterate women

Joint bar diagram: The separate bars are drawn jointly for each component in the joint bar diagram.

Ex. : Show the information given below by a joint abr diagram (Scale 1 cm = 100 women)

Village Literate women Illiterate womenA 540 230B 440 290C 550 350D 340 410E 460 190

Percentage bar diagram : For precise comparison, percentage of each component is calculated and ti is shown by sub-divided bar diagram. All bars are of equal heights.

Ex. Show the information given in Ex. 4 by a percentage bar diagram (Scale 1 cm = 10%)

Class A B C DNo. of students 55 56 54 50

127

Agriculture, 90

Irrigation, 54

Electricity, 54

Industry, 108

Communication, 36

Other, 18

Students passed in English 44 42 27 23% students passed in English 44/55 x 100=

8042/56 x 100=

7527/54 x 100=

5023/50 x 100 =

46

Pie diagram : It consists of circle. Different classes are represented by different sectors of the circle. The measures of areas of these sectors are proportional to the given data of corresponding class. Measures of arcs of sectors corresponding to each are calculated. A circle of convenient radius should be drawn. In counter clockwise direction angles of measures of arcs corresponding to each class are constructed as shown in figure.

Ex. Draw a pie-diagram representing the information as shown in the table

Head % Measure of arc of sectorAgriculture 25 25/100 x 360=900

Irrigation 15 15/100 x 360=540

Electricity 15 15/100 x 360=540

Industry 30 30/100 x 360=1080

Communication 10 10/100 x 360=360

Other 5 5/100 x 360=1800Total 3600

128

0

20

40

60

80

100

120

Class A B C D

Class

% o

f st

ud

ents

Failed PassedScale : On Y axis

1 cm = 10%

379.25

617.54

760.65

0

100

200

300

400

500

600

700

800

Dec.,03 Dec.,04 Dec.,05

(II) Graphical Representation:

Histogram : It is a special type of bar diagram. Rectangales are drawn with base equal to the class limits and heights proportional to the frequencies. These rectangles are joined to each other. (If classes are not continuous, make them so).

Ex. Draw a histogram for the following information (Scale : 1cm = 5%)

Class 15-20 20-25 25-30 30-35 35-40 40-45% 42 38 35 26 16 5

Maharashtra Seamless Limited

Sales & Income from Operations

129

0

5

10

15

20

25

30

35

40

45

15-20 20-25 25-30 30-35 35-40 40-45

Percentage

Cla

ss

Lim

its

Scale : On Y axis 1 cm = 5%

52.08 53.8

94.85

0

10

20

30

40

50

60

70

80

90

100

Dec.,03 Dec.,04 Dec.,05

17.65 18.66

32.9

0

5

10

15

20

25

30

35

Dec.,03 Dec.,04 Dec.,05

Net profit

Basic EPS

(Nine months ended)

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CHAPTER 13REPORT WRITING

13.0 INTRODUCTION

The results of the research Study need to be formally communicated in the form of research reports. The preparation and transmission of a clear, accurate, and convincing report effectively communicates the research findings. This is important because regardless of the quality of the research process and the accuracy and usefulness of the resulting data, the findings will not be utilized appropriately by the decision-makers unless they understand the research reports properly.

A report should give the client an understanding of the data and conclusions. It should also convince the client that its conclusions are correct so as to obtain the appropriate action from them. Researchers must also make their presentations technically accurate as well as understandable and useful.

13.1 ROLE OR QUALITIES OF THE REPORT

The report serves three chief functions; it organizes the data, establishes the quality of the research and motivates appropriate action.

It organizes the data, and analyzes the findings in both a logical and lucid manner thus giving them a permanent form. Due to its systematic style, it serves as an essential reference for future research on related lines.

Since many executives cannot easily ascertain the quality of a research design, questionnaire, or experiment, the report is treated as an index of the researcher's skill and performance. Thus the quality of the report often becomes a significant parameter of the quality of the research itself.

The third and the most important role is the determination of the action taken. Efficient reports lead to appropriate actions or policies. They inspire decision-makers to promptness.

Each research project is different, thus the process of communicating the results also varies. Depending on the nature of the problem, the information contained therein, and the individual preferences of those who utilize the report, the findings may be reported in any or all of these forms.

13.2 TYPES OF REPORTS

Basic report

This is composed of working papers and preliminary drafts of the project's findings. It is prepared by the researchers for their own use. The methodology and data contained in these are used by them for reference or to aid other studies.

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Report for publication

These reports are prepared for articles in trade and professional journals, popular magazines, bulletins, or monographs. Normally, these are relatively condensed reports. The writer has to determine the character and interest of the audience to be reached as well as the publisher's policies. Technical Reports

These reports are usually intended for scientific or technically trained persons. Specific descriptions of the entire procedures employed, including introduction of the problem and the hypotheses researched, details that lead to conclusions, complicated technical appendixes on the methodology and complete bibliographies are provided in the report. Reports for executives

These reports contain major conclusions and recommendations of the research project and are intended for decision-makers. Unlike technical reports, voluminous details and methodological information don't receive much focus here (they are generally put in an appendix, so that they can be referred to if the need arises). This is so because not only are managers extremely busy, they also lack the skill and interest to understand the technical und logical aspects and terminology of research.

As findings can be presented orally or in writing, the term "report" refers to either form of presentation.

Clients generally appreciate an oral presentation from the researchers in addition to the more essential written reports, as it provides an opportunity to clarify their doubts. Hence the ability to communicate well in both speech and writing is essential for the researchers' success.

13.3 PRINCIPLES OF REPORT WRITING OR STEPS IN REPORT WRITING

The fundamental form of communicating research findings is through written reports. The entire information that has been gathered during the research process now has to be passed on to the decision-makers so that they can take appropriate actions. Each report has to be tailored depending on the needs and profile of the users. If the report writer knows the tastes and thought modes of the client managers, then the presentation is likely to be more effective. Otherwise, failure to understand the nature and capacity of these individuals, their interest, or lack thereof, in the subject area, the circumstances under which they will read and evaluate the report and the uses they will make of the report may negate the very purpose of the report writing.

Executives have their own preferences. For instance, some might prefer a short report, only on results. Others may demand the research methodology and results together in the report. Some may want considerable information on the research methods used in the study. Many

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executives emphasize brevity while others demand complete discussion. Thus the audience determines the type of report. So researchers have to be aware of such wide diversity of interests and preferences and accordingly choose to present their study report.

Given below are some specific pointers as to how written reports have to be made to suit executive requirements.

