Research Methodology

Assignment Set 1

Q1. Why should a manger know about research when the job entails managing people, products, events, environments, and the like?

Ans:-

The manager, while managing people, products, events, and environments will

invariably face problems, big and small, and will have to seek ways to find long

lasting effective solutions. This can be achieved only through knowledge of

research even if consultants are engaged to solve problems.

The primary purpose for applied research (as opposed to basic research) is

discovering, interpreting, and the development of methods and systems for the

advancement of human knowledge on a wide variety of scientific matters of our

world and the universe. Research can use the scientific method, but need not do

so. The goal of the research process is to produce new nowledge, which takes

three main forms (although, as previously discussed, the boundaries between

them may be fuzzy):

Exploratory research, which structures and identifies new problems

Constructive research, which develops solutions to a problem

Empirical research, which tests the feasibility of a solution using empirical

evidence. The research room at the New York Public Library, an example of

secondary research in progress. Research can also fall into two distinct types:

1) Primary research

2) Secondary research

In social sciences and later in other disciplines, the following two research

methods can be applied, depending on the properties of the subject matter and

on the objective of the research:

Qualitative research

Quantitative research

Research is often conducted using the hourglass model Structure of Research.

The hourglass model starts with a broad spectrum for research, focusing in on the

required information through the methodology of the project (like the neck of the

hourglass), then expands the research in the form of discussion and results.

Research and development is nowadays of great importance in business as the

level of competition, production processes and methods are rapidly increasing. It

is of special importance in the field of marketing where companies keep an eagle

eye on competitors and customers in order to keep pace with modern trends and

analyze the needs, demands and desires of their customers.

Unfortunately, research and development are very difficult to manage, since the

defining feature of research is that the researchers do not know in advance

exactly how to accomplish the desired result. As a result, higher R&D spending

does not guarantee "more creativity, higher profit or a greater market share.

Q 2. a. How do you evolve research design for exploratory research? Briefly analyze.

Ans:-

The central purpose is to formulate hypotheses regarding potential problems and

opportunities present in the decision situation. The hypotheses can be tested at a

later phase with a conclusive research design (Leinhardt and Leinhardt, 1980).

Exploratory research design applies when the research objectives include the

following:

a. identifying problems (threats or opportunities)

b. developing a more precise formulation of a vaguely identified problem(threat or

opportunity)

c. gaining perspective regarding the breath of variables operating in a situation

d. establishing priorities regarding the potential significance of various problems

(threats or opportunities)

e. gaining management and researcher perspective concerning the character of

the problem situation

f. identifying and formulating alternative courses of action; and

g. gathering information on the problems associated with doing conclusive

research.

h. identification of problems (threats or opportunities) can be assisted through the

following:

i) Searching secondary sources

ii) Interviewing knowledgeable persons

iii) Compiling case histories.

Q 2 b. Briefly explain Independent, dependent and extraneous variables in a research design.

Ans:-

 Independent Variable:

A variable that you believe might influence your outcome measure. This might be

a variable that you control, like a treatment, or a variable not under your control,

like an exposure. It also might represent a demographic factor like age or gender.

Contrast this with the definition of the dependent variable. An independent

variable is a hypothesized cause or influence on a dependent variable.

One way to distinguish these variables is to ask yourself what you are want to

learn from this research. The dependent variable is a variable you are trying to

predict. Any variable that you are using to make those predictions is an

independent variable. A recently published research study examined the

relationship of dietary fat consumption and the development of ischemic

stroke in a cohort of 832 men who were free of cardiovascular disease at

baseline (1966-1969) and who were followed for a twenty year period. In this

study, the independent variables were:

percentage of total fat in the diet,

percentage of saturated fat, and

the percentage of monounsaturated fat.

Dependent variable:

In a research study, the variable that you believe might be influenced or modified

by some treatment or exposure. It may also represent the variable you are trying

to predict. Contrast this with the definition of an independent variable. Sometimes

the dependent variable is called the outcome variable. This definition depends on

the context of the study. In a study of prenatal care, the birthweight is an

outcome or dependent variable, but in neonatology, it is more likely to be an

independent variable. A recently published research study examined the

relationship of dietary fat consumption and the development of ischemic

stroke in a cohort of 832 men who were free of cardiovascular disease at

baseline (1966-1969) and who were followed for a twenty year period. In this

study, the dependent variable was:

incidence of ischemic stroke.

Extraneous variable :

The independent variables which are not directly related to

the purpose of the study but affect the dependent variable are known as

extraneous variables. For eg, if a researcher wants to test the hypothesis that

there is relationship between children’s school performance and their self-

concepts, in which case the latter is an independent variable and the former is

the dependent variable. In this context, intelligence may also influence the school

performance. However, since it is not directly related to the purpose of

the study undertaken by the researcher, it would be known as extraneous

variable. The influence caused by the extraneous variable on the dependent

variable is technically called as an ‘experimental error’. Therefore, a research

study should always be framed in such a manner that the dependent variable

completely influences the change in the independent variable and any other

extraneous variable or variables.

Q 3. a. Differentiate between ‘Census survey’ and ‘ Sample Survey’.

Ans:

Difference between Census and Sampling

Practically every country in the world conducts censuses and sampling surveys on

a regular basis in order to get valuable data from and about their populations.

This data is used by the federal and state governments in making numerous

decisions with regard to various health care, housing, and educational issues,

among others. While both these two data-gathering methods essentially serve the

same purpose, they have a number of differences with regard to approach and

methodology, as well as scope. These two methods may also differ in terms of the

variance in the data gathered, as you will see later.

Scope

A census involves the gathering of information from every person in a certain

group. This may include information on age, sex and language among others. A

sample survey on the other hand commonly involves gathering data from only a

certain section of a particular group.

