RESEARCH METHODOLOGY AND QUANTITATIVE

TECHNIQUES WITH SOFTWARE APPLICATIONS (31.05.2010 to 04.06.2010)

Under the aegis of Quality Improvement Programme Centre

IIT Roorkee, Roorkee

Coordinated

By

D. K. Nauriyal S. P. Singh

Dept. of Humanities & Social Sciences Indian Institute of Technology Roorkee,

Roorkee - 247667 Uttarakhand

ACKNOWLEDGEMENTS

In organizing this course, we drew support from several people and organisations. First of all, we thank Prof. Vinod Kumar, Co-ordinator, QIP, IIT Roorkee, who has extended his complete support towards the smooth conduct of this programnme. Our sincere thanks are also due to Prof. S. P. Singh, the Head, Department of Humanities & Social Sciences for his encouragement and support. We owe profound gratitude to Prof. S. S. Dhillon, Punjab School of Economics, GNDU, Amritsar, Dr. R. K. Deswal, National Institute of Technology (NIT) Kurukshetra, and our institute especially Dr. Rashmi Gaur, who have kindly accepted our request to deliver lectures in the programme. We must also admit that without a very high level of receptivity, active involvement and cooperation from the participants, the programme would have never accomplished its objectives. They made the sessions lively and vibrant through their active and thoughtful interaction. They deserve all the appreciation and kudos. The infrastructural support and secretarial assistance received from the office of the Coordinator, QIP IIT Roorkee has been quite significant. It would not be fair on our part if we fail to acknowledge the timely and kind help lent by the QIP staff. Last but not the least we thank all those who have directly or indirectly contributed towards the success of this programme.

Coordinators

D. K Nauriyal S. P. Singh

CONTENT

Sl. No. TITLE SPEAKER PAGE

1 An Overview of Research Methodology D. K. Nauriyal 1-7

2 Research Design D. K. Nauriyal 8-10

3 Methods of Data Collection D. K. Nauriyal 11-16

4 Basic Statistical Measures S. P. Singh 17-19

5 Sampling Methods S. P. Singh 20-22

6 How to Conduct Surveys S. P. Singh 23-36

7 Parametric and Non-parametric Tests S.S. Dhillon 37-40

8 How to write research Proposal S.P.Singh 41-49

9 Refining Your Skills in Basic Statistical Analysis S.P.Singh 50-58

10 Statistical Sostware: SPSS S.P.Singh 59-63

11 DEA Techniques S.P.Singh 64-76

12 Advanced Multivariate Analysis S.P.Singh 77-93

13 Interpretation, Report Writing and Interpretation D. K. Nauriyal 94-109

14 List of Participants 110-116

1. AN OVERVIEW OF RESEARCH METHODOLOGY Is Research Science or Art?

Objectivity of Investigator Unbiased Procedural integrity Accurate reporting

Accuracy of Measurement

Valid and Reliable Meaningful and useful Appropriate design (sample, execution)

Open-minded to Findings

Willing to refute expectations Acknowledge limitations

It is an art of scientific investigation

A movement from Known to unknown A voyage of discovery

Objectives of Research To achieve new insight into a phenomenon (exploratory / formulative research. 2. To portray accurately the characteristics of a particular individual situation or a group (Descriptive Research). 3. To determine the frequency with which something occurs or with which it is associated with something else (Diagnostic Research). 4. To test a hypothesis of a causal relationship between variables (Hypothesis Testing Research). Why Research?

Taking the challenge to solve an unsolved problem. Desire to get intellectual satisfaction of doing some creative work. Desire to get a research degree. Desire to move up the career ladder in the academic institutions. Desire to be of service to the society.

Significance of Research 1. Research inculcates scientific and inductive thinking and it promotes the development

of logical habits of thinking. 2. Research provides the basis for nearly all govt. policies in our economic system.

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3. It helps to solve various operational and planning problems of business and industry. Market research / operations research/demand forecasting Conceptualizing the Research

Curiosity and intuition play an important role

What concept or puzzling phenomena is interesting?

Chapter 10Conducting & Reading Research

Baumgartner et al

(1)Identifying the

Research Problem/Opport

unity

(2)Determine the

Research Design

(3)Determine Data

Collection Method

(4)Design Data Collection

Forms

(5)Design Sample

and Collect Data

(6)Analyzing the

Data

(7)Preparing and Presenting the

Report

(8)Follow-up

The Research Process

Key Properties of Research Validity - Have you measured (or observed) what you think you have? Were the instruments used suitable for purpose? Have you adequately and faithfully captured the state of affairs? Reliability Even if the methods are valid, can we be sure that the data are consistent and a true reflection of the phenomena under study? Replicability This is essential in scientific work, it means that the work has been done and described in such a way that it is repeatable. In social science exact replication is often impossible, but similar studies and to the weight of evidence. Generalisability Are the findings generally applicable, for example to other contexts, situations, times, or persons other than the sample? Establishing Credibility Credibility is a property of good research. Care and attention in planning and conducting the research.

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Care and attention in writing it up in such a way that readers have confidence in the integrity of the work. Research reputations are established by repeatedly carrying out interesting and worthwhile work that is consistently methodologically strong and accurately reported. Approaches to the Research: Deductive and Inductive Deductive Approach: Deductive reasoning works from the more general to the more specific. It is a "top-down" approach. We might begin with thinking up a theory about our topic of interest. We then narrow that down into more specific hypotheses that we can test. We narrow down even further when we collect observations to address the hypotheses. This ultimately leads us to be able to test the hypotheses with specific data -- a confirmation (or not) of our original theories. Inductive Approach: Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. This is a "bottom up" approach. In inductive reasoning, we begin with:

specific observations and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up

developing some general conclusions or theories. Types of Research 1. Descriptive (Ex-post facto research) Vs. Analytical (Critical Evaluation of the material). 2. Applied (Action) Vs. Fundamental (Basic or Pure). 3. Quantitative (Inferential/experimental/ simulation) Vs. Qualitative. 4. Conceptual (abstract idea or theory) Vs. Empirical (Experience or observations based on data) 5. Longitudinal Research (Over a time period such as clinical or diagnostic research) Vs. Laboratory or Simulation Research. Quantitative and Qualitative Research Quantitative Research is an inquiry into an identified problem, based on testing a theory, measured with numbers, and analyzed using statistical techniques. The goal of quantitative methods is to determine whether the predictive generalizations of a theory hold true. All quantitative research requires a hypothesis before research can begin. Qualitative Research By contrast, a study based upon a qualitative process of inquiry has the goal of understanding a social or human problem from multiple perspectives. Qualitative research

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is conducted in a natural setting and involves a process of building a complex and holistic picture of the phenomenon of interest. In qualitative research, a hypothesis is not needed to begin research.

Quantitative Research In quantitative research, the researcher is ideally an objective observer who neither participates in nor influences what is being studied.

Qualitative Research In qualitative research, however, it is thought that the researcher can learn the most by participating and/or being immersed in a research situation

Characteristics of quantitative and qualitative research Quantitative Qualitative

Objective Subjective Research questions: How many? Strength of association?

Research questions: What?

"Hard" science "Soft" science Literature review must be done early in study

Literature review may be done as study progresses or afterwards

Test theory Develops theory One reality: focus is concise and narrow Multiple realities: focus is complex and

broad Facts are value-free and unbiased Facts are value-laden and biased Reduction, control, precision Discovery, description, understanding,

shared interpretation Measurable Interpretive Mechanistic: parts equal the whole Organismic: whole is greater than the parts Report statistical analysis. Basic element of analysis is numbers

Report rich narrative, individual; interpretation. Basic element of analysis is words/ideas.

Researcher is separate Researcher is part of process Subjects Participants Context free Context dependent Hypothesis Research questions Reasoning is logistic and deductive Reasoning is dialectic and inductive Establishes relationships, causation Describes meaning, discovery Uses instruments Uses communications and observation Strives for generalization Generalizations leading to prediction, explanation, and understanding Highly controlled setting: experimental setting (outcome oriented)

Strives for uniqueness Patterns and theories developed for understanding Flexible approach: natural setting (process oriented)

Sample size: n Sample size is not a concern; seeks "informal rich" sample

Which one to choose? Choose a more quantitative method when most of the following conditions apply:

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The research is confirmatory rather than exploratory i.e. this is a frequently researched topic, and (numerical) data from earlier research is available.