Focus on the Objective of the Study

In general, the research is designed such that the information gathered facilitates a decision to be taken regarding some issue. Hence the report should be built around this decision and on the research findings that are relevant to it.

Use of non technical language

The report should be aimed at the experience level of the reader. Some researchers are under the false impression that they can convince management of their expertise and thoroughness in research by giving technical details. Technical details might be of use to some readers, but not to all. So it is better to replace technical terms with descriptive explanations.

Make it easy to follow

The material should be structured logically, the body of the report should be self-evident and the topics easy to find. For instance, explicit headings indicating every different subject and subheadings for subtopics make it easy for the busy managers to skim through the report and get the gist of it. Also certain subtle aspects such as short and focused paragraphs make the report easy to follow.

Make it clear

Clarity in writing is an essential quality because vagueness can produce wrong decisions and substantial losses. Not speed but clarity should be the aim of report writers. Simple and familiar words should be used rather than difficult and complex words. Since slang and clichés can be misinterpreted they are best avoided. The report should have a uniform style and format, thus making it easy to read. Use good sentence structure

Most skilled writers use short sentences as long, involved sentences are difficult to read. Poorly constructed sentences lead to confusion, whereas well-constructed sentences help the reader think clearly.

Avoiding errors in grammar and spelling is extremely important. This is because one incorrect sentence or misspelled word can undermine the credibility of the entire research project.

Make it interesting to read

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Research reports should be interesting to read. They should not be dull, tedious, or boring. Mere recording of facts without purpose or organization inevitably results in confusion that leads to loss of interest. Each paragraph should be written with an awareness of its position in the entire report. Make it brief and be selective

The report should be just long enough to cover the objectives of the study. It should highlight major points by stressing the big issues and taking them up first. Comprehending significant points and writing concisely and to the point brings in clarity and impresses the reader. It is important to exclude anything unnecessary. But this is entirely dependent on tile judgement of the research writer as it is difficult to decide to what extent the explanatory material has to be used. Explanation of technicalities and the logic underlying the conclusions can either confuse or clarify the reader depending upon how skillfully they are presented. One solution is to mention that certain details are omitted but are available on request.

Stress practical action

There should not be an impression that the findings of the research are true only theoretically under idealized conditions but not in reality. Using analogies, scientific examples, or comparisons drawn from experience; familiar to the reader increases the validity of the report.

Vary typography

Variations in type sizes and skillful use of white space will attract attention and facilitate reading. Use of capitals to emphasize central points, use of quotations, italics, or underlining, dots, exclamation marks, and lead lines direct the attention to significant parts of a page.

Use of visual devices in the report

Usage of graphs, maps, pictures gives reports a dynamic quality and emphasis. They should be used to supplement and not replace the written text. As a general rule, a sentence in the text of a report should not contain more than two or three numerical values as they make sentence difficult to read and understand.

13.4 THE REPORT FORMAT OR LAYOUT

A report must use the format that best fits the needs and wants of its readers. Though there is no one best format for all reports, a basic outline is provided below that gives a picture of the structure of the report.

The following items represent the conventional and logical arrangement of the steps involved in report preparation.

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1. Letter of transmittal accompanying the report 2. Title page 3. Table of contents 4. Executive summary 5. Introduction 6. Statement of objectives 7. Methodology 8. Limitations 9. Findings 10. Conclusions and recommendations 11. Appendix 12. Bibliography

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CHAPTER 14BIVARIATE MEASURES OF ASSOCIATION

14.0 INTRODUCTION

Marketing Managers are very often required to find the degree of association between variables. For example, they may need to know the association between the sales and price of a product or the effect of an advertisement on sales. Such association can be measured by the use mathematical methods. This chapter deals with only those methods that measure the association between two variables. In the next chapter we shall discuss techniques that can be used on many variables.

Bivariate methods that use ratio ad interval data will be discussed in detail. We will also examine in brief bivariate methods involving nominal and ordinal data. For each of these methods we will measure two components : one, the nature of association, and two, the strength of the association. The nature of the association will allow us to predict the dependent variable at any given level of independent variable. The strength of association measures the degree of association i.e. the variation that can occur in actual value from the predicted values.

Various assumptions are made for each technique and these are mentioned in the appropriate sections. One should understand these assumptions thoroughly to correctly interpret the results of the analysis. Otherwise one would make erroneous interpretations of the analysis. Two common errors made in the use of methods of association are : (1) association represents causation and (2) association of unrelated variables.

If two variables show association, it only means that they occur together, it does not necessarily mean that one causes the other. Let us assume that an association between sales of pens and sales of student notebooks was established. This does not mean that the sales of notebooks affect the sale of pens. Instead, these associations occur due to seasonal variations in the demand for student stationery.

Similarly, one should check if the relation is of practical value. For example, though there may be a statistically significant correlation between internal employee turnover and sales figures, the association is not really a practical one. To take a more exotic example, one may find a correlation between rainfall in Cherapunji and the sale of shoes in Syria. Such correlation is possible due to the nature of statistics.

Previous Intention to buy in future TotalsPurchase Yes No

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Yes 40 A 20 B 60 (66.7%) (33.3%) (100%)

10 C 30 C 40No (25%) (75.0%) (100%)Totals 50 50

Calculation of Phi (ψ) Coefficient

14.1 CROSS TABULATIONS

The simplest way to look for associations is to cross tabulate the data. This method is also referred to as gross break. It is usually used for nominal data though it is also applicable to interval or ratio data. This method is the lest complicated and the most useful with nominal data.

Variable Y

Variable X

Bo

Be

Ao

Ae

Bo+Ao

Do

De Co

Ce

Do+Co

Total Bo+Do

Ao+Co

N

O subscript means observed frequency and E subscript means expected frequency.Expected frequencies are calculated by the formulae

BC – AD= ------------------------------------------------

(A+C) (B+D) (B+A) (D+C)

Let us look at the following example. The table depicts two variables : previous purchase experience and intent to purchase. Both are nominal variables. The sample size is 100. The table represents the values both as absolutes and as percentages. The percentages are calculated along rows.

Usually, the independent variable is positioned in the left margin (the rows) and the dependent variable is positioned in the top margin (the columns). The extreme right column gives the number of people (in this case 60 out of 100), who have previous purchase experience. Of these, 40 people (66.67%) intend to purchase the product again while 20 people (33.33%) have no intention of buying the product in the future. If there are relatively high percentages on any diagonal it indicates strong relation. In the example we find that more people with

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previous purchase experience wish to repurchase, and those who did not purchase before do not wish to do so.. This indicates that repurchase is strongly and directly dependent on previous purchase. If the other diagonal had high frequencies it would have indicated an inverse relationship.