Sampling Variance

The main advantage of a census is a virtually zero sampling variance, mainly

because the data used is drawn from the whole population. In addition, more

precise detail can generally be gathered about smaller groups of the population.

As for sampling, there is a possibility of sampling variance, since the data used is

drawn from only a small section of the population. This makes sampling a much

less accurate form of data collection than a census. In addition, the sample may

be too small to provide an accurate picture of the population.

Cost And Timetable

A census can be quite expensive to conduct, particularly for large populations. In

most cases, they are also a lot more time-consuming than sample surveys.

Adding considerably

to the timetable is the necessity of gathering data from every single member of

the population. The huge scope of a census also makes it harder to maintain

control of the quality of the data. For instance, anyone who does not complete a

census form will be visited by a government representative who’s only job to is to

gather census data.

A sample survey for its part costs quite a bit less than a census, since data is

gathered from a much smaller group of people. In addition, sample surveys

generally take a much shorter time to conduct, again given the smaller scope.

This also means reduced requirements for respondents, which in turn leads to

better data monitoring and quality control.

Summary

Census Gathers information from every individual in a certain group

Since data from the entire population is used, there is no sampling

variance

Provides detailed information about smaller groups

Can be quite costly, particularly for large populations, due to census tally

workers as well as hiring temporary census home visitors

Includes an uncomfortable visit from a government worker if the census is

not filled out on time

Sampling Gathers information from only a section of the population

May have a significant degree of sample variance, since the data is derived

from only a small section of a population

May not provide enough information about smaller groups or smaller

geographical sections of a place

Costs much less than a census, since data is gathered from only a small

section of a group

Q 3. b. Analyze multi-stage and sequential sampling.

Ans:-

Multistage sampling

Multistage sampling is a complex form of cluster sampling. Using all the sample

elements in all the selected clusters may be prohibitively expensive or not

necessary. Under these circumstances, multistage cluster sampling becomes

useful. Instead of using all the elements contained in the selected clusters, the

researcher randomly selects elements from each cluster. Constructing the

clusters is the first stage. Deciding what elements within the cluster to use is the

second stage. The technique is used frequently when a complete list of all

members of the population does not exist and is inappropriate.

In some cases, several levels of cluster selection may be applied before the final

sample elements are reached. For example, household surveys conducted by the

Australian Bureau of Statistics begin by dividing metropolitan regions into

'collection districts', and selecting some of these collection districts (first stage).

The selected collection districts are then divided into blocks, and blocks are

chosen from within each selected collection district (second stage). Next,

dwellings are listed within each selected block, and some of these dwellings are

selected (third stage). This method means that it is not necessary to create a list

of every dwelling in the region, only for selected blocks. In remote areas, an

additional stage of clustering is used, in order to reduce travel requirements.[1]

Although cluster sampling and stratified sampling bear some superficial

similarities, they are substantially different. In stratified sampling, a random

sample is drawn from all the strata, where in cluster sampling only the selected

clusters are studied, either in single stage or multi stage.

Sequential sampling

Sequential sampling is a non-probability sampling technique wherein the

researcher picks a single or a group of subjects in a given time interval, conducts

his study, analyzes the results then picks another group of subjects if needed and

so on.

This sampling technique gives the researcher limitless chances of fine tuning his

research methods and gaining a vital insight into the study that he is currently

pursuing.

Q 4. List down various measures of central tendency and explain the difference between them?

Ans:-

Arithmetic Mean

The arithmetic mean is the most common measure of central tendency. It simply

the sum of the numbers divided by the number of numbers. The symbol m is

used for the mean of a population. The symbol M is used for the mean of a

sample. The formula for m is shown below:

where ΣX is the sum of all the numbers in the numbers in the sample and N is

the number of numbers in the sample. As an example, the mean of the numbers

regardless of whether the numbers constitute the entire population or just a

sample from the population.

The table, Number of touchdown passes, shows the number of touchdown

(TD) passes thrown by each of the 31 teams in the National Football League in the

2000 season. The mean number of touchdown passes thrown is 20.4516 as

shown below.

Although the arithmetic mean is not the only "mean" (there is also a geometic

mean), it is by far the most commonly used. Therefore, if the term "mean" is used

without specifying whether it is the arithmetic mean, the geometic mean, or some

other mean, it is assumed to refer to the arithmetic mean.

Median

The median is also a frequently used measure of central tendency. The median is

the midpoint of a distribution: the same numbers of scores are above the median

as below it. For the data in the table, Number of touchdown passes, there are

31 scores. The 16th highest score (which equals 20) is the median because there

are 15 scores below the 16th score and 15 scores above the 16th score. The

median can also be thought

of as the 50th percentile.

Let's return to the made up example of the quiz on which you made a three

discussed previously in the module Introduction to Central Tendency and

shown in Table 2.

For Dataset 1, the median is three, the same as your score. For Dataset 2, the

median is 4. Therefore, your score is below the median. This means you are in

the lower half of the class. Finally for Dataset 3, the median is 2. For this dataset,

your score is above the median and therefore in the upper half of the distribution.

Computation of the Median: When there is an odd number of numbers, the

median is simply the middle number. For example, the median of 2, 4, and 7 is 4.

When there is an even number of numbers, the median is the mean of the two

middle numbers. Thus, the median of the numbers

Mode

The mode is the most frequently occuring value.For the data in the table,

Number of touchdown passes, the mode is 18 since more teams (4) had 18

touchdown passes than any other number of touchdown passes. With continuous

data such as

response time measured to many decimals, the frequency of each value is one

since no two scores will be exactly the same. Therefore the mode of continuous

data is normally computed from a grouped frequency distribution. The Grouped

frequency distribution table shows a grouped frequency distribution for the

target response time data. Since the interval with the highest frequency is 600-

700, the mode is

the middle of that interval (650).