You are trying to measure a trend (almost impossible with qualitative

research).

There is no ambiguity about the concepts being measured, and only one way to measure each concept.

The concept is being measured on a ratio or ordinal scale.

And choose a qualitative method when most of these conditions apply:

You have no existing research data on this topic.

The most appropriate unit of measurement is not certain (Individuals? Households? Organizations?)

The concept is assessed on a nominal scale, with no clear demarcation

points.

You are exploring the reasons why people do or believe something. One extreme example:

You are studying the trends in weather in the town where you live. There aren't many variables: temperature ranges, wind speed, rainfall, barometric pressure, and perhaps a few others. Most of the variables are measured mechanically, and a lot of historical data exists. You wouldn't even consider doing qualitative research on this.

Research Methods vs Methodology Research Methods: Methods of data collection Statistical methods used for establishing relationships between the data and the unknowns. Methods used to evaluate the accuracy of results obtained. Research Methodology: Research Methods Consideration of the logic behind the methods we use. Research Process Series of actions or steps necessary to effectively carry out research and the desired sequencing of these steps.

A. Formulating the Research Problem: Understanding the problem thoroughly, and

Rephrasing the same into meaningful terms from an analytical point of view.

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Studies of broader literature (Conceptual and empirical)

This stage is important because 1. The research problem needs to be defined unambiguously. 2. It helps to collect the relevant data, choice of research methods etc.

B. Extensive Literature Survey:

1. Abstracting and indexing the journals published or unpublished bibliographies. 2. Identifying the academic journals, conference proceedings, govt. reports, books

etc. C. Development of Working Hypothesis:

Tentative assumptions made in order to draw out and test its logical or empirical consequences.

It is prior thinking about the subject. D. Preparing the Research Design Exploration / Description / Diagnosis / Experimentation. Research design needs consideration of the following:

The means of obtaining the information The availability and skills of the researcher Time available Cost factor

E. Determining the sample size Probability / Non-probability. Probability: Simple Random Sampling Systematic Sampling Cluster/Area Sampling Non-Probability / Purposive /Deliberate sampling: Convenience Sampling Judgment Sampling Quota Sampling F. Collecting the Data: Collection of only appropriate data

Primary Data- By observation, through personal interviews, telephone interviews and by mailing

of questionnaire

Secondary Data G. Analysis of Data:

1. Computation of statistics viz., mean, median, mode, standard Deviation, coefficient of variation, coefficient of skewness etc.

2. Designing regression equation for estimating dependent variable as a function of a set of independent variables.

3. Performing correlation analysis.

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4. Factor, Discriminat, Conjoint analysis H. Hypotheses Testing: I. Interpretation of Results: J. Validation of Results: The results after interpretation must be validated by using

past data. It ensures credibility of the results. K. Report Writing

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2. RESEARCH DESIGN

RESEARCH DESIGN It is a conceptual structure within which research is conducted. It constitutes the blue print for the collection, measurement and analysis of data The research design addresses the following questions:

1. What is the study about? 2. Why is the study being done? 3. Where will the study be carried out? 4. What type of data is required? 5. Where can the required data be found? 6. What periods of time will the study include? 7. What will be the sample design? 8. What techniques of data collection will be used? 9. How will the data be analyzed? 10. Style of Report.

Thus the Essentials of the Research Design are:

The design is an activity and time-based plan. The design is always based on the research question. The design guides the selection of the sources and types of information. The design is a framework for specifying the relationships among the studys

variables. The design outlines procedures for every research activity.

Research Design Type of Study

Exploratory/ Formulative Descriptive / Diagnostic Overall Design Flexible (for considering

different parts of the problem Rigid Design

Sampling Design Non-probability (purposive or Judgment)

Probability (Random Sampling)

Statistical Design No Pre-planned design for analysis

Pre-planned design for analysis

Observational Design Unstructured instruments for collection of data

Structured or well thought out instruments for collection of data.

Opeartional Design NO fixed decisions about the operational procedures

Advanced decisions about operational procedures.

Determining Research Design

Exploratory Research: collecting information in an unstructured and informal manner.

Descriptive Research: refers to a set of methods and procedures describing

marketing variables.

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Causal Research (experiments and other approaches): allows isolation of causes and effects via use of experiment or surveys.

Components of Research Design A. Sampling Designs.

Probability: Simple Random Sampling, Systematic Sampling, Stratified Random Sampling, Cluster Sampling, Multi-stage Sampling

Non-Probability: Convenience Sampling, Judgment Sampling, Quota sampling, Snowball Sampling

B. Statistical Designs (Sample size, collection and analyses of data). C. Operational Designs (Techniques by which the procedure specified in the sampling, statistical and observational designs can be carried out). Features of a Good Design

1. Generally, the design which minimizes biases and maximizes the reliability of the data collected and analyzed is considered a good design.

2. A good research design involves consideration of the following factors: a. The means of obtaining information b. The availability and skills of the researcher and the staff, if any c. The objective of the problem to be studied d. The nature of the problem to be studied. e. The availability of time and money for the research work. Important Concepts Relating to Research Design 1. Dependent and independent variables 1. Extraneous variables: (Independent variables that are not related to the purpose of the study but may affect the dependent variable are terms as extraneous variables). 3. Control: (The technical term is used when we design the study minimizing the effects of extraneous independent variables). 4. Confounded relationships (When the dependent variable is not free from the influence of extraneous variables). 5. Research Hypotheses:

The research hypothesis is a predictive statement that relates an independent variable to a dependent variable.

Predictive statements which are assumed but not to be tested are not termed as research hypotheses.

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Experimental and non-experimental hypothesis testing research: For example mothers age 15-45 overall group above 15 years. 7. Experimental and control groups: For example dummy variable of caste / religion.

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3. METHODS OF DATA COLLECTION

Methods of Data Collection

Primary data: information that is developed or gathered by the researcher specifically for the research project at hand

Secondary data: information that has previously been gathered by someone other

than the researcher and/or for some other purpose than the research project at hand Classification of Secondary Data

Internal secondary data: data that have been collected within the firm

Internal databases: databases (collection of data and information describing items of interest) consisting of information gathered by a company typically during the normal course of business transactions

External secondary data: data obtained from outside the firm Types:

Published Syndicated Services Data External Databases

External secondary data Published: sources of information prepared for public distribution and

found in libraries or a variety of other entities

Syndicated Services Data: data provided by firms that collect data in a standard format and make them available to subscribing firms

External secondary data

External Databases: databases provided by outside firms; many are now available online (online information databases)

Bibliographic databases..citations by subject Numeric or statistical databases, 2001 Census Government Reports, Other Studies Directory or list databases Comprehensive databases, Contain all of the above

Advantages of Secondary Data

Obtained quickly (compared to primary data gathering) Inexpensive (compared to primary data gathering) Usually available Enhances existing primary data

Limitations of Secondary Data

Exact data that one may need may not be available. May have difficulty in getting access. Errors in data base.

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Possible coding problems Data may be available but it may have problems:

Missing or incomplete data. Unknown definitions of data. Changed definitions or procedures. Might be too aggregated. Evaluating Secondary Data

What was the purpose of the study? Who collected the information and when was this done? What information was collected (questions, scales, etc.)? How was the information obtained (sampling frame, method of sample draw,

communication method, resulting sample, etc.)? How consistent is the information with other published information?

Locating Secondary Data Sources

Step 1:Identify what you wish to know and what you already know about your topic.

Step 2: Develop a list of key words and names. Step 3: Begin your search using several library and Web sources. Step 4: Compile the literature you have found and evaluate your findings.

Primary Data Collection Methods of Primary Data: Observation, In-depth Techniques, Experimentation, Surveys Types of Observations Structured (Descriptive) Unstructured (Exploratory) Participant (Anthropological) Non-Participant (Political forecasts) Disguised Participation (Presence of the observer is hidden) In-Depth Techniques: Focus groups Interviews Interviews: Personal, Telephonic, Focused, Non-Directive Projective Techniques Primary Data Collection Methods 1. Observation

Human or physical observation includes mystery shopping, cameras in store, watching children handle toys, etc.