Variable YVariable X B B B+A

D C D+CTotal B+D A+C N

Intention to buy in futurePast Purchase Expenditure

20 40 6030 10 4050 50 100

Contingency and Phi Correlation

We often need to establish the strength of a correlation in more quantitative terms than those used above. The two coefficients ψ(Phi) and C (contingency) can indicate the strength of a relation. The calculation of these coefficients is given below. The range of values the variable can take is – 1 to 1. As it approaches 1, it shows a stronger relationship.

ψ (Phi)= -0.41

Calculation of C (contingency) Coefficient (Bo – Ao) (Ao + Co)

Ae = N(Bo + Ao) (Bo + Do)

Be = N(Do + Co) (Ao + Co)

Ce = N(Do + Co) (Bo + Do)

De = N

Intention to buy in futurePast Purchase Expenditure

2030

4030

60

30 10 40

138

20 20Total 50 50 N

Now (o-e)2

χ2 = ∑ ------------- 3

χ2

C = -------------- χ2 + N

Using the above formulae we get C = 0.38C can take values from 0 to 1, but will never approach 1. The closer the value to 1, stronger the relation.

Scatter diagrams

Interval data can be correlated in many ways. For better understanding, data should first be plotted on a graph. In Fig.14.1, three graphs are shown. Case A shows a graph of highly correlated data, Case B displays highly Where dispersed data, and Case C shows a non-linear relationship.

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35

30

25

20

15

10

5

0

0 5 10 15 20

35

30

25

20

15

10

5

00 5 10 15 20

35

30

25

20

15

10

5

0

Fig. 14.1: Scatter Diagram

Scatter diagrams serve the following purposes. They

visually give an idea of the degree of correlation (for instance, the Effect of advertisement on sales).

distinguish a non-linear correlation from a linear one (for instance, Learning curve)

Identify some extreme data points which may interfere in the analysis.

14.2 REGRESSION ANALYSIS

Regression analysis is based on the relationship between two or more variables. Through regression analysis, the researcher attempts to predict the value of an unknown variable based on past observation of that variable and others. The variables involved are both independent (known) and dependent (unknown).

In regression analysis, there will be only one dependent variable, whereas the number of independent variables can be more than one. As the number of independent variables increases, the accuracy of the predictions will also increase. A causal relationship between the variables (independent and dependent) can often be found because changes in independent variables may cause a change in the dependent variable. However, it will not be true to always assume that the independent variable has caused the dependent variable to change; the change may be due to another variable that we have not considered. Hence, it becomes important for us to consider the relationships found by regression as relationships of association. It need not be a cause and effect relationship.

The regression line is a line that best describes the nature of the data presented. It can be linear or non-linear depending on the relationship between the variables. When the relationship between two variables is linear, the resulting line is linear. To estimate the linear regression line between two variables, researchers need to use the following formulae.

The equation of any straight line is given by the equation

Y = a + bX

Where

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0 5 10 15 20

X and Y are variables and a is the Y intercept of the line and b is the slope. The lines are different from each other only due to the different values of a and b. Here Y is the dependent variable and X is independent variable a and b are calculated as follows :

a = Y – bX

∑XY - n∑XY

b = ∑X2 – NX2

Using this regression line a researcher can predict the values of the dependent variable for any given value of independent variable. To find the validity of the estimated line we can calculate a standard error from the following equation.

∑ (Y-Y2)S = -------------

n - 2

Y = Values of dependent variableY = Estimated value from estimating equation that correspond to each Y valueN = number of data elements used in fitting the regression line.

There is a shortcut formula to calculate the standard error:

Y = Values of dependent variableX = Values of independent variable

∑ Y2 – a ∑ Y – b ∑XYSe = ------------------------------

n - 2

A = Y intercept calculated using the equation given above. b = slope of the equation as given in the above equation.

The larger the standard error, the greater is the scattering or dispersion of the data. Using this error, we can calculate the interval estimate at a given confidence level for a Y value calculated for a given x. The method for finding a confidence interval is similar to the method for predicting a regular confidence interval.

14.3 CORRELATION ANALYSIS

We may often need to know the degree of linear relation between two variables. For instance, when we have two variables, advertising expenditure and sales revenue, we may want to know to what extent there exists a correlation between the two. This can be found by using the correlation coefficient. We have two measures for describing the relation – coefficient of determination and co-efficient of correlation.

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The Coefficient of Determination

This is the primary way to measure the extent or strength of the association that exists between two variables. It is also referred to as sample coefficient of determination if it is used on samples, ad it is symbolized by r2. It is given by the following formula:

∑(Y – Y 2 ) r2 = 1 = ∑(Y – Y)2

The value of r2 can vary between 0 and 1. If the value of r2 is equal to 1, it means that the regression line is a perfect estimate of the data. If the value of r2 is equal to 0, it means the regression line is a totally inaccurate estimate of the data.

Statisticians interpret the coefficient of determination as the amount of variation in Y explained by the regression line. If the value is equal to 1, the variation of Y is fully explained by the regression line. If the value is 0, the regression line is totally unable to explain the variation in Y. We have a shortcut formula for finding this coefficient.

a∑Y + b∑XY + nY 2 R2 = ∑Y2 – nY2

Where

r2 = Sample coefficient of determinationa = Y interceptb = slope of best-fitting estimating linen = number of data pointsX =values of the independent variableY = values of the dependent variableY = mean of the observed values of the dependent variable.

Coefficient of Correlation

This is the second measure for finding the strength of the correlation between two variables. It is actually the square root of the coefficient of determination and is represented by r. The values of the coefficient fall in the range of -1 to 1. Here again zero represents poorly explained correlation. 1 represents a strong inverse relation between the variables.

14.4 ERRORS POSSIBLE IN THE USE OF REGRESSION AND CORRELATION ANALYSIS

The following avoidable errors are commonly made in using these techniques :

Researchers tend to extrapolate the data beyond the range of observed data

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They tend to associate a cause and effect relationship between the Variables as explained before.

Historical data is often used to predict future trends. However, in such Situations, the data should be current. The conditions during the prediction Period should not be different from the conditions during data collection.

Researchers tend to read the values of r and r2 as percentages of correctness of the estimation. However, this is not a correct reading.

Researchers also find relations when they do not exist, as already explained.

14.5 LINEAR DISCRIMINANT ANALYSIS

Regression techniques are useful in analyzing intervally-scaled data. It is often necessary in research to establish relationship between nominal variables, i.e. will buy – will not buy, heavy user – light user, credit purchase – cash purchase, etc. In such situations, Discriminant Analysis is useful.