Q.5. Select any topic for research and explain how you will use both secondary and primary sources to gather the required information.

Ans:

Primary Sources of Data

Primary sources are original sources from which the researcher directly collects

data that has not been previously collected, e.g., collection of data directly by the

researcher on brand awareness, brand preference, and brand loyalty and other

aspects of consumer behavior, from a sample of consumers by interviewing them.

Primary data is first hand information collected through various methods such as

surveys, experiments and observation, for the purposes of the project

immediately at hand.

The advantages of primary data are –

It is unique to a particular research study

It is recent information, unlike published information that is already

available

The disadvantages are –

It is expensive to collect, compared to gathering information from

available sources

Data collection is a time consuming process

It requires trained interviewers and investigators

2 Secondary Sources of Data

These are sources containing data, which has been collected and compiled for

another purpose. Secondary sources may be internal sources, such as annual

reports, financial statements, sales reports, inventory records, minutes of

meetings and other information that is available within the firm, in the form of a

marketing information system. They may also be external sources, such as

government agencies (e.g. census reports, reports of government departments),

published sources (annual reports of currency and finance published by the

Reserve Bank of India, publications of international organizations such as the UN,

World Bank and International Monetary Fund, trade and financial journals, etc.),

trade associations (e.g. Chambers of Commerce) and commercial services

(outside suppliers of information).

Methods of Data Collection:

The researcher directly collects primary data from its original sources. In this

case, the researcher can collect the required data precisely according to his

research needs and he can collect them when he wants and in the form that he

needs it. But the collection of primary data is costly and time consuming. Yet, for

several types of social science research, required data is not available from

secondary sources and it has to be directly gathered from the primary sources.

Primary data has to be gathered in cases where the available data is

inappropriate, inadequate or obsolete. It includes: socio economic surveys, social

anthropological studies of rural communities and tribal communities, sociological

studies of social problems and social institutions, marketing research, leadership

studies, opinion polls, attitudinal surveys, radio listening and T.V. viewing surveys,

knowledge-awareness practice (KAP) studies, farm management studies, business

management studies etc.

There are various methods of primary data collection, including surveys, audits

and panels, observation and experiments.

1. Survey Research

A survey is a fact-finding study. It is a method of research involving collection of

data directly from a population or a sample at a particular time. A survey has

certain characteristics:

It is always conducted in a natural setting. It is a field study.

It seeks responses directly from the respondents.

It can cover a very large population.

It may include an extensive study or an intensive study

It covers a definite geographical area.

A survey involves the following steps -

Selection of a problem and its formulation

Preparation of the research design

Operation concepts and construction of measuring indexes and scales

Sampling

Construction of tools for data collection

Field work and collection of data

Processing of data and tabulation

Analysis of data

Reporting

There are four basic survey methods, which include:

Personal interview

Telephone interview

Mail survey and

Fax survey

Personal Interview

Personal interviewing is one of the prominent methods of data collection. It may

be defined as a two-way systematic conversation between an investigator and an

informant, initiated for obtaining information relevant to a specific study. It

involves not only conversation, but also learning from the respondent’s gestures,

facial expressions and pauses, and his environment.

Interviewing may be used either as a main method or as a supplementary one in

studies of persons. Interviewing is the only suitable method for gathering

information from illiterate or less educated respondents. It is useful for collecting

a wide range of data, from factual demographic data to highly personal and

intimate information relating to a person’s opinions, attitudes, values, beliefs,

experiences and future intentions. Interviewing is appropriate when qualitative

information is required, or probing is necessary to draw out the respondent fully.

Where the area covered for the survey is compact, or when a sufficient number of

qualified interviewers are available, personal interview is feasible.

Interview is often superior to other data-gathering methods. People are usually

more willing to talk than to write. Once rapport is established, even confidential

information may be obtained. It permits probing into the context and reasons for

answers to questions.

Interview can add flesh to statistical information. It enables the investigator to

grasp the behavioral context of the data furnished by the respondents. It permits

the investigator to seek clarifications and brings to the forefront those questions,

which for some reason or the other the respondents do not want to answer.

Interviewing as a method of data collection has certain characteristics. They are:

1. The participants – the interviewer and the respondent – are strangers;

hence, the investigator has to get himself/herself introduced to the

respondent in an appropriate manner.

2. The relationship between the participants is a transitory one. It has a

fixed beginning and termination points. The interview proper is a

fleeting, momentary experience for them.

3. The interview is not a mere casual conversational exchange, but a

conversation with a specific purpose, viz., obtaining information

relevant to a study.

4. The interview is a mode of obtaining verbal answers to questions put

verbally.

5. The interaction between the interviewer and the respondent need not

necessarily be on a face-to-face basis, because the interview can also

be conducted over the telephone.

6. Although the interview is usually a conversation between two persons,

it need not be limited to a single respondent. It can also be conducted

with a group of persons, such as family members, or a group of

children, or a group of customers, depending on the requirements of

the study.

7. The interview is an interactive process. The interaction between the

interviewer and the respondent depends upon how they perceive each

other.

8. The respondent reacts to the interviewer’s appearance, behavior,

gestures, facial expression and intonation, his perception of the thrust

of the questions and his own personal needs. As far as possible, the

interviewer should try to be closer to the social-economic level of the

respondents.

9. The investigator records information furnished by the respondent in the

interview. This poses a problem of seeing that recording does not

interfere with the tempo of conversation.

10. Interviewing is not a standardized process like that of a chemical

technician; it is rather a flexible, psychological process.

3 Telephone Interviewing Telephone interviewing is a non-personal method of

data collection. It may be used as a major method or as a supplementary method.