Ethnography watching behaviors in the consumers natural setting Mechanical or electronic observation using Nielsen people meter, eye tracking

devices, or using software to track behaviors on the Web, etc. Limitations:

A. Expensive method in terms of time and money B. Limited Information. C. Interference of unforeseen factors

Merits of interview method

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It provides greater information in-depth. Can overcome the resistance through persuasion . Personal information can be sort. Low no-response. Can secure most spontaneous reactions. Adaptability of the language to the level of interviewee. Can collate supplementary information which maybe of great value in

interpreting the results. Interviewer can clarify unclear questions Literacy is not required Interviewer can collect more complex answers and observations Interviewer can minimize missing and inappropriate responses Interviewer can prevent respondent from answering out of sequence

Interview method is most useful when:

Other methods do not make sense. When the issues are complex and in-depth understanding is needed. When the issues and questions are still being determined.

Pre-requisites of interviewing

Careful selection, training, and briefing of the interviewer. Must ask questions properly and intelligently. Must answer legitimate questions of the interviewee. Should not show surprise or disapproval Must discourage irrelevant conversation.

Interviewer must possess the technical competence and necessary practical experience.

Occasional field checks. Guidelines for successful Interviewing: 1) Choose the time when the interviewee is at ease. 2) Approach must be friendly and informal. 3) Establish Rapport with the interviewee. - People are motivated to communicate when atmosphere is favorable. 4) Listen with understanding, respect and curiosity. 5) Control the course of the interview and avoid irrelevant conversation. Demerits:

1. No thinking space to the interviewee 2. Survey is restricted to those, who have telephone facilities. 3. Unsuitable for intensive surveys where comprehensive answers are required. 4. Greater possibility of bias.

Limitations of the interview method

Possibility of Data collection and interpretation biases. Time consuming when sample is large. May introduce systematic errors. Lack of proper rapport , with the interviewee.

Through mailed questionnaires

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Merits: Low cost Free from the bias of the interviewer. Enough thinking space. Can be reached to otherwise inaccessible people. Sample could be larger. Demerits: Low rate of return. Only the educated and cooperating people could be approached. Difficulty in modifying the approach once the questionnaire is made. Possibility of ambiguous replies/omissions of questions. Method is slightly to be slowest of all. Important considerations while framing a questionnaire

A) General form (close ended/Open ended). Question sequence: First few questions are important because they are lightly to influence the attitude and the desired cooperation from the respondent

First questions should break the ice General to specific order of questions Questions on personal or sensitive topics left towards the end Avoid a series of questions that are likely to elicit the same response (bias) One question can affect another Questions should be easily understood and should be simple There should always be provision for indications of uncertainty. e.g." Don't know

No preference Questionnaire Design: General Principles Open-ended vs closed-ended questions: Open-ended questions generate answers that are more nuanced and information-rich. They permit subject freedom to answer question in own words (without pre-specified alternatives). Open-ended questions do not provide respondents with any answers from which to choose. Open-ended Questions: Advantages and Disadvantages Advantages:

Not forced to choose between categories May better reflect respondents thoughts\beliefs Appropriate when list of possible answers is excessive Lets respondent have the say, let him tell the researcher what he means, and not

vice-versa (obtain unanticipated answers)

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Disadvantages: Respondent may say too much or too little Provide incomplete or unintelligible answers Flexibility in responses difficult to code and analyze -Interpretations of answers

may vary Too much variance in response Expensive and time-consuming

Closed-ended Questions Closed-ended questions provide respondents with a list of responses from which to choose. Alternatively, closed-ended questions can provide multiple choices for the respondent to accept or reject Closed-ended Questions: Advantages and Disadvantages - Advantages:

Easy to answer and takes little time Answers can be precoded (assigned a number) and easily transferred to a

computer Answers are easy to compare Easier to elicit responses to sensitive questions Answers are more reliable Meaning of responses more meaningful to researcher

Disadvantages:

May not be accurate--forces people to accept categories, or puts too many people into other category

Answers relative to response scale provided Respondent's choice not among listed alternatives Choices listed communicate kind of response wanted Wording of response choices may influence responses

Difference between a questionnaire and a schedule 1) Questionnaires are sent through mail to the informants while schedules are filled in

either by the researcher himself or by the enumerators who are specially appointed for the purpose.

2) Questionnaire is relatively cheap but data collection through schedules in expensive.

3) Non-response is high in case of a questionnaire.

4) In case of a questionnaire, identity of the person who has actually filled in may be unknown as he/she might be doing it on behalf of someone else.

5) Questionnaire method is slow as many respondents may not return the filled in response in time.

6) Personal contact is not possible in case of questionnaires.

7) Questionnaire method can be used only when respondents are literate and cooperative.

8) Coverage with questionnaire could be wider and cheap.

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9) Risk of collecting incomplete and wrong information is relatively more under the questionnaire method particularly when people are unable to understand questions properly.

10) Observation method can also be used along with the schedules but it is not possible with the questionnaire.

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4. BASIC STATISTICAL MEASURES The data of given situation must be characterized by some statistical measure for the purpose of estimation or comparison with similar data or making inference about the sample population to which the data belong. Statistical measures can be classified into:

1. Measures of Central tendencies 2. Measures of Variation 3. Measures of Skew ness 4. Measures of Kurtosis 5. Time series 6. Correlation 7. Regression

1. Measures of Central Tendency: Following are the measures of central tendency. (a) Arithmetic Mean (b) Weighted Arithmetic Mean (c ) Median (d) Mode (e) Geometric Mean (f) Harmonic Mean 2. Measures of Variation are 1. Range and Coefficient of range 2. Quartile deviation and Average Deviation 3. Standard Deviation 4. Coefficient of Variation 3. Measures of Skewness : Shape of distribution is another characteristics which is a measure of concern. Shape of a distribution explains the nature of the distribution of frequencies of observations which is defined as Coefficient of Skewness and it ranges from -1 to +1. If coefficient is zero then distribution is symmetrical. If it is +ive then distribution is positively skewed then relation between mean, median and mode is Mean>Median>Mode If coefficient is ive the distribution is ively skewed here Mean

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Graphs can used to plot such values and is called Histogram.of time series. The fluctuations may be due to:

(a) Causes which operate over a long time period (b) Causes which operate over a short time period These causes are segregated and this process is called analysis of time series. The variations in the value of the variance can be analysed into the following three main components. 1. The basic or long time period 2. Short time of periodic changes 3. Irregular fluctuations

Measurement of Trend:

a. Free hand smoothing b. Sectional Average: in this whole series is divided into suitable number of sections

and average of each section is found. These averages are plotted against the mid year of the sections, then a free hand smooth curve is drawn through these points. The curve represents Trend.

c. Method of Moving Average: In this fluctuations due to cyclical changes are eliminated by averaging the values of the variance for a specified number of successive years. Number of years over which the values are averaged depends upon the average length of the cycle found in the series. Then mean is taken. All these means are plotted and successive points are joined by straight line segment. The resulting polygonal graph indicates the trend of the given time series.

d. Method of Least Squares: From the mid year of the time series , time deviations are to be taken and these deviations are to be squared. Then multiply the values with the squares. The resultants are trend ordinates. When these are plotted against the corresponding year we get the line of best fit in the sense of least square.

Correlation: The relation between two or more characteristics of a population or a sample can be studied with the help of a statistical method called correlation. If two quantities vary in a related manner so that a movement in increase or decrease in one tends to be accompanied by a movement in the same or in the opposite direction in the other, the two quantities are said to be correlated. It may be +ive or ive. It may be perfect or imperfect. Methods: 1. Graphic Method; 2. Scatter Diagram; 3. Co-efficient of correlation Co-efficient of correlation is a numerical measure of correlation.

1. Karl pearson coefficient of correlation also called product movement correlation 2. Spearmans rank correlation Test of significance is done for both measures by using t- test

Coefficient of Determination measures variations explained by the independent variable. It is ratio of explained variations to total variations. Regression Analysis: Regression means stepping towards the average. Regression is dependence of a variable on one of more variable/s.