Discriminant analysis classifies people or objects into two or more categories using intervally Scaled predictor (independent) variables. It can also be used to generate perceptual maps. The mathematical logic is similar to regression analysis. It is too complicated to be computed manually and is computed by computer. The programs produce n-1 formulae for a problem with n categories. For example, if we have six categories like very high user, high user, medium user, low user, very low user and on-user, then for these six categories, we obtain five formulae. We use these formulae to classify the objects or persons.

Some computer programs produce two formulae, while some others produce one formula for two-category analysis. Let us take a look at an example in which one formula is produced. Before proceeding with the analysis, we need to check whether the programs are able to give the desired level of significance. We should proceed only if this condition is met.

Assume that we obtained the following formula for a problem.

Y = -0.1X1 + 0.5X2 – 0.65X3

Where

Y = Usage of Cable TVX1 = The size of the familyX2 = Presence of college going childrenX3 = Presence of retired people in the family

We have to follow the steps given below to find a critical value that is useful in classifying the consumers in future.

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1. The means of each of the independent variables are calculated separately for user and non-user.

2. The means of user groups are used to produce a value for Y users while the means of nonuser groups are used to produce a Y value for nonusers.

The critical value is the midpoint between these two Y values.

When there is new data, one substitutes the values of dependent variables in the equation to obtain the Y value. If the Y value is higher than the critical value, then the family is a user, else a nonuser.

The accuracy of the discriminant analysis is tested by the use of the classification matrix, also known as the confusion matrix. Here a sample of known users and nonusers is classified by using the above critical value. The sample can be nothing but the original sample. But using the same data to generate a value and then using the same sample to test it may bias its accuracy. So, many a researcher holds back a part of the original data from the discriminant analysis and then uses it for testing.

A confusion matrix for a purchase intent problem is shown in Table 14.1. The number of correct and incorrect classifications is indicated by the percentages in brackets.

Previous Purchase Experience

Positive Negative Total

Intend to buy in future 450(75%)

200(25%)

650

Do not intend to buy 150(25%)

600(75%)

750

Total 600 800 1400

14.6 AUTOMATIC INTERACTION DETECTOR

The best way of staying ahead of the competition is by discovering information unknown to the competitor. Today’s companies have huge databases that contain a large data. A huge amount of data from past research is also available. However, this data is not checked for all possible associations. This is so because it does not make sense to make some relations. But some of such irrational relationships have worked out all over the world. For example, a super market chain found a relationship between sales of beer and diapers. By using this knowledge they could sell more by placing these goods together on the shelves. Such unknown relationships can exist between any dependent variable and some independent variables. This can be detected by the use of a technique called Automatic Interaction

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Detector AUD), It us possible to even find a relation between a set of dependent variables and a set of independent variables. This method is made more efficient by the use of computer. Basically, the AID technique is to systematically breaks down a large group of sample units into smaller subgroups that share similarities in terms of dependent and independent variables.

The measures of associations in this chapter are useful for finding relationships between various variables. These relationships are useful for predicting the values in future. But these techniques are useful only if one knows their limitations and uses them appropriately. Unfortunately, these techniques can be manipulated or misapplied. If a researcher or a manager wants support for his decisions, it is possible to achieve this by appropriate manipulation of the data. But this does not mean that one should condemn the techniques. Instead it is the attitude of the people who use it that is at fault. One can even get wrong results by using these methods inappropriately.

APPENDIX : LIST OF STATISTICAL PACKAGES

Free Software

1. ADE-4 4. Analyse-it for Microsoft Excel2. Anderson 5. Answer Tree 2.03. Statistical 6. Applied Analytic Systems4. Archives 7. AssiStat5. ARIMA 8. AUTOBOX6. B/D 9. BMDP7. Boomer 10. Bilog 38. DE 11. Chemetr5ica9. Histograms 12. Clinstat10. EasyMA 13. CoHort11. EasyStat 14. Crossgraphs12. Empty 15. Crystal Ball13. Corners 16. Data Desk14. EpiInfo 17. DBMS/Copy

15. First 18. EaqSt16. Bayes 19. EasyStat17. G*Power 20. EcStatic18. GrafProg 21. Egret19. Insight 22. Epi Info20. Instat 23. EPIMETA21. JDB 24. EQS22. LOCFIT 25. Equivtest23. MdacAnova 26. Eviews24. MANET 27. ExecuStat25. Meta-analysis 5.3 28. Forecast Pro26. Meta-analysis calculator for : 29. Forecast X

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Murdoch, Duncan. 30. GAUSS27. PAMCOMP 31. GB-STAT28. Regress 32. GenStat29. Rweb 33. GLIM30. SABRE 34. Insight31. Scilab 35. Instat32. Serpik 36. Interleap33. Graph 37. JMP34. SISA 38. Limdep35. Statistical Software by Paul W. 39. Lispstat

Mielke Jr. 40. LISREL36. STPLAN 41. LogXact37. ViSta 42. MacAnova38. WebStat 43. Maestro39. Weighted 44. Maple40. Least 45. Matrex41. Squares 46. Mesa42. Linear 47. Minitab, Inc.43. Fits 48. MLAB44. WinSAAM 49. MODSTAT

50. Multivariance 7.32Commercially Available Software 51. NCSS

52. Nomparametic bootstrap regression1. Activstats 53. NPSTAT/NPFACT2.Amos 4.0 54.nQwery Advisor3.Analy Corp 55. Oriana

56. P-Stat57. Permustat

58. Prism 82. Statistix59. Proceed 83. Statit60. Prostat 84. Statlab61. PSI-Plot 85. Statmost62. Qualitek-2 86. Stat View63. RATS 87. StatXact64. Resampling Stats. 88. STATVEX65. 2Risk (Spreadsheet Add-in) 89. SUDAAN66. S-Plus 90. SYSTAT67. SAS 91. Testimate68. Shazam 92. TPL Tables69. Scientific Software 93. TSP70. Sigma Stat 94. UniStat

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71. SIMSTAT for Windows 95. Vitalnet72. Solas 96. Vizion73. Soritec for Windows 97. WesVarPC74. SPSS 98 WINKS (Windows KWISTAT)75. SQCpack 99. WinSTAT76. STATA Corp. 100. xlstat77. Sta Table 101. XPro78. Stat Ease Inc. 102. SPSS79. StatGraphics80. Statistica82. Statistical Calculator.