It will be useful in the following situations:

11. When the universe is composed of those persons whose

names are listed in telephone directories, e.g. business houses,

business executives, doctors and other professionals.

12. When the study requires responses to five or six simple

questions, e.g. a radio or television program survey.

13. When the survey must be conducted in a very short period

of time, provided the units of study are listed in the telephone

directory.

14. When the subject is interesting or important to respondents,

e.g. a survey relating to trade conducted by a trade association or a

chamber of commerce, a survey relating to a profession conducted by

the concerned professional association.

15. When the respondents are widely scattered and when there

are many call backs to make.

4. Group Interviews A group interview may be defined as a method of collecting

primary data in which a number of individuals with a common interest interact

with each other. In a personal interview, the flow of information is multi

dimensional. The group may consist of about six to eight individuals with a

common interest. The interviewer acts as the discussion leader. Free discussion is

encouraged on some aspect of the subject under study. The discussion leader

stimulates the group members to interact with each other. The desired

information may be obtained through self-administered questionnaire or

interview, with the discussion serving as a guide to ensure consideration of the

areas of concern. In particular, the interviewers look for evidence of common

elements of attitudes, beliefs, intentions and opinions among individuals in the

group. At the same time, he must be aware that a single comment by a member

can provide important insight. Samples for group interviews can be obtained

through schools, clubs and other organized groups.

5. Mail Survey The mail survey is another method of collecting primary data.

This method involves sending questionnaires to the respondents with a request to

complete them and return them by post. This can be used in the case of educated

respondents only. The mail questionnaires should be simple so that the

respondents can easily understand the questions and answer them. It should

preferably contain mostly closed-ended and multiple choice questions, so that it

could be completed within a few minutes. The distinctive feature of the mail

survey is that the questionnaire is self-administered by the respondents

themselves and the responses are recorded by them and not by the investigator,

as in the case of personal interview method. It does not involve face-to-face

conversation between the investigator and the respondent. Communication is

carried out only in writing and this requires more cooperation from the

respondents than verbal communication. The researcher should prepare a mailing

list of the selected respondents, by collecting the addresses from the telephone

directory of the association or organization to which they belong. The following

procedures should be followed –

a covering letter should accompany a copy of the questionnaire. It must

explain to the respondent the purpose of the study and the importance of his

cooperation to the success of the project.

Anonymity must be assured.

The sponsor’s identity may be revealed. However, when such information

may bias the result, it is not desirable to reveal it. In this case, a disguised

organization name may be used.

A self-addressed stamped envelope should be enclosed in the covering

letter.

After a few days from the date of mailing the questionnaires to the

respondents, the researcher can expect the return of completed ones from them.

The progress in return may be watched and at the appropriate stage, follow-up

efforts can be made.

The response rate in mail surveys is generally very low in developing countries

like India. Certain techniques have to be adopted to increase the response rate.

They are:

1. Quality printing: The questionnaire may be neatly printed on quality light

colored paper, so as to attract the attention of the respondent.

2. Covering letter: The covering letter should be couched in a pleasant style,

so as to attract and hold the interest of the respondent. It must anticipate

objections and answer them briefly. It is desirable to address the

respondent by name.

3. Advance information: Advance information can be provided to potential

respondents by a telephone call, or advance notice in the newsletter of the

concerned organization, or by a letter. Such preliminary contact with

potential respondents is more successful than follow-up efforts.

4. Incentives: Money, stamps for collection and other incentives are also

used to induce respondents to complete and return the mail questionnaire.

5. Follow-up-contacts: In the case of respondents belonging to an

organization, they may be approached through someone in that

organization known as the researcher.

6. Larger sample size: A larger sample may be drawn than the estimated

sample size. For example, if the required sample size is 1000, a sample of

1500 may be drawn. This may help the researcher to secure an effective

sample size closer to the required size.

Q 6. a. Explain the role of Graphs and Diagrams?

Ans:-

Roles of Graphs

Graph is a diagram, as a curve, broken line, or series of bars, representing various

kinds of quantitative information and relationships, such as the successive

changes in a variable quantity or quantities.

The graphs which are most commonly used in visual aids are Bar graphs, Pie

Charts, Line graphs and Scatter diagrams. Graphs play a very important role

during presentations because they make the data easier to understand and

interpretations and comparisons can be made quickly. They are useful in

presentations also because they can summarize large amounts of data and can

convey the basic idea of the research.

Graphs really help the audience in absorbing the data as they are simple to

interpret and are appealing. By using graphs, variations and trends in data can be

showed clearly and they show how the values of particular variables change over

time. Graphs also help in determining the relationship between variables.

A graph is an abstract data structure that is meant to implement the graph and

hypergraph concepts from mathematics.

A graph data structure consists of a finite (and possibly mutable) set of ordered

pairs, called edges or arcs, of certain entities called nodes or vertices. As in

mathematics, an edge (x,y) is said to point or go from x to y. The nodes may be

part of the graph structure, or may be external entities represented by integer

indices or references.

A graph data structure may also associate to each edge some edge value, such

as a symbolic label or a numeric attribute (cost, capacity, length, etc.).

Roles of Diagrams

Diagram is a graphic representation of an algebraic or geometric relationship.

Role Activity Diagrams (RADs) are a useful way of describing processes. They are

valuable in documenting processes as they are now, and as they might be in the

future. The main part of the SPRINT BPR Methodology describes the ways in which

Role Activity Diagrams can be used within a broader change project. This guide

tells you how to draw Role Activity Diagrams.

The guide will show you that Role Activity Diagrams are a reasonably simple

diagramming technique. It is not difficult to learn how to draw them and it is not

difficult for most people to interpret them.