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Y = a+ b X +u is a linear form of regression equation. Y = a + b1X1 + b2X2+ -------+ bn Xn+ Ui is a example of multiple regression U is a random error to fit the st. line we apply the method of least squares. In order to estimate a and b we need to minimize sum of square of Ui. For this we solve the required regression equations. Coefficient of Determination: In order to measure the extent of strength of correlation between the dependent and independent variable/s we calculate the statistic called coefficient of determination (r2/R2) This measure is developed on the basis of two levels of variations The variations of Y values around the fitted regression line given by (Y-^Y)2 and The variations of Y values around their own mean given by (Y-Y*)2 Where Y* is mean of Y. then r2/R2 = 1- e2 /Y2 The value of r2/R2 shows the goodness of fit of the regression equation/s. Higher value of r2/R2 , higher the closeness of fit and lesser r2/R2 , lesser the goodness of fit.

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5. SAMPLING METHODS

Sampling is a process of selecting a subset of randomized number of members of the population of a study and collecting data about their attributes. Based on the data of the sample the analyst will draw inference about the population. Advantages of Sampling:

(1) Less time taken to collect data (2) Less cost for data collection (3) Physical impossibility of complete enumeration necessitates sampling (4) More accuracy of data collected due to its limited size.

Sampling Frame: The complete list of all the members/units of the population from which each sampling unit is selected is known as sampling frame. It should be free from error. Sampling Methods: Sampling methods are divided into two/;

(a) Probability Sampling (b) Non-Probability Sampling

Probability Sampling: In probability sampling each unit of the population has a probability of being selected as an unit of the sample. But this probability varies from one method to another method of probability sampling. In non-probability sampling there may be instances that certain units of population will have zero probability of selection, because judgment biases and convenience of the interviewer are considered to be the criteria for the selection of sample units. Probability Sampling Methods:

(1) Simple Random sampling (2) Systematic Sampling (3) Stratified Sampling (4) Cluster Sampling (5) Multistage Sampling (1) Simple Random Sampling: Let N = No of units of population

n = no of unit of sample Where n< N There are two ways of performing SRS (a) with replacement abd (b) without replacement SRS with replacement: Each unit of the pop. has the equal probability of being selected. Prob. Of selection = 1/N Selection is done by using Random Number Tables SRS without replacement: Each unit of pop has varing prob of being selected as an unit of the sample. The Prob of First unit = 1/N The Prob of second unit = 1/N-1 -- - - - - - - - - - - - - - - - The Prob of nth unit = 1/N-(n-1)

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Unit are selected from the population based on the respective probability using Monte-Crlo Simulation.

(2) Systematic Sampling: This is a special kind of random sampling in which the selection of the first unit of the sample from the popo is based on randomistion. The remaining units of the sample are selected from the pop at a fixed interval of n, where n is a sample size. Sampling interval width I = N/n

(3) Stratified Sampling: It is an improvised sampling over simple random sampling and systematic sampling. In this sampling pop is divided into specified set of strata such that members with in stratum have similar attributes but members between strata have dissimilar attributes.

(a) Proportional Stratified sampling: when same proportion of units are selected from each stratum. There is no much difference (less variance) in attributes with in each stratum.

n is the sample selected such that n = n1+n2+------+nk N=pop size, Ni=Strata Size; ni = size of sub sample n1/N1=n2/N2=--------=nk/Nk=n/N n1= n.N1/N------------------- nk=n.Nk/N (b) Dis proportional S.S.: When different proportion of units are selected from

each stratum. Attributes differ and there is high variance. In this sampling the stratum which has more variance will have prop more sampling units as compared to other stratum with less variance. No of sampling units of the stratum i=ni= qi.si. n/ Sum qisi si is st deviation of stratum i qi= Ni/N

(4) Cluster Sampling: Pop is divided into different clusters. Memebers within the cluster are dissimilar in terms of their attributes. But different clusters are similar to each other. Each cluster can be treated as a small population which possess all the attributes of the pop. Any one cluster is selected and all units of cluster constitute the sample.

(5) Multistage Sampling: In a large scale survey covering the entire nation the size of

the sample frame will be very large. In such study multistage sampling technique is used.

The entire country is divided into regions. Stage 1: Different states of the country are sampled from each region using stratified sampling. Here it is assumed that the states within the region are similar and the regions are dissimilar. Stage 2: Then cluster sampling is can be used from each selected state by assuming that different districts of each state as its cluster. Stage 3: In each selected district a random sampling may be used to select the proportional number of units from it.

II. Non-Probability Sampling Methods:

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1. Convenience Sampling 2. Judgment Sampling also called Purposive Sampling 3. Quota sampling 4. Snowball Sampling

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6. HOW TO CONDUCT SURVEYS Introduction This lecture will help you to learn how to conduct a survey and design a questionnaire. Survey research is being conducted in almost all areas of management, economics and social sciences. It is quite relevant to understand the various techniques and tools used in survey research. As a faculty of management and social sciences, you conduct market research, socio-economic evaluation study, opinion-based studies, and policy and programme assessment studies. For all such type of studies, conducting survey through questionnaire becomes essential. Keeping this in view, this lecture focuses on survey research techniques and how to design a questionnaire that gets the true opinions of your sample. Questionnaires are the most common marketing research method. They are used for structured interviews, written surveys, email, and internet surveys. Conducting a survey is a useful way of finding something out, especially when `human factors' are under investigation. Although surveys often investigate subjective issues, a well-designed survey should produce quantitative, rather than qualitative, results. That is, the results should be expressed numerically, and be capable of rigorous analysis. Researchers quite often underestimate how difficult it is to carry out a survey well; a good survey is more than a handful of questionnaires and a couple of bar charts: it requires careful planning, methodical application, and detailed analysis of the results Methods of getting information We are living in an information age. More information has been published in the last decade than in all previous history. Everyone uses information to make decisions about the future. If our information is accurate, we have a high probability of making a good decision. If our information is inaccurate, our ability to make a correct decision is diminished. Better information usually leads to better decisions. There are six common ways to get information. These are: literature searches, talking with people, focus groups, personal interviews, telephone surveys, and mail surveys. 1. A literature search involves reviewing all readily available materials. These materials can include internal company information, relevant trade publications, newspapers, magazines, annual reports, company literature, on-line databases, and any other published materials. 2. Talking with the people is a good way to get information during the initial stages of a research project. It can be used to gather information that is not publicly available, or that is too new to be found in the literature. Examples might include meetings with prospects, customers, suppliers, and other types of business conversations at trade shows, seminars, and association meetings. Although often valuable, the information has questionable validity because it is highly subjective and might not be representative of the population. 3. A Focus Group is used as a preliminary research technique to explore peoples ideas and attitudes. It is often used to test new approaches (such as products or advertising), and to discover customer concerns. A group of 6 to 20 people meet in a conference-room-like setting with a trained moderator. The room usually contains a one-way mirror for viewing, including audio and video capabilities. The moderator leads the group's discussion and keeps the focus on the areas you want to explore. Their disadvantage is that the sample is small and may not be representative of the population in general.

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4. Personal Interviews are a way to get in-depth and comprehensive information. They involve one person interviewing another person for personal or detailed information. Personal interviews are very expensive because of the one-to-one nature of the interview. Typically, an interviewer will ask questions from a written questionnaire and record the answers verbatim. Personal interviews (because of their expense) are generally used only when subjects are not likely to respond to other survey methods. 5. Telephone Surveys are the fastest method of gathering information from a relatively large sample (100-400 respondents). The interviewer follows a prepared script that is essentially the same as a written questionnaire. However, unlike a mail survey, the telephone survey allows the opportunity for some opinion probing. Telephone surveys generally last less than ten minutes. 5. Mail Surveys are a cost-effective method of gathering information. They are ideal for large sample sizes, or when the sample comes from a wide geographic area. They cost a little less than telephone interviews, however, they take over twice as long to complete (eight to twelve weeks). Because there is no interviewer, there is no possibility of interviewer bias. The main disadvantage is the inability to probe respondents for more detailed information. 6. Email and Internet surveys are relatively new and little is known about the effect of sampling bias in Internet surveys. While it is clearly the most cost effective and fastest method of distributing a survey, the demographic profile of the Internet user does not represent the general population, although this is changing. Before doing an e-mail or Internet survey, carefully consider the effect that this bias might have on the results. What is Survey? A survey is a method of collecting information directly from people about their ideas, feelings, health, plans, beliefs, and social, educational and financial background. It usually takes the form of self-administered questionnaires and interviews. Self-administered questionnaires can be completed by hand or by computer. Interviews take place in person or on telephone. Why do we conduct survey? There are at least three good reasons for conducting surveys.