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CHAPTER 15MULTIVARIATE MEASURES OF ASSOCIATION

15.0 INTRODUCTION

We learned about measures of association between two variables in the previous chapter. In this chapter we shall discuss measures of association between more than two variables. It should be noted that the same precautions one takes with bivariate methods are necessary here also. To assist recall, let us repeat the main points. Assumptions underlying each of the methods should be fully understood and utilized to obtain accurate results. Association between two variables does not imply a causal relationship. A mathematical relationship may occur due to the very nature of randomness in sampling, but experience and judgement should be used to accept or reject a relation.

15.1 MULTIPLE REGRESSION

In the previous chapter we saw how Regression Analysis is used in marketing research. We also mentioned that more than one independent variable can be used to predict the dependent variable. This process of using more than one independent variable to predict the defined variable is called multiple regression.

Let us take a case with three variables. Suppose we want to correlate the sales data with the number of advertisements and the rise in per capita savings. The dependent variable, sales, can be represented by Y, and the independent variables, per capita savings and number of advertisements, can be represented by X1 and X2. Table 15.1 gives the data collected.

As we have seen, there is a general equation for a line. A line with n independent variables has the following general equation.

Y = a + b1X1 + b2X2 + b3X3 + ... + bnXn

Where Y = dependent variable X1, X2, ..., Xn = independent variables a = Y intercept b1, b2 ..., bn = slopes associated with X1, X2, ...,Xn respectively

For a two independent variables problem, as in the above case, the equation reduces to = a+ b1X1 + b2X2

Values of a, b1 and b2 are to be determined. Now we obtain equations known as normal equations, the values of which are readily available. For three

∑Y = na + b1∑X + b2∑X2

variables the equations are

solving these equations we find the values of a, b1 and b2

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∑X1Y = a∑X1 + b1∑X12 +b2∑X1X2

∑X2Y = a∑X2 + b1∑X1 X2 +b2∑ X12

a -12.38592177b1 1.10.7709999b2 -0.024588329

Once we find these values, we can forecast any value of dependent variable for given values of independent variables. To find the accuracy of this estimate we need to find the standard error of the estimate. This

∑(Y- Ŷ)2

Se = -------------n – k – 1

can be calculated by the following formula

WhereY = Sample values of the dependent variable

= corresponding estimated values from the regression equationn = number of data points in the samplek = number of independent variables

Regression values for the problem at 90 percent confidence

Multiple R 0.96321919R Square 0.92779121Adjusted R Square 0.891686815Standard Error 3.238170452

Year Sales in thousands

Number of advertisements

Per capita earnings thousand rupees

1994 45 120 371995 35.5 112 231996 56 143 451997 64 180 561998 54 156 391999 48 148 402000 52 157 41

Table 15.1: Data for Multiple Regression Problem

Use of Multiple Regression in Marketing Research

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Multiple Regression can be used for solving a variety of marketing problems. Some of them are listed below:

1. Forecasting where either company variables (relative price, relative advertising etc) or external variables (population growth, disposable income etc) or both are used to forecast sales, demand, or market share.

2. Outlet location decisions where the traffic, the location of competitors, the square footage available, and so on, are used to analyze the attractiveness of outlet locations for chain stores.

3. Quota determination involves using territory size, last period sales, competitor strength and related variables to determine sales quotas or objectives.

4. Marketing mix analysis by analyzing the relationship between elements in the marketing mix and market share or sales.

5. Determining the relationship between the criterion variable and one predictor variable while the effects of other predictor variables are held constant. An example is the estimate of the reliance on price as an indicator of the quality of furniture, while other factors, such as the brand of the product and the stores in which it is available are held Constant.

6. Estimating values for missing data (item non- response) in surveys. For example, one can estimate income from occupation, age, and education data.

Lisrel

LISREL is an acronym for Linear Structural Relationships, and it was first introduced by Joreskog in 1973. It is a highly complicated technique, but as it is extremely useful in explaining causality among constructs that cannot be directly measured, we shall discuss it here.

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Formal Education

On-the-Job Training

Motivation Test

Behaviour Observation

Manager’s Observation

Work Output Measurement

Training

Motivation

Performance

LISREL analyzes covariance techniques through two models. The first one is a measurement model and is used to relate the observed, recorded or measured variables to the latent variables (constructs). For example, when we are measuring an employee's perfom1aDce, a construct, we relate it with variables like - (1) managerial observations (2) recorded measures of output (3) performance appraisal by peers and (4) performance appraisal by subordinates. These variables together give the researcher an understanding 01 the employee's performance. This type of measurement model for useful in modeling constructs like feelings, performance, behavior and attitudes.

The second model is a structural equation model. This model shows the causal relationships among the latent variables and also describes unexplained causal effects and variance. It consists of a set of linear structural equations. For better understanding, it is often diagrammed by the use of path analysis and the resulting diagram is called a path diagram. Figure 15.1 depicts one such path diagram for employee performance. The circles represent latent variables, the rectangles the measurable variables while the arrows represent the causal relationships. The latent variables give rise to the measurable relationships as indicated. The arrow between 'motivation' and 'training' indicates the interrelationship between these variables.

Attribute Decision levelsColour Silver

Sky BlueMaroon

Price Rs.5,00,000Rs.4,50,000Rs.5,50,000

External style and Design AlphaBetaGammaDelta

Performance levels HighMediumLow

Mileage 8 KM/Litre10 KM/Litre12 KM/Litre14 KM/Litre

Table : 15.2 : A Table of Attributes for Conjoint Analysis

15.2 CONJOINT ANALYSIS

Whenever there are some attributes that the consumer is ready to trade off or compromise on, researchers use conjoint analysis to identify the products that meet their needs best. Conjoint

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analysis is used with non- metric (non-quantifiable) independent variables. Such analysis is often used in new product development and for positioning.

Usually, cross tabulation is used when the variables are two or three in number. But as the number of attributes grows, the problem becomes too complex to be handled by cross tabulation. For instance, if we are testing for an ideal new automobile, we can have three colors, three prices, four external designs, three performance levels and four mileage factors. The total decision levels in the model are (3X3X4X3X4) 432. It is not possible to make so many cross table comparisons. Conjoint analysis is advisable under such circumstances.

Method

The attributes most pertinent to the study are first selected by using the experience of the researcher or an exploratory study or a combination of both. The above automotive example is represented in detail in Table 15.2.

The researchers prepare cards with combinations of various attributes. These combinations are later given to the consumers for ranking. From these railings one can infer the relative importance of each attribute. This is also known as part-worth or utility scores.