The guide progresses by introducing each of the main constructs of the Role

Activity Diagram. It describes these in turn, giving advice about the drawing

conventions. This is syntactic guidance. In addition to these syntactic skills, the

creation of Role Activity Diagrams relies upon an ability to scope a study, to

decide on the level of interest and to determine the boundaries of each role in a

diagram. This is where the real skill of using Role Activity Diagrams comes in but

such issues are addressed only partially in this report. In reality the reader will

rely upon experience gained through using Role Activity Diagrams in projects.

This experience will teach how Role Activity Diagrams can best be used, and what

they are most useful for.

Q.6. b. What are the Types and General rules for graphical representation of data?

Ans:-

REPRESENTATION OF DATABesides the tabular form, the data may also be presented in some graphic or

diagrammatic form.

“The transformation of data through visual methods like graphs, diagrams, maps

and charts is called representation of data.”

The need of representing data graphically:

Graphics, such as maps, graphs and diagrams, are used to represent large

volume of data.

They are necessary:

If the information is presented in tabular form or in a descriptive record, it

becomes difficult to draw results.

Graphical form makes it possible to easily draw visual impressions of data.

The graphic method of the representation of data enhances our understanding.

It makes the comparisons easy.

Besides, such methods create an imprint on mind for a longer time.

It is a time consuming task to draw inferences about whatever is being

presented in non–graphical form.

It presents characteristics in a simplified way.

These makes it easy to understand the patterns of population growth,

distribution and the density, sex ratio, age–sex composition, occupational

structure, etc.

General Rules for Drawing Graphs, Diagrams and Maps

1. Selection of a Suitable Graphical Method

Each characteristic of the data can only be suitably represented by an appropriate

graphical

method. For example,

To show the data related to the temperature or growth of population between

different periods in time line graph are used.

Similarly, bar diagrams are used for showing rainfall or the production of

commodities.

The population distribution, both human and livestock, or the distribution of the

crop producing areas are shown by dot maps.

The population density can be shown by choropleth maps.

Thus, it is necessary and important to select suitable graphical method to

represent data.

2. Selection of Suitable Scale

Each diagram or map is drawn to a scale which is used to measure the data. The

scale must cover the entire data that is to be represented. The scale should

neither be too large nor too small.

S 3. Design

The diagram or map should have following design:

Title: The title of the diagram/map must be clear and include -

o The name of the area,

o Reference year of the data used and

o The caption of the diagram.

These are written with different font sizes and thickness. The title, subtitle and the

corresponding year is shown in the centre at the top of the map/diagram.

Legend or Index: The index must clearly explain the colours, shades, symbols

and signs used in the map and diagram. A legend is shown either at the lower left

or lower right side of the map sheet.

Direction the maps should show the direction North and properly placed on the

top.

Types of Diagrams

The diagrams and the maps is of following types:

(i) One-dimensional diagrams such as line graph, poly graph, bar diagram,

histogram, age, sex, pyramid, etc.;

(ii) Two-dimensional diagram such as pie diagram and rectangular diagram;

(iii) Three-dimensional diagrams such as cube and spherical diagrams.

The most commonly drawn diagrams and maps are:

• Line graphs

• Bar diagrams

• Pie diagram

• Wind rose and star diagram

• Flow Charts

1. Line Graph

The line graphs are usually drawn to represent the time series data related to the

temperature, rainfall, population growth, birth rates and the death rates.

Construction of a Line Graph

1st step: Round the data to be shown upto the 1 digit of even numbers.

2nd step: Draw X and Y-axis. Mark the time series variables (years/months) on

the X axis and the data quantity/value to be plotted on Y axis.

3rd step: Choose an appropriate scale to show data and label it on Y-axis. If the

data involves a negative figure then the selected scale should also show it.

4th step: Plot the data to depict year/month-wise values according to the

selected scale on Y-axis, mark the location of the plotted values by a dot and join

these dots by a free hand drawn

line.

☞ Example 1: Construct a line graph to represent the data

2. PolygraphPolygraph is a line graph in which two or more than two variables are shown on a

same diagram by different lines. It helps in comparing the data. Examples which

can be shown as

polygraph are:

The growth rate of different crops like rice, wheat, pulses in one diagram.

The birth rates, death rates and life expectancy in one diagram.

Sex ratio in different states or countries in one diagram.Construction of a

Polygraph.

All steps of construction of polygraph are similar to that of line graph. But

different lines are drawn to indicate different variables.

☞ Example 2: Construct a polygraph to compare the variables.

3. Bar Diagram

It is also called a columnar diagram. The bar diagrams are drawn through columns

of equal

width. Following rules were observed while constructing a bar diagram:

(a) The width of all the bars or columns is similar.

(b) All the bars should are placed on equal intervals/distance.

(c) Bars are shaded with colours or patterns to make them distinct and attractive.

Three types of bar diagrams are used to represent different data sets:

The simple bar diagram

Compound bar diagram

Polybar diagram.

Simple Bar Diagram

A simple bar diagram is constructed for an immediate comparison. It is advisable

to arrange

the given data set in an ascending or descending order and plot the data

variables

accordingly. However, time series data are represented according to the

sequencing of the

time period.

☞ Example 3: Construct a simple bar diagram.

Construction Steps:

Draw X and Y-axes on a graph paper. Take an interval and mark it on Y-axis to

plot data.

Divide X-axis into equal parts to draw bars. The actual values will be plotted

according to the

selected scale.

4. Line and Bar Graph

The line and bar graphs as drawn separately may also be combined to depict the

data related to some of the closely associated characteristics such as the climatic

data of mean monthly

temperatures and rainfall.

☞ Example 4: Construct a Line and bar Graph.

Construction:(a) Draw X and Y-axes of a suitable length and divide X-axis into parts to show

months in a year.