1. A policy needs to set or a programme must be planned.

Surveys are conducted to meet policy or programmes needs. For instance, a company is considering providing day care for children of its working staff. How many have young children? How many would use the agency services?

2. You want to evaluate the effectiveness of programmes to change peoples knowledge, attitudes, health or welfare.

3. You are a researcher and a survey is used to assist you.

When is a Survey Best? Many methods are available for obtaining information about people. A survey is one of them. Surveys can be used to make policy or to plan and evaluate programmes and

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conduct research when the information you need should come directly from people. The data they provide are descriptions of attitudes, values, habits and background characterizes such as age, health, education and income.

Types of Survey

1. Cross-sectional Survey With this design, data are collected at a single point in time. Think of a cross sectional survey as a snapshot of a group of people or organizations. Cross-sectional surveys have several advantages. 2. Longitudinal Surveys With longitudinal survey, data are collected over time. At least three variations are particularly useful. (a) Trend: a trend design means surveying a particular group over time. For example,

studying a group of rural peoples socio-economic conditions over time. (b) Cohort: In cohort survey, you study a particular group over time but people in the

group may vary. (c) Panel: panel survey means collecting data from the same sample over time.

The Survey Content

Select your information needs or Hypotheses. Make sure you can get the information you need Do not ask for information unless you can act on it. Survey items may take form of open-ended or forced choice questions: Forced

choice questions with several choices are easier to score than open ended, short answer, essay questions. Open ended questions give respondents an opportunity to state a position in their own words; unfortunately these words may be difficult to interpret.

Rules for Writing Survey Items with Forced Choices

1. Each question should be meaningful to respondents. In a survey of political views, the questions should be about the political process, parties, candidates and so on. If you introduce other questions that have no readily obvious purpose, such as those about age or gender, you might want to explain why they are being asked.

2. Use Standard English. Because you want an accurate answer to each survey items, you must use conventional grammar, spelling and syntax.

3. Make questions concrete: questions should be close to the respondents personal experience. For instance, asking respondents if they enjoyed a book is more abstract than asking if they recommended it to others or read more books of the same author.

4. Avoid biased words and phrases: certain names, places and views are emotionally charged. When include in a survey, they unfairly influence peoples response.

5. Check your own biases: an additional source of bias is present when survey writers are unaware of their own position towards a topic. Look at this:

Do you think the left parties and the congress will soon reach a greater degree of understanding? (Biased question) When you have questions that you suspect encourage strong views on either side.

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Better: in your opinion, in the next two years, how is the relationship between the left parties and congress likely to change? Much improvement Some improvement Some worsening Much worsening Impossible to predict

6. Use caution when asking about the personal: another source of bias may result from questions that may intimidate the respondent.

How much do you earn each year? Are you single or divorced? How do you feel about your teacher, counselor or doctor? When personal information is essential to the survey, you can ask questions in the least emotionally charged way if you provide categories of responses. Example: Poor: What was your annual income last year? Rs.. Better: In which category does your annual income last year fit best: Below Rs. 100000 Rs. 100000 - Rs.150000 Rs. 150000 Rs. 200000 Rs. 200000 Rs. 250000 Rs. 250000 and above.

7. Each question should have just one thought: do not use questions in which a

respondents truthful answer could be both ways yes and no at the same time

Survey Design Here, we shall discuss options and provides suggestions on how to design and conduct a successful survey research. There are 7 steps in the survey research:

1. Establish the objectives of the study - What you want to examine 2. Determine your sample - Whom you will interview 3. Choose interviewing methodology - How you will interview 4. Create your questionnaire - What you will ask 5. Pre-test the questionnaire, if practical - Test the questions 6. Conduct interviews and enter data - Ask the questions 7. Analyze the data - Produce the reports

Setting Objectives The first step in any survey is deciding the objectives. If your objectives are unclear, the results will probably be unclear. Some typical objectives may be like these:

The potential market for a new product or service Ratings of current products or services Employee attitudes Customer/patient satisfaction levels Reader/viewer/listener opinions Association member opinions

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Opinions about political candidates or issues Corporate images

Selecting Your Sample

There are two main components in determining whom you will interview. The first is deciding what kind of people to interview. Researchers often call this group the target population. If you conduct an employee attitude survey or an association membership survey, the population is obvious. If you are trying to determine the likely success of a product, the target population may be less obvious. Correctly determining the target population is critical. If you do not interview the right kinds of people, you will not successfully meet your goals.

The next thing to decide is how many people you need to interview. You must make a decision about your sample size based on factors such as: time available, budget and necessary degree of precision.

Bias A survey is biased if its outcome has been influenced by factors other than the one being studied. Bias is occasionally overt: the experimenter is not open-minded about the results, and interprets them wrongly. But more often bias comes from poor survey design. A typical problem is that of comparing two groups of people that are not really alike. For example, if there are more men than women in one group, and more women than men in another, the responses of the groups to any question will be influenced by the differences between men and women. The solution to this problem is that of randomization. In some cases it is necessary to use `stratified' random sampling to ensure that the sample is typical of the population.

Selecting Respondents Select survey respondents at random from the intended audience. If at all possible, identify a comparison group that doesn't get the information so that you can see how much of the change in knowledge, attitude, and/or behavior is a result of your information versus a result of other factors in the market place. This is a variation on a control group; in a real experiment, you would randomly assign people to either a group that gets the information or the control group that would not. But random assignment is not feasible in the context of report cards, so a comparison group is an acceptable alternative. One of the easiest ways to create a comparison group is to collect baseline data, i.e., responses to key questions collected before the information was disseminated. This is often referred to as a "pre/post" survey. You do not have to contact the same people before and after the distribution period. But be sure to survey a representative sample each time so that their responses are comparable. Interviewing Methods Once you have decided on your sample you must decide on your method of data collection. Each method has advantages and disadvantages. Some of the methods are listed as follows: 1. Personal Interview: An interview is called personal when the Interviewer asks the questions face-to-face with the Interviewee. Personal interviews can take place in the home, at a shopping mall, on the street, outside a movie theater or polling place, and so on.

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2. Telephone Surveys: Surveying by telephone is the most popular interviewing method. This is made possible by nearly universal coverage.

3. Mail Surveys: One way of improving response rates to mail surveys is to mail a postcard telling your sample to watch for a questionnaire in the next week or two. Another is to follow up a questionnaire mailing after a couple of weeks with a card asking people to return the questionnaire.

4. Computer Direct Interviews: These are interviews in which the Interviewees enter their own answers directly into a computer. They can be used at malls, trade shows, offices, and so on. Some researchers set up a Web page survey for this purpose.

5. Email Surveys: Email surveys are both very economical and very fast. More people have email than have full Internet access. This makes email a better choice than a Web page survey for some populations. On the other hand, email surveys are limited to simple questionnaires, whereas Web page surveys can include complex logic.

6. Internet/Intranet (Web Page) Surveys: Web surveys are rapidly gaining popularity. They have major speed, cost, and flexibility advantages, but also significant sampling limitations. These limitations make software selection especially important and restrict the groups you can study using this technique. Internet survey is recommended mainly when your target population is Internet users. Business-to-business research and employee attitude surveys can often meet this requirement. Another reason to use a Web page survey is when you want to show video or both sound and graphics. A Web page survey may be the only practical way to have many people view and react to a video. Tips for Improving Response Rates

Know your respondents. Make certain the questions are understandable to

them, to the point, and not insensitive to their social and cultural values.

Use trained personnel to recruit respondents and conduct surveys. Set up a quality assurance system for monitoring quality and retraining

Identify a larger number of eligible respondents than you need in case you do not get the sample size you need.