It is not possible to find the customer response to each of the 432 combinations in this problem. Researchers therefore use a minimum set of configurations from which the response to each attribute can be independently assessed. This minimum set is usually generated by a computer package. The cards can be as given in Table 15.3

Once the rankings are obtained, the results are again fed into a computer software and the results are analyzed.

CAR A (Card 1) CAR B (Card2)SilverRs.5,00,000AlphaHigh10 KM/Litre

Sky blueRs.4,50,000GammaMedium8 KM/Litre

Table 15.3: Sample Cards for Conjoint Analysis in Automobiles

15.3 FACTOR ANALYSIS

The term factor analysis was first introduced by thurstone in 1931. The main applications of this technique are: (1) to reduce the number of variables to a manageable number and (2) to detect the structure in the relationships between variables (that is, to distinguish variables from variables that belong together and have overlapping measurement characteristics).

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Box 15.1Capturing Cutoffs We can elicit cutoffs with simple questions like these: What is the most you would consider paying for a car? $________ ? "Which of the following VCR brands would you never consider buying? (check all that apply) []Magnavox [ ] Sony [ ] Hitachi [ ] Sarnsung

That's all there is to it. Quantitative attributes (like memory, hard drive size, CD-ROM speed, or processor speed for PCs) can be asked as in the first question. Qualitative attributes (for PCs, type of storage drive, PC brand, processor brand, or menu of pre-loaded software) can be asked as in the second question.

Incorporating Cutoffs

We can incorporate these cutoffs into our conjoint models as penalties. Say a car is $30,000 and Jones has a cutoff of $25,000, but Smith does not. The model produces a coefficient, or utility, for the price of $25,000. It also identifies a penalty for the fact that the price is $5,000 over Jones' cutoff. The penalty impacts Jones, but not Smith. The inclusion of this information about individual's idiosyncratic tastes should improve the predictive ability of the model, and to the extent it does, it is beneficial to include.

The Bottorra Line

In recent studies we tested the value of including cutoffs in conjoint analysis. Both studies included "holdout" choice questions whose purpose was to test the accuracy of alternative predictive models. Mean absolute deviation (MAD) is a measure of how far off, on average, are a model's predictions of holdout choice shares. Small MAD is good and big MAD is bad.

The first study was a panel study among members of our Consumer Web Panel. The

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In addition to the more accurate simulators we can construct by including the cutoffs, we can also report the incidence and magnitude of these cutoffs. This extra information adds a nice table to our reports. Here's an example of part of the cutoff information from the panel study of PC attributes:

Attribute / Level Utility % with cutoffs Cutoff Penalty

233 Mhz AMD processor

-.61 21 -6.5

266 Mhz Pentium II -0.2 12 -.49300 Mhz Pentium II .63 10 -Cost : $ 1200 .33 5 -Cost $1400 .07 12 -.49Cost $1600 -40 21 -.97

This table shows that the utility of a 266 Mhz Pentium II is .02 for most consumers, but is .51 for the 12% of consumers for whom 166 is not fast enough (.51 = the .02 utility plus

It is often possible that the data collected in a study may be reducible to a lesser number of variables. Suppose we conduct research in which one question measures the current satisfaction of the consumer using a product, while another question measure the repurchase frequency. It is often possible that both these variable are highly correlated. This means that one of the variables is redundant and so can be deleted. Instead, a regression line between these two is calculated and is used in place of thee two variables. This method can be used only if the correlation is high; it can also be extended to multiple variables.

Method

Factor Analysis can be done in many ways. We shall take a look at the most frequency used approach known as principal component analysis. In this method, the given set of variables is reduced to another set of composite (combination of one or two) variables are not correlated to one another. These new variables, known as factors, account for the variance of the entire data.

Extracted Factors Percentage of the remaining variance the

factor accounts for

Cumulative frequency

I 56% 56%II 23% 79%III 16% 95%IV 4% 99%

Variable Unrotated Factors Rotated FactorsA .70 -.40 0.79 0.15B .60 -5.0 0.75 0.03C .60 -3.5 0.68 0.1D .50 .50 0.06 0.7E .60 .50 0.13 0.77F .60 .60 0.07 0.85Eigenvalue 2.18 1.59 1.69 1.94Percent of variance 36.30 23.20 0.28 0.32

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Cumulative variance 36.30 59.50 0.28 0.60

Table 15.5 : Factor matrices

The best combination of variables forms the first factor and accounts for most of the variance. The next factor is chosen such that it is the best combination that accounts for variance unexplained by the first factor. This process is repeated until all the variance is accounted for. But practically, a researcher stops after most of the variance is accounted for. Table 15.4 represents the probable data obtained while performing a principal component analysis.

Income

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B

A

D

C

-0.4

0.8

0.6

0.4

0.2

-0.2

-0.6

-0.8

0.60.40.20.8

Rotated Factor II

Rotated Factor I

Age

Results from a factor study are given in Table (adapted from Cooper and Schindler). The values in this table are correlation coefficients between corresponding factors and variables. For instance, 0.70 is the correlation coefficient r between variable A and factor I. These coefficients are called loadings because they indicate the amount of variance of the variable each factor is loaded with Eigenvalues are the sum of squares of variances of factor values. For instance, the Eigenvalue of factor I is (0.70)2+(0.60)2+(0.60)2+(0.50)2+(0.60)2+(0.60)2. When the Eigenvalues are divided by the number of variables (here 6), we get the total variance explained by the factor.

In this table we note that the loadings between factor and 2 are not mutually exclusive. We want the two factors to have as little correlation between them as possible. This means that the loadings should not overlap. Ideally we should get loadings such that each factor is loaded only in those variables in which the other variable loadings are zero. But we see that the unrotated factors have high loadings on D,E and F in both the factors, indicating a correlation between the two factors. To correct this situation there is a method known as Rotation of Axes. This can be orthogonal or oblique.

To understand orthogonal rotation, assume that we are dealing with a two dimensional space rather than a multi dimensional one. The values obtained as produced in Table are shown in. Two axes are used here. But the location of these axes is only arbitrary and one can rotate the axes without changing the real loadings. Only the relative values of the loadings change. Let us rotate to obtain better values for the factors. The rotation resulted in the factors shown in Table 15.5 in the rotated factors section. Now we see that the loadings are less correlated with each other. Factor I is highly loaded in variables A, Band C while Factor II is highly loaded in Variables D,E and F. Thus one can obtain the best factors by using this method.