(b) Select a suitable scale with equal intervals on the Y-axis and label it at its right

side.

(c) Similarly, select a suitable scale with equal intervals on the Y-axis and label at

its left side.

(d) Plot data using line graph and columnar diagram.

5. Multiple Bar DiagramMultiple bar diagrams are constructed to represent two or more than two variables for the purpose of comparison. For example, a multiple bar diagram may be constructed to show proportion of males and females in the total, rural and urban population or the share of canal, tube well and well irrigation in the total irrigated area in different states.

(a) Mark time series data on X-axis and variable data on Y-axis as per the selected

scale.

(b) Plot the data in closed columns.

☞ Example 5: Construct a Multiple bar Diagram.

6. Compound Bar Diagram

When different components are grouped in one set of variable or different

variables of one component are put together, their representation is made by a

compound bar diagram. In this method, different variables are shown in a single

bar with different rectangles.

Construction

(a) Arrange the data in ascending or descending order.

(b) A single bar will depict the set of variables by dividing the total length of the

bar as per percentage.

☞ Example 6: Construct a Compound Bar Diagram.

7. Pie Diagram

Pie diagram is another graphical method of the representation of data. It is drawn

to depict

the total value of the given attribute using a circle. Dividing the circle into

corresponding

degrees of angle then represent the sub– sets of the data. Hence, it is also called

as Divided

Circle Diagram. The angle of each variable is calculated using the following

formulae.

If data is given in percentage form, the angles are calculated using the given formulae.

Calculation of Angles

(a) Arrange the data on percentages in an ascending order.

(b) Calculate the degrees of angles for showing the given values

(b) It could be done by multiplying percentage with a constant of 3.6 as derived

by dividing the total number of degrees in a circle by 100, i. e. 360/100.

(c) Plot the data by dividing the circle into the required number of divisions to

show the share different regions/countries Construction

(a) Select a suitable radius for the circle to be drawn. A radius of 3, 4 or 5 cm may

be chosen for the given data set.

(b) Draw a line from the centre of the circle to the arc as a radius.

(c) Measure the angles from the arc of the circle for each category of vehicles in

an ascending order clock-wise, starting with smaller angle.

(d) Complete the diagram by adding the title, sub – title, and the legend. The

legend mark be chosen for each variable/category and highlighted by distinct

shades/colours.

Precautions

(a) The circle should neither be too big to fit in the space nor too small to be

illegible.

(b) Starting with bigger angle will lead to accumulation of error leading to the plot

of the smaller angle difficult.

☞ Example 7: Construct a Pie Diagram.

8. Flow Maps/Chart

Flow chart is a combination of graph and map. It is drawn to show the flow of

commodities or people between the places of origin and destination. It is also

called as Dynamic Map.

Transport map, which shows number of passengers, vehicles, etc., is the best

example of a flow chart. These charts are drawn using lines of proportional width.

Many government agencies prepare flow maps to show density of the means of

transportation on different

routes.

The flow maps/ charts are generally drawn to represent two the types of data as

given below:

1. The number and frequency of the vehicles as per the direction of their

movement

2. The number of the passengers and/or the quantity of goods transported.

Requirements for the Preparation of a Flow Map:

(a) A route map depicting the desired transport routes along with the connecting

stations.

(b) The data pertaining No. of trains of selected routes of to the flow of goods,

Delhi and

adjoining areas services, number of vehicles, etc., along with the point of origin

and destination of the movements.

(c) The selection of a scale through which the data related to the quantity of

passengers and goods or the number of vehicles is to be represented.

Construction(a) Take an outline map of Delhi and adjoining areas in which railway line and the nodal stations are depicted.(b) Select a scale to represent the number of trains. Here, the maximum number is 50 and the minimum is 6. If we select a scale of 1cm = 50 trains, the maximum and minimum numbers will be represented by a strip of 10 mm and 1.2 mm thick lines respectively on the map.(c) Plot the thickness of each strip of route between the given rail route.(d) Draw a terraced scale as legend and choose distinct sign or symbol to show the nodalpoints (stations) within the strip.

☞ Example 8: Construct a Flow Map of Train movements in and around Delhi.

Thematic Maps

Varieties of maps are drawn to understand the patterns of the regional

distributions or the characteristics of variations over space these maps are known

as the distribution maps or thematic maps.

Requirements for Making a Thematic Map

(a) State/District level data about the selected theme.

(b) Outline map of the study area along with administrative boundaries.

(c) Physical map of the region. For example, physiographic map for population

distribution and relief and drainage map for constructing transportation map.

Rules for Making Thematic Maps

(i) The drawing of the thematic maps must be carefully planned. The final map

should properly reflect the following components:

a. Name of the area

b. Title of the subject-matter

c. Source of the data and year

d. Indication of symbols, signs, colours, shades, etc.

e. Scale

(ii) The selection of a suitable method to be used for thematic mapping.

Classification of Thematic Maps based on Method of Construction

The thematic maps are generally, classified into quantitative and non-quantitative

maps.

The quantitative maps are drawn to show the variations within the data. For

example, maps depicting areas receiving more than 200 cm, 100 to 200 cm, 50 to

100 cm and less than 50 cm of rainfall are referred as quantitative maps. These

maps are also called as statistical maps.

The non-quantitative maps, on the other hand, depict the non–measurable

characteristics in

the distribution of given information such as a map showing high and low rainfall-

receiving

areas. These maps are also called as qualitative maps.

The construction of quantitative maps: There are three types of quantitative maps

-

(a) Dot maps

(b) Choropleth maps

(c) Isopleth maps

9. Dot Maps

The dot maps are drawn to show the distribution of phenomena such as

population, cattle,

types of crops, etc. The dots of same size as per the chosen scale are marked

over the given

administrative units to highlight the patterns of distributions.