Keep survey responses confidential or anonymous

Send remainders to complete mailed surveys and make repeat phone calls.

Provide gift or cash incentives

Be realistic about the eligibility criteria. Anticipate the proportion of respondents who may not be able to participate because of survey circumstances (such as incorrect addresses) or by change (sudden illness).

Formally respect each respondents privacy.

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Part-II

DESIGNING QUESTIONNAIRE/SCHEDULE Introduction Questionnaire is widely used for data collection in survey research. It is fairly reliable tool for gathering data from large, diverse, varied and scattered groups. Questionnaire is a list of questions sent to a number of persons for their answers and which obtains standardised results which can be tabulated and treated statistically. Sometimes a distinction is made between `questionnaire and `schedule or `interview guide. Generally questionnaire is mailed to the respondents who are to give answers in a manner specified either in the covering letter or in the main questionnaire itself. On the other hand a schedule refers to a form of questionnaire which is generally filled in by the investigator himself. He/she sets with the informant face to face and fills the form. Schedule is more effective than mailed questionnaire because in most of the cases, respondents do not response properly the mailed questionnaire because of ignorance, illiteracy and lack of awareness and interest, while in case of schedule, investigator has face to face contact with respondents, she/he would be able to get reliable information from the respondents. Types of Questionnaire Questionnaire may be broadly of two types, viz. Structured and unstructured questionnaire. According to P.V. Young, structured questionnaires are those which pose definite, concrete, and pre-determined questions, i.e.; they are prepared in advance and not constructed on the spot during the question period. Additional questions may be asked only when some clarification is required. Answers to these questions are normally given with high precision. For e.g. age, sex, marital status, number of children nationality etc., are automatically structured. Structured questionnaire may further be grouped into closed form or open-end questionnaire. A close form questionnaire is one in which questions are set in such a manner that it leaves only a few alternative answers. The informant is left with only a few choices to answer them. For e.g., do you think poverty and unemployment have increased in India after economic reform? Yes/No/Cant say. In above stated question, respondent has to select one out of three alternatives. The open-ended questionnaire, on the other hand, is one in which the respondent has full choice of using his own style and diction of language expression, length and perception. He has enough freedom while providing answers to open questions. The unstructured questionnaire contains a set of questions which are not structured in advance and which may be adjusted according to the need of question period. The unstructured questionnaire is used mainly for conducting interviews. Flexibility is its chief merit. A widespread criticism of closed questionnaire is that they force people to choice among offered alternatives instead of answering in their own words. Closed questions spell the response options; they are more specific than open questions and therefore more apt to communicate the same frame of reference to all respondents. Let us take a hypothetical case; we want to identify the most important problem facing the country. In open-closed experiment people are asked what they think is the most

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important problem facing the nation. In a close-ended framework, we set five alternatives, namely, unemployment, economic disparity, crime, poor governance and inflation. One open-ended question is also set. In response to the open-ended questions, the respondents may identify power shortages the most vital problem of the country. Thus, open-ended questions are also relevant, especially when the researcher has inadequate knowledge about the various problems faced by the country.

Construction of Questionnaire

A. General Considerations

1. Most problems with questionnaire analysis can be traced back to the design phase of the project. Well-defined goals are the best way to assure a good questionnaire design. When the goals of a study can be expressed in a few clear and concise sentences, the design of the questionnaire becomes considerably easier. The questionnaire is developed to directly address the goals of the study.

2. One of the best ways to clarify your study goals is to decide how you intend to use the information. This sounds obvious, but many researchers neglect this task.

3. Be sure to commit the study goals to writing. Whenever you are unsure of a question, refer to the study goals and a solution will become clear. Ask only questions that directly address the study goals.

4. KISS - keep it short and simple. If you present a 20-page questionnaire most potential respondents will give up in horror before even starting. A one of the most effective methods of maximizing response is to shorten the questionnaire.

5. If your survey over a few pages, try to eliminate questions. Many people have difficulty knowing which questions could be eliminated. For the elimination round, read each question and ask, "How am I going to use this information?" If the information will be used in a decision-making process, then keep the question... it's important. If not, throw it out.

6. Involve other experts and relevant decision-makers in the questionnaire design process.

7. Formulate a plan for doing the statistical analysis during the design stage of the project. Know how every question will be analyzed and be prepared to handle missing data. If you cannot specify how you intend to analyze a question or use the information, do not use it in the survey.

8. Provide a well written cover page. The respondent's next impression comes from the cover letter (for mailed questionnaire). It provides your best chance to persuade the respondent to complete the survey.

9. Giver your questionnaire a title that is short and meaningful to the respondents. A questionnaire with a title is generally perceived to be more credible than one without.

10. Begin with a few non-threatening and interesting items. If the first items are too threatening or "boring", there is little chance that the person will complete the questionnaire.

11. Leave adequate space for respondents to make comments. Leaving space for comments will provide valuable information not captured by the response categories.

12. Place the most important items in the first half of the questionnaire. Respondents often send back partially completed questionnaires.

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13. Use professional production methods for the questionnaireeither desktop publishing or typesetting and key-lining. Be creative.

14. The final test of a questionnaire is to try it on representatives of the target audience.

B. Language The wording of a question is extremely important. Researchers strive for objectivity in surveys and, therefore, must be careful not to lead the respondent into giving a desired answer. Many investigators have confirmed that slight changes in the way questions are worded can have a significant impact on how people respond. Because questionnaires are usually written by educated persons who have special interest in and understanding of the topic of their investigation and because these people usually consult with other educated and concerned persons, it is common for questionnaires to be overwritten, over complicated, and too demanding of the respondent. Therefore, it requires special measures to cast questions that are clear and straight forward in four important aspects; simple language, common concepts, manageable tasks and widespread information. In choosing the language for a good questionnaire, the nature and structure of population to be studied should be kept in mind. Technical terms and jargons should be avoided to the maximum possible extent. Words used in ordinary conversation should be preferred. For example: Acquaint - inform Assist - help Consider - think Reside - live State - say Sufficient - enough Initiate - start and so on In surveys of general population, questions should consist of simple words, which convey the exact meaning. Ambiguous and vague words should be avoided. As far as possible, the words of local dialect should be used. Double version of questions should be avoided. Common concepts should be used in the questionnaire. Mathematical abstractions tend to be difficult for the general public `variance for instance survey investigators would not think of asking the general public questions about variances or standard deviations. They know perfectly well that the concept of an average is much more widely understood than others.

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C. Question Content A questionnaire designer has to ensure that all the necessary items are duly incorporated in the questionnaire. The investigator may take the help of standard checklists to see that all the required items are included in the questionnaire. The checklist can also be prepared by the investigator himself. Check lists may differ depending upon the aims and objectives of the survey research. Some of the important items of checklist of content are as follows: 1. Is this question necessary for clear understanding? Just how well it is used. 2. Are several questions needed on the subject matter of this one question? 3. Do the respondents have the information necessary to answer the questions? 4. Does the question need to be more concrete, more specific and closely related to

the respondents experience? 5. Is the question content sufficiently general and free from superiors concreteness

and specificity? 6. Is the question content biased or loaded in one direction without accompanying

questions to balance the emphasis?

D. Question Types

Researchers use three basic types of questions: multiple choice, numeric open end and text open end (sometimes called "verbatim"). Examples of each kind of question follow:

Multiple choice Question

1. Where do you live? (1) Northern Region (2) Central Region (3) Eastern region (4) Western region (5) Southern region

2. Numeric Open End Question How much did you spend on fruits last week? ------------- 3. Text Open End How can your company improve its working conditions? --------------------------------------------------------------------------------- Rating Scales and Agreement Scales are two common types of questions. Rating scale Example How would you rate this Product? 1. Excellent 2. Very good 3. Good 4. Fair 5. Poor On a scale where 10 means you have a great amount of interest in a subject and 1 means you have none at all, how would you rate your interest in each of the following topics?

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1. New economic policy 2. SEZ policy 3. Corporate social responsibility 4. Labour Market reforms

Agreement scale Example How much do you agree with each of the following statements: Sl. No.