15.4 CLUSTER ANALYSIS

A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, i.e. taxonomies. Cluster Analysis (first used by Tryon, 1939) was developed to cater to such a need. Whenever one needs to classify a "mountain" of information into manageable meaningful piles, cluster analysis is used. Unlike discriminant analysis, it does not have an a priori hypothesis. Discriminant 2nalysis begins with a well- defined group composed of well-defined groups with two or more distinct characteristics, and tile analysis provides a set of variables to separate them. But cluster analysis starts with an undifferentiated group of people, events and objects and reorganizes them into homogenous groups.

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Method

Once the sample is selected the following steps are gone through to arrive at clusters Variables on which objects, events, or people are to be measured should be defined. The variables can be financial status, size of the family, education, etc. The data for these variables should be collected.

Similarities among the elements, persons, events or objects should be calculated by the use of correlation, Euclidean distances, and other techniques.

Mutually exclusive groups, called clusters, should be made. To achieve this, within-cluster similarity and between-cluster differences should be mathematically maximized. Alternatively one can arrange the clusters hierarchically.

These clusters should be compared with each other and then they should be validated.

Different clustering methods result in different results. So it is necessary to have enough understanding of the data to differentiate between the real clusters and the clusters imposed on the data by the method.

Figure 15.2 depicts a cluster analysis of customers based on their age and income. Let us say that this analysis was done for a real estate product. Group B is a group with high income old people and can be targeted with a luxurious and restful real estate product. On the other hand, cluster D represents market segment that needs a low priced, functional product without any frills.

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CHAPTER 16ANALYSIS OF VARIANCE (ANOVA)

16.0 INTRODUCTION

Analysis of Variance (ANOVA) is a method for testing the equality of two or more population means. The methods for testing hypothesis have already been discussed; however, with these methods one can compare only two population means. ANOVA, on the other hand, can be used when more than two means are involved. It thus helps the researcher to test for the significance of the differences among more than two sample means. It is also useful in situations where the earnings of different companies have to be compared or the efficiency of different brands of a product have to be compared. In such cases, more than two samples are compared for analysis.

Analysis of variance can be unvariate (one-way) or multivariate. Both these methods are commonly used in the analysis of experiments. We shall discuss these in detail in this chapter.

16.1 UNIVARIATE ANOVA

Let us use a hypothetical example to explain the concept of ANOVA. “Fairever” company has come up with three different variants in the package design of its fairness cream product. It has tested these product package variants in the markets and obtained the sales figures as given in Table 16.1.

In order to use analysis of variance we have to make three assumptions.

Each of the samples is drawn from a normal population. If the sample size is large enough, i.e. when n is greater than 30, we do not need this assumption.

The variance of these populations is the same and is represented by σ2. Treatments are assigned at random to the test units. This assumption is often

overlooked by using pseudo treatments such as occupation, stage of life cycle, and urban or rural residence. These non-randomly assigned treatments greatly increase the possibility of interference by other unaccounted variables associated with the pseudo treatments. These effects will then be wrongly attributed to the pseudo treatments.

The Method

As we have already seen, in an experiment, we have different groups subjected to different treatments. The greater the effect of treatments on these groups, the greater is the variation between the groups’ means. But at the same time, the variation within the group should not change and should be equal to the population variance. ANOVA measures the change in variance between the groups as compared to the variance within the groups. The steps followed in ANOVA testing are given below:

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1. Determine one estimate of the population variance from the variance among the sample means.

2. Determine a second estimate of the population variance from the variance within the samples.

3. Compare these two estimates, if they are approximately equal in value, accept the null hypothesis.

Between the group variance = Mean Square Treatment

Sum of square among groups= MST = ----------------------------------------

Degrees of freedom

Sum of Squared deviation of group sample means (Xj), from overall sample mean (X) weighted by sample size = --------------------------------------------------------------------------- Number of Groups (G)-1

SST= ------

dF

Table 16.1 : F Distribution

∑

MST =

Where nj = Size of jth sampleξj = Mean of jth sampleξ = Overall sample mean

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n;(xj – xi)2

k-1

k

j=1

(25,25) Degrees of Freedom

(5,5) Degrees of Freedom

(2,1) Degrees of Freedom

K = number of samplesWithin the group variance = Mean Square Error

Sum of squares within groupsMSE = ---------------------------------------

Degrees of freedom

SSE= ------

dF

Sum of Squared deviation of each group sample (xij) from the mean of the observations for that group sample (Xi), number of all group sample

= ----------------------------------------------------------------------------------------------Sum of the sample sizes for all groups minus the number of groups

∑ ∑

MST = ∑

Where nj = Size of jth sampleξj = Mean of jth sampleξij = Each observation in jth groupK = number of samples

To compare these two variances we can obtain a ratio of them. This ratio is known as F statistic:

MSTF = ------

MSE

If the null hypothesis is true, then the F statistic should be one, since there is no effect due to the treatments and the between the group variance is equal to the population variance. If the null hypothesis is not true, the F statistic tends to be larger than one. The null hypothesis is rejected when F is ‘significantly’ larger than one. Table 16.2 gives the result of the calculations made for finding F in the “Fairever” problem.

Sales in thousands (j)Periods (I) Product A Product B Product C

1 28 27 232 30 29 24

160

(xij – xj)2

nj - k

j=1 i=1

j=1

3 33 30 284 34 32 27

Table 16.1: Sales of Fairever product with three different product packages

F statistic (F ratio) has a several curves of distribution. It is actually part of a whole family of distributions. Each of these distributions is identified by a pair of degrees of freedom: the degrees of freedom in the numerator of the F ratio and the degrees of freedom in the denominator of the F ratio. In figure 16.1 a few of the F distributions are illustrated.

The degree of freedom for the numerator and the denominator are as follows:

Degrees of freedom for the numerator of the F ratio = number of samples – 1

Degrees of freedom in the denominator of the F ratio

= ∑ (nj – 1) = rt – K

As can be seen in figure 16.1 , the F distribution is skewed towards the right and becomes more symmetrical as the numbers of degrees of freedom in the numerator and denominator increase.

In ‘Fairever’ example, let us assume that we require a 0.01 level of significance. The degree of freedom is calculated as follows:

Source dF Sum of Squares

Mean Squared

F ratio F probability

Between Groups

2 69.75 34.75 5.93 8.02

Within groups

9 52.75 5.86

Total 11 122.5 40.61

Table 16.2: The output of ANOVA in the Fairever problem

Numerator has K-1 = (3-1)=2 degrees of freedom

Denominator has Nj – k = 12-3=9 degrees of freedom

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k

i=1

From the F tables in the appendix, under the required degrees of freedom for 0.01 level of significance, we obtain the value of 8.02 for F.Since the F ratio we have calculated is less than his value, the null hypothesis is acceptable. Thus there is no effect on the sales due to the change in the package design of the ‘Fairever’ product.