Requirement

(a) An administrative map of the given area showing state/district/block

boundaries.

(b) Statistical data on selected theme for the chosen administrative units, i.e.,

total

population, cattle etc.

(c) Selection of a scale to determine the value of a dot.

(d) Physiographic map of the region especially relief and drainage maps.

Precaution

(a) The lines demarcating the boundaries of various administrative units should

not be very thick and bold.

(b) All dots should be of same size.

Construction

(a) Select the size and value of a dot.

(b) Determine the number of dots in each state using the given scale. For

example, number of dots in Maharashtra will be 9,67,52,247/100,000 = 967.52. It

may be rounded to 968, as the

fraction is more than 0.5.

(c) Place the dots in each state as per the determined number in all states.

(d) Consult the physiographic/relief map of India to identify mountainous, desert,

and/or snow covered areas and mark lesser number of dots in such areas.

☞ Example 9: Construct a Dot Map.

10. Choropleth Map

The choropleth maps are also drawn to depict the data characteristics as they are

related to the administrative units. These maps are used to represent the density

of population,literacy/growth rates, sex-ratio, etc.

Requirement for drawing Choropleth Map

(a) A map of the area depicting different administrative units.

(b) Appropriate statistical data according to administrative units.

Steps to be followed

(a) Arrange the data in ascending or descending order.

(b) Group the data into 5 categories to represent very high, high, medium, low

and very low concentrations.

(c) The interval between the categories may be identified on the following

formulae i.e. Range/5 and Range = maximum value – minimum value.

(d) Patterns, shades or colour to be used to depict the chosen categories should

be marked in an increasing or decreasing order.

Construction

(a) Arrange the data in ascending order as shown above.

(b) Identify the range within the data. In the present case, the states recording

the lowest and highest literacy rates are Bihar (47%) and the Kerala (90.9%)

respectively. Hence, the range

would be 91.0 – 47.0 = 44.0

(c) Divide the range by 5 to get categories from very low to very high. (44.0/ 5 =

8.80). We can convert this value to a round number 9.0.

(d) Determine the number of the categories along with range of each category.

Add 9.0 to the lowest value of 47.0 as so on. We will finally get following

categories:

47 – 56 Very low (Bihar, Jharkhand, Arunachal Pradesh, Jammu and Kashmir)

56 – 65 Low (Uttar Pradesh, Rajasthan, Andhra Pradesh, Meghalaya, Orissa,

Assam,Madhya Pradesh, Chhattisgarh)

65 – 74 Medium (Nagaland, Karnataka, Haryana, West Bengal, Sikkim,

Gujarat,Punjab, Manipur, Uttaranchal, Tripura, Tamil Nadu)

74 – 83 High (Himachal Pradesh, Maharashtra, Delhi, Goa)

83 – 92 Very High (Mizoram, Kerala)

(e) Assign shades/pattern to each category ranging from lower to higher hues.

(f) Prepare the map as shown in Fig.

(g) Complete the map with respect to the attributes of map design.

☞ Example 10: Construct a Choropleth Map.

11. Isopleth Map

Variations in the degrees of slope, temperature, occurrence of rainfall, may be

represented by drawing the lines of equal values on a map. All such maps are

termed as Isopleth Map. The word Isopleth is derived from Iso meaning equal and

pleth means lines. Thus, an imaginary line, which joins the places of equal values,

is referred as Isopleth. The more frequently drawn isopleths include Isotherm

(equal temperature), Isobar (equal pressure), Isohyets (equal rainfall), Isonephs

(equal cloudiness), Isohels (equal sunshine), contours (equal heights),Isobaths

(equal depths), Isohaline (equal salinity), etc.

Requirement

(a) Base line map depicting point location of different places.

(b) Appropriate data of temperature, pressure, rainfall, etc. over a definite period

of time.

(c) Drawing instrument specially French Curve, etc.

Rules to be observed

(a) An equal interval of values be selected.

(b) Interval of 5, 10, or 20 is supposed to be ideal.

(c) The value of Isopleth should be written along the line on either side or in the

middle by

breaking the line.

Interpolation

Interpolation is used to find the intermediate values between the observed values

of at two

stations/locations. Generally, drawing of isopleths joining the places of same value

is also termed as interpolation.

Method of Interpolation: For interpolation, follow the following steps:

(a) Firstly, determine the minimum and maximum values given on the map.

(b) Calculate the range of value i.e. Range = maximum value – minimum value.

(c) Based on range, determine the interval in a whole number like 5, 10, 15, etc.

The exact point of drawing an Isopleth is determined by using the following

formulae.

The interval is the difference between the actual value on the map and

interpolated value. For example, in an Isotherm map of two places show 28º C

and 33º C and you want to draw 30ºC isotherm, measure the distance between

the two points. Suppose the distance is 1cm or 10 mm and the difference

between 28 and 33 is 5, thus, exact point of 30 will be plotted 4mm away from

28ºC or 6mm ahead of 33ºC.

☞ Example 11: Construct an Isopleth Map.

Research Methodology

Assignment Set 2

Question 1: What is questionnaire? Discuss the main points that you will take into account while drafting a questionnaire?

Answer:

A questionnaire is a research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. Although they are often designed for statistical analysis of the responses, this is not always the case. The questionnaire was invented by Sir Francis Galton.

Questionnaires have advantages over some other types of surveys in that they are cheap, do not require as much effort from the questioner as verbal or telephone surveys, and often have standardized answers that make it simple to compile data. However, such standardized answers may frustrate users. Questionnaires are also sharply limited by the fact that respondents must be able to read the questions and respond to them. Thus, for some demographic groups conducting a survey by questionnaire may not be practical.