Statement Strongly agree

agree Agree somewhat

disagree Strongly disagree

1 My manager provides constructive criticism

2 Our medical plan provides adequate coverage

3 Globalization has benefited the Indian Economy

4 Rural-urban disparities has increased in the post-reform period.

E. Qualities of a Good Question The qualities of a good question are as follows:

1. Evokes the truth. Questions must be non-threatening. Anonymous questionnaires that contain no identifying information are more likely to produce honest responses than those identifying the respondent.

2. Asks for an answer on only one dimension. For example, a researcher investigating a new food snack asks "Do you like the texture and flavor of the snack?" If a respondent answers "no", then the researcher will not know if the respondent dislikes the texture or the flavor, or both. Another questionnaire asks, "Were you satisfied with the quality of our food and service?"

3. Can accommodate all possible answers. Multiple choice items are the most popular type of survey questions because they are generally the easiest for a respondent to answer and the easiest to analyze. For example, consider the question:

What brand of computer do you own? __ A. IBM PC B. Apple Clearly, there are many problems with this question. What if the respondent doesn't own a microcomputer? What if he owns a different brand of computer? What if he owns both an IBM PC and an Apple? There are two ways to correct this kind of problem. The first way is to make each response a separate dichotomous item on the questionnaire. For example: Do you own an IBM PC? (circle: Yes or No)

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Do you own an Apple computer? (circle: Yes or No) Another way to correct the problem is to add the necessary response categories and allow multiple responses. This is the preferable method because it provides more information than the previous method. What brand of computer do you own? (Check all that apply) __ Do not own a computer __ IBM PC __ Apple __ Other 4. Has Mutually exclusive options. A good question leaves no ambiguity in the mind of the respondent. There should be only one correct or appropriate choice for the respondent to make 5. Produces variability of responses. When a question produces no variability in responses, we are left with considerable uncertainty about why we asked the question and what we learned from the information. If a question does not produce variability in responses, it will not be possible to perform any statistical analyses on the item. For example:

What do you think about this report? __ A. It's the worst report I've read B. It's somewhere between the worst and best C. It's the best report I've read Since almost all responses would be choice B, very little information is learned. 6. Does not presuppose a certain state of affairs. Among the most subtle mistakes in questionnaire design are questions that make an unwarranted assumption. An example of this type of mistake is: Are you satisfied with your current auto insurance? (Yes or No) This question will present a problem for someone who does not currently have auto insurance. Write your questions so they apply to everyone. One of the most common mistaken assumptions is that the respondent knows the correct answer to the question. Industry surveys often contain very specific questions that the respondent may not know the answer to. For example: What percent of your budget do you spend on direct mail advertising? ____ 7. Does not imply a desired answer. The wording of a question is extremely important. As examples: Don't you think most of the politicians are corrupt?

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8. Does not use emotionally loaded or vaguely defined words. Quantifying adjectives (e.g., most, least, majority) are frequently used in questions. It is important to understand that these adjectives mean different things to different people. E. Question Sequence Items of a questionnaire should be grouped into logically coherent sections. Grouping questions that are similar will make the questionnaire easier to complete, and the respondent will feel more comfortable. Questions that use the same response formats, or those that cover a specific topic, should appear together. Each question should follow comfortably from the previous question. Writing a questionnaire is similar to writing anything else. Transitions between questions should be smooth. Questionnaires that jump from one unrelated topic to another feel disjointed and are not likely to produce high response rates. Some researchers have suggested that it may be necessary to present general questions before specific ones in order to avoid response contamination. Other researchers have reported that when specific questions were asked before general questions, respondents tended to exhibit greater interest in the general questions.

The numbering of questions should be in a logical sequence. To check the sequence of questions the following questions should be answered. 1. Are the answers to the questions likely to be influenced by the content of the

preceding questions? 2. Are the questions led up to in a natural way? 3. Do some questions come too early or too late from the point of view of arousing

interest and receiving sufficient attention, avoiding resistance and inhabitations?

E. Commandments for Construction of Good Questionnaire D.C. Miller provides a guide to the questionnaire construction: 1. Keep the language pitched to the level of respondent. 2. Try to pick words that have the same meaning for every one. 3. Avoid long questions 4. Do not have a priori assumption that your respondent possesses factual information

or first hand opinions. 5. Establish the frame of reference you have in mind. 6. In informing a question either suggest all possible alternatives or do not suggest

any. 7. Protect your respondents ego. 8. If you are after unpleasant orientations, give your respondent a chance to express

his positive feeling first so that he is not put in an unfavourable light. 9. Decide whether you need a direct question, an indirect question, or an indirect

followed by a direct question. 10. Decide whether the question should be open or closed. 11. Decide whether general or specific questions are needed. 12. Avoid ambiguous wording. 13. Avoid biased questions. 14. Phrase questions so that they are not unnecessarily objectionable.

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15. Decide whether a personal or impersonal question will obtain the better response. 16. Questions should be limited to a single idea or a single reference.

Pre-test the Questionnaire The last step in questionnaire design is to test a questionnaire with a small number of interviews before conducting your main interviews. Ideally, you should test the survey on the same kinds of people you will include in the main study. Pre-tests and Pilot study are the essence of a good questionnaire. It enables the investigator to identify the mistakes and unwarranted and undesirable trends that might have crept into the questionnaire. It helps in enriching the design of the questionnaire and assists in testing the validity and reliability of statistical techniques to be adopted for data processing and analysis.

Questionnaire for Interviewers/Investigator After making a pre-test of a questionnaire, a questionnaire for interviewer should be constructed for getting relevant information so that the mistakes or inconsistency observed in the questionnaire may be removed. 1. Did any of the questions seem to make respondent uncomfortable? 2. Did you have to repeat any questions? 3. Did respondent misinterpret any questions? 4. Which questions were the most difficult or awkward for you to read? Have you

come to dislike any specific questions? Why? 5. Did any of the sections seem to drag? 6. Were there any sections in which you felt that the respondent would have liked the

opportunity to say more? and so on. After finalising the questionnaire/schedule by correcting it on the basis of pre-testing, the investigator has to collect data from the field. The following points should be taken into account by the investigator while collecting data through questionnaire/schedule. 1. He must plan in advance and should fully know the problem under consideration.

He must choose a suitable time and place so that respondent should be ease during interview.

2. All possible efforts should be made to establish proper rapport with the informant; people are motivated to communicate when the atmosphere is favourable.

3. He must know that ability to listen with understanding, respect and curiosity is the gateway to communication, and hence acts accordingly during the survey.

4. Investigators approach must be friendly and informal. Initially friendly greetings in accordance with the cultural pattern of the respondent should be exchanged and then the purpose of the survey should be explained.

5. To the extent possible, there should be a free-flow interview and the questions must be well phrased in order to have full cooperation of the respondent.

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7. PARAMETRIC AND NON-PARAMETRIC STATISTICAL TESTS

Testing of Hypothesis- developed by Neyman and Pearson- employs statistical tools to arrive at a decision in certain situations where there is element of uncertainty. In test of Hypothesis we see whether there is significant difference between parameters and Statistic, parameter and parameter and statistic and statistic. Inference about population on basis of sample may be pertaining to certain Hypothesis. Hypothesis: A Hypothesis is an assumption or a theoretical proposition that is capable of empirical verification or disproof. It may or may not be true. Statistical Hypothesis is an assertion about Probability distribution of one or more random variables. It can be simple, if it completely specifies the probability distribution of population. or Complex or composite if it does not completely specifies the Probability distribution of population. Test of Significance: The procedure to access the significance of the difference between a sample statistics and corresponding population parameter or difference between two independent statistics is called test of significance. Example Agronomist wants to establish from his research experiment data if the average yield of new variety has some specific value or not. Or whether the yield of two varieties of wheat is same or not. Question is how to arrive at the conclusion whether difference is real (significant) or due to chance (called non-significant) and how large difference is to be considered statistically significant.