16.2 MULTIVARIATE ANOVA

The basic approach and logic for multivariate approach are the same as for the univariate approach. Multivariate ANOVA consists of two basic techniques: ANOVA with interaction and ANOVA without interaction. In ANOVA without interaction, the effects of the independent variables are independent of each other. In ANOVA with interaction, the effect of the independent variables is dependent on each other. This means that the effect of the independent variables taken together is different from the sum of the individual effect.

In the following section we discuss three experimental designs. Randomized Block Designs and Latin Square methods do not consider interaction effects while the Factorial Design takes the interaction effect into account.

16.3 ANOVA FOR RANDOMIZED BLOCKS DESIGNS (RBD)

Let us take the experiment described in the chapter “Experimentation” for understanding randomized block design. To determine the effect of repackaging on ‘Nimba’ toothpaste sales, we conducted an experiment in which we used the class of city as the blocking factor.

Now assume that the effect of blocking factors and treatment variables do not interact with each other. In RBD, we have to calculate between the block variance along with between the treatment variance. Moreover the effects of blocks are taken into consideration for calculating the mean Square Error.

The required formulae are as follows:

Between the treatment variance

Mean Square Treatment (MST)

Sum of Squares Treatments (SST)= -----------------------------------------------

dF

∑nj (xj – xt)2

= ----------------- c-1

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c

j=1

Between the block variance

Mean Square Blocking (MSB)

Sum of Errors (SSE)= -----------------------------

dF

∑ni (xi – xt)2

= ----------------- r-1

Mean Square Error (MSE)

Sum of Errors (SSE)= -----------------------------

dF

∑ ∑ ∑ (xijk – xi-xj+xj)2

= --------------------------------- nr – r - c-1

we also have another formula for total means quare (TMS) which we shall not sue at present.

Total Mean Square (TMS)

Total Sum of Squares (TSS)= --------------------------------------

dF

MSBFBlocks = ------

MSE

∑ ∑ ∑ (xijk – xt)2

= --------------------------------- nr –1

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r

i=1

c

j=1

r

i=1

nis

k=1

c

j=1

r

i=1

nij

k=1

where nj = Sample size of treatment group jni = Sample size of block group ixj = Mean of block jxt = Total or ground meannij= Number of respondent (observations) receiving treatment I and blocking variable j

xijk= the Kth observation in treatment I and block j.nr = total number of observations

xr = xj nj

16.4 ANOVA FOR LATIN SQUARE DESIGN

In Latin Square Design we have two blocking variables and a controlling variable. In the chapter “Experimentation” we also designed a Latin square design for analyzing the effect of repackaging on Nimba. The size of the city and the size of the shop are the two blocking variables.

The formulae required are

Mean Square Row Block (MSR)

Sum of Squares Row Block (SSR)= ---------------------------------------------

dF

r ∑ (xi – xt)2

= ----------------- r-1

Mean Square Column Block (MSC)

Sum of Squares Column Block (SSC)= -------------------------------------------------

dF

c ∑ (xj – xt)2

= ----------------- c-1

Mean Square Treatment (MST)

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r

i=1

c

j=1

Sum of Squares Column Block (SSC)= -------------------------------------------------

dF

t ∑ (xk – xt)2

= ----------------- t-1

Mean Square Error (MSE)

Sum of Errors (SSE)= ----------------------------

dF

TTS – SSR – SSC – SST = ---------------------------------

(r – 1) (c – 2)

Total Mean Square (TMS)

Total sum of Squares (TSS)= -------------------------------------

dF

∑ ∑ (xij – xtj)2

= ---------------------- rc-1

MSRFRow = ------

MSE

MSCFColumns = ------

MSE

MSTFTreatment = ------

MSE

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c

j=1

r

i=1

t

k=1

where nj = Sample size of row block group jni = Sample size of column block group ink = Sample size of treatment group kxj = Mean of row ixj = Mean of column jxk = Mean of treatment group k xt = Total or ground mean

16.5 ANOVA WITH INTERACTION: FACTORIAL DESIGN

When there is reason to suspect interaction between the variables, we have to sue the factorial design method. In the chapter on “Experimentation” we have discussed the Nimba example again with two

TSS – SSB – SST= ------------------------

nr – r – c- 1

Independent variables – size and package design. We suspect an interaction between these factors.

Mean Square Treatment (MST)

Sum of squares treatments (SST)= --------------------------------------------

dF

∑nj (xj – xt)2

= ----------------- c-1

Mean Square Blocking (MSB)

Sum of Errors (SSB)= ---------------------------

dF

r ∑ni (xi – xt)2

= ----------------- r-1

Total Mean Square (TMS)

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c

j=1

r

i=1

Total Sum of Square (TSS)= ------------------------------------

dF

∑ ∑ ∑ (xijk – xt)2

= --------------------------------- nr –1

Mean Square Error (MSE)

Sum of Errors (SSE)= ---------------------------

dF

TSS – SST1 – SST2 – SSE= -------------------------------------

nr – rc

∑ ∑ ∑ (xijk – xt)2

--------------------------------- nr –1

MST1

FTreatment1 = -------MSE

MST2

FTreatment2 = -------MSE

MSIFInteraction = ------

MSE

16.6 ANALYSIS OF COVARIANCE

Often a researcher can introduce one or more variables statically into an experiment for the sole purpose of control. There are not real variables; they are purely imaginary variables.

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c

j=1

r

i=1

nij

k=1

c

j=1

r

i=1

nij

k=1

When a researcher would like to compare two or more groups on a dependent variable controlling one or more relevant variables, ANCOVA is useful. But it is often used (misused) in quasi-experimental studies or field studies. In the field, groups often intact and the individuals cannot be randomly assigned between the groups. This means that there are differences in the groups statistically. To make the groups statistically equal, ANCOVA is used to adjust the individual differences.

The mathematical methodology for implementing ANCOVA is too complicated to be explained here. It is a linear procedure that should be used with strict adherence to the assumptions. So we shall explain its use with an example. A study was done where the sample of corporations was classified into three control configurations, based on the distribution of stock ownership. These control configuration groups were Family Influence, management Control and Indirect Family Influence. One of the objectives of the studies was to assess the effects of managerial power in terms of control configuration groups on managerial tenure and longevity. The researcher felt that the corporate performance of each group of companies might bias the results of the analysis. So the researcher used corporate performance as a covariate in ANCOVA. Thus the characteristics of the groups were essentially equated. (The use of computer software makes such calculations easy).

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