Hypotheses are of two types Null Hypothesis (H0) and Alternate Hypothesis (H1) Decision-maker should always logically adopt a neutral or null attitude towards the outcome of experiment. Null Hypothesis is a statistical Hypothesis (set by statistician as a judge) of no difference and it is tested for its possible rejection under the assumption that it is true. Null hypothesis shall be rejected or shall not be rejected at certain level of significance . Null hypothesis should never be accepted on the basis of one sample statistic Alternate Hypothesis: Set by Experimental. The Hypothesis representing the opposite of the null hypothesis is called alternate hypothesis. It is any statistical hypothesis, which is complementary to null hypothesis Ex. If we are to test whether the average per capita of two states differ significantly or not, The null hypothesis will be H0: a= b ( i.e. PCI of states A & B do not differ significantly)

Alternate Hypothesis 1. H1: a = b (Two Tailed alternate) 2. H1: a > b ( one Tailed ; Right tailed alternate) 3. H1: a < b ( one Tailed ;Left tailed alternate)

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Significance Level: The significance is the probability with which null hypothesis will be rejected due to sampling error though it is true. Decision to reject or accept null hypothesis depends upon the information contained in the sample and there is always a risk of taking wrong decision. One is likely to commit two types of errors. TYPE I Error: The error of rejecting null Hypothesis on the basis of information contained in the sample when actually it is true is called Type-I error ( probability of rejecting null Hypothesis when it is true) It is denoted by (In quality control it is called producers risk because it is probability of rejecting a good lot). Probability of committing type I error is called Level Significance = Level of Significance = Probability of rejecting H0 when it is true. Type-II Error: It is the probability of accepting the null hypothesis when it is false. Also called consumers risk because it is prob of accepting bad lot. It is denoted by . Hypothesis H0 and Hi are mutually exclusive events. i.e. if H0 is accepted (rejected) then H1 is rejected (accepted).

Power of Test: The probability of accepting null Hypothesis on the basis of sample information when null hypo is true is called Power of a test.

Therefore Power of Test: Prob. (Accept H0 when H0 is true)

Prob.(Accept H0 when true) + Prob. ( Accept H0 when H0 is false) = 1

Prob (Accept H0 when true)+ = 1 Therefore Power of test = Prob(Accept H0 when true) = 1- So test will be more powerful when error is small.

Sample Space: Pop size= N, Random sample size drawn= n and possible samples are k=Ncn Suppose some statistic t is computed from each of the samples. t =f(x1,x2,x3,-----xn) Possible sample statistic are t1,t2,t3-----tk constitute sample space. It is used to test null hypothesis. Some will lead to rejection of Ho other may lead to acceptance of H0. Thus sample space of statistic is divided into two disjoint and exhaustive sets. Critical Region (W) : It is part of sample space which leads to rejection of null hypothesis if given sample statistic fall in this region. Acceptance Region: It is that part of sample space, which leads to acceptance of null hypothesis, if sample statistic falls in it. Critical Point: The point in sample space which divides the sample space in two mutually disjoint and exhaustive sets is known as critical Point. The critical points are tabulated values for different sampling distributions. Form of Sample Space is determined by different sampling distribution like t, F, 2,Z etc. Sampling Distribution: Sampling distribution is Probability distribution of a statistic.

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Two tailed and one tailed tests: A two tailed test rejects the null hypothesis if, say, the sample mean is significantly higher or lower than the hypothesized value of the mean of the population. Such a test is appropriate when the null hypothesis is some specified value and the alternative hypothesis is a value not equal to the specified value of the null hypothesis.

Symbolically, the two tailed test is appropriate when we have H0: = 0 and Hi : : 0 which may mean >0 or 0 then it is one tail test, which is known as right tailed test. (Where there is only one rejection region either on the left tail or right tail) Tests of Hypotheses: Tests of hypotheses (also known as tests of significance) can be classified as: 1. Parametric Tests 2. Non-parametric Tests Parametric Tests: Parametric tests usually assume certain properties of the parent population from which sample is drawn. Assumptions like observations come from a normal population, sample size is large, assumptions about the population parameters like mean, variance etc. must hold good before parametric tests can be used. Probability distribution of statistic (sampling distribution) is known i.e. it follows particular distribution like t, F, Z etc. Parametric tests cannot be applied if nature of parent population is unknown and data is measured on nominal/ ordering scale. The important parametric tests are: z-test, t-test, 2- test and F-test. (2-test is also used as a test of goodness of fit and also as a test of independence in which case it is a non-parametric test.) All these test are based on the assumption of normality i.e. the source of data is considered to be normally distributed. Z-test: it is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean. The relevant test statistic, Z, is worked out and compared with its probable value at specified level of significance for judging the significance if the measure concerned. As n becomes large Z-test is generally used even when binomial distribution or t-distribution is applicable on the presumption that such a distribution tends to approximate normal distribution. Z- test is used for comparing the mean for the population, when pop. variance is known, for judging the significance of difference between means of two independent samples when pop. variance is known, for comparing the sample proportion to a theoretical value of population or for judging the difference in proportions of two independent samples when n happens to be large. This test may be used for judging the significance of median, mode, coefficient of correlation and several other measures. t-test: t-test is based on t-distribution and is considered an appropriate test for judging the significance of a sample mean or for difference between means of two samples in case of

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small sample (s) when pop. variance is not known (then sample variance is used for pop variance.). In case two samples are related, we use paired t-test (difference test) for judging the significance of mean of difference between two related samples. Also used for testing the significance of the coefficient of simple and partial correlations. The relevant test statistic t is calculated from the sample data and then compared with its probable value based on t-distribution to read from table at different level of significance and degree of freedom for accepting or rejecting the hypothesis. 2-test: It is based on chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance. 2 = (Xi- X)2/2 = (n-1) S2/2 with n-1 d. f. F-test: F-test is based on F-distribution and is used to compare the variance of the two independent samples. This test is also used in the context of analysis of variance (ANOVA) for judging the significance of more than two sample means at one and the same time. It is also used for judging the significance of multiple correlation coefficients. Test statistic, F, is calculated and compared with its probable value for accepting or rejecting the null hypothesis. (we use F-ratio Table for certain d.f. at certain level of significance) Non-Parametric Tests: The tests which are used when practical data may be non normal and /or it may not be possible to estimate the parameter(s) of the data are called non-parametric tests. Since these tests are based on the data, which are free from distribution and parameter, these tests are called non-parametric tests or distribution free tests. The non-parametric tests can be used for nominal data (qualitative data, like greater or less etc.) and ordinal data, like ranked data. These tests require less calculation, because there is no need to compute parameters. Also these tests can be applied to very small samples, more specifically during pilot studies in market research. Inference about the population can be made by the non-parametric tests when assumptions of the standard methods cannot be satisfied since the non-parametric tests involve no or less restricting assumptions when compared to the parametric tests. Main non-parametric tests are 1. One-sample tests a. one sample sign tests b. Chi-square test c. Kolmogorov-Smirnov test d. Run test for randomness 2. Two- Sample tests a. Two-sample sign test b. Median test c. Mann-Whitney U test (Rank sum test) 3. K-sample test a. Median test b. Kruskal- wallis test (H test) c. Kendalls coefficient of concordance test

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8. HOW TO WRITE RESEARCH PROPOSAL

ROLE OF RESEARCH IN UNIVERSITIES

Leverage funds to expand facilities Accord international recognition Support staff training Enable Universities participate in community service Enable academic staff to achieve promotion Generates new knowledge for National growth and Development Two main purposes: (i) to get a degree and (ii) conduct sponsored and Consultancy

research projects. With increasing privatization of higher education and shrinking public grants,

greater stress of Academic Institutions is on generating their own resources. Academic institutions need faculty capable of doing independent sponsored and

consultancy research projectslead the research team A good research proposal (RP) is not only necessary for a high quality of research

but also for getting grant from the funding agencies A RP must be convincing to anonymous experts who examine it and see whether it

is methodologically sound, conceptually clear and would make significant contribution to the knowledge on the subject.

As large No. of RP submitted to the funding agencies for financial assistance, your proposal need to be excellent and not just very good for getting approved for the grant.

TWO MAIN TYPES OF FUNDED RESEARCH

1. Research you really want to do: Find sponsor! --CSIR, MHRD, UGC, DST, UNDP, Foundations, NGOs, World Bank, DFID, Ministries

2. Topics some sponsor wants to see done: Industries, organizations, Ministries, market surveys, evaluation studies, R&D