As a type of survey, questionnaires also have many of the same problems relating to question construction and wording that exist in other types of opinion polls.Questionnaires may be classified as:

Structured/ Standardized Questionnaire:

Structured questionnaires are those in which there are definite, concrete and preordained questions with additional questions limited to those necessary to clarify inadequate answers or to elicit more detailed responses. The questions are presented with exactly the same wording and in the same order to all the respondents.

Unstructured Questionnaire: In unstructured questionnaires the respondent is given the opportunity to answer in his own terms and in his own frame of reference.

Points to take into account while drafting a questionnaire:

Writing an effective questionnaire is not a task for novices. At the very least it requires an understanding of four basics. These are:

Considering the differences that exist when writing a questionnaire that respondent’s will fill out themselves as opposed to when a professional interviewer administers the questionnaire to the respondent.

Knowing what questions should be asked early on in the questionnaire,in the middle or toward the end.

Understanding how to phrase questions. Being sensitive to questionnaire length.

There are some basic differences in how the questionnaire should be constructed if it is to be filled out personally by the respondent or if an interviewer is going to administer it. These are:

Self-administered questionnaires should be simple, straightforward and logical. Question 2 should follow question 1. Question 3 should follow question 2, and so forth. Further, the going-in assumption with self-administered questionnaires should be that respondents will not complete a questionnaire when there are complex skip patterns, when pages are crowded or hard to read or when instructions for completion are overly complex.

It has been estimated that as many as 50% of respondents who start a self-administered questionnaire will not complete it because they become irritated and annoyed at the way it is constructed. When writing a self-administered questionnaire, then, every care must be taken to ensure that it is easy to complete in that it almost answers itself.

Self-administered questionnaires should be written with an eighth grade mentality in mind while interviewer-administered questionnaire can be quite complex. Because interviewers are trained in the flow of the questionnaires they administer and will conduct a number of practice interviews prior to confronting a respondent, developing a complex questionnaire that is interviewer-administered does not present a problem for the respondent.

Keep the respondent in one mind-set at a time. If at all possible, complete all your questions about one topic before moving on to the next. For example, don’t ask about a favorite place to shop, then about brands used and then go back to additional questioning on favorite place to shop.

Save sensitive questions for the end. Again, this might not always be possible, but when it doesn’t matter, be aware that sensitive questions such as race or income can alienate respondents and turn them off to the entire interview process. If asked at the end, respondents are more likely to answer as they are wholly invested in the questionnaire.

Biased question: What do you like about the last airline flight you took? Assumption here is that respondent liked something and the question tends to push for a positive response.

Unbiased question. What, if anything, do you like the last airline flight you took? By simply using if anything as part of the question phrasing, the respondent is not put on the spot to find something to like.

When conducting telephone interviews, it’s relatively easy to keep respondents on the phone and answering questions for 15, 20 or 25 minutes if the questionnaire has a good flow and is thoughtfully written. But try keeping a respondent on the phone for 3 minutes with a questionnaire that is the least bit confusing, seems redundant or is insensitive to sensitive issues.

Question 2: What do you mean by primary data? What are the various methods of collecting primary data?

Answer:

Primary Date is data that has not been previously published, i.e. the data is derived from a new or original research study and collected at the source, e.g., in

marketing, it is information that is obtained directly from first-hand sources by means of surveys, observation or experimentation.

Data observed or collected directly from first-hand experience. Published data and the data collected in the past or other parties are called secondary data. Primary data are directly collected by the researcher from their original sources. In this case, the researcher can collect the required date precisely according to his research needs, he can collect them when he wants them and in the form he needs them. But the collection of primary data is costly and time consuming. Yet, for several types of social science research required data are not available from secondary sources and they have to be directly gathered from the primary sources.

In such cases where the available data are inappropriate, inadequate or obsolete, primary data have to be gathered. They include: socio economic surveys, social anthropological studies of rural communities and tribal communities, sociological studies of social problems and social institutions. Marketing research, leadership studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing surveys, knowledge-awareness practice (KAP) studies, farm managements studies, business management studies etc.

Primary data are always collected from the source. It is collected either by the investigator himself or through his agents. There are different methods of collecting primary data. Each method has its relative merits and demerits. The investigator has to choose a particular method to collect the information. The choice to a large extent depends on the preliminaries to data collection some of the commonly used methods are discussed below.

1) Direct Personal observation:

This is a very general method of collecting primary data. Here the investigator directly contacts the informants, solicits their cooperation and enumerates the data. The information are collected by direct personal interviews.

The novelty of this method is its simplicity. It is neither difficult for the enumerator nor the informants because both are present at the spot of data collection. This method provides most accurate information as the investigator collects them personally. But as the investigator alone is involved in the process, his personal bias may influence the accuracy of the data. So it is necessary that the investigator should be honest, unbiased and experienced. In such cases the data collected may be fairly accurate. However, the method is quite costly and time-consuming. So the method should be used when the scope of enquiry is small.

2) Indirect Oral Interviews:

This is an indirect method of collecting primary data. Here information is not collected directly from the source but by interviewing persons closely related with the problem. This method is applied to apprehend culprits in case of theft, murder etc. The information relating to one's personal life or which the informant hesitates to reveal are better collected by this method. Here the investigator prepares 'a small list of questions relating to the enquiry. The answers (information) are collected by interviewing persons well connected with the incident. The investigator should cross-examine the informants to get correct information.

This method is time saving and involves relatively less cost. The accuracy of the information largely depends upon the integrity of the investigator. It is desirable that the investigator should be experienced and capable enough to inspire and create confidence in the informant to collect accurate data.

3) Mailed Questionnaire method:

This is a very commonly used method of collecting primary data. Here information is collected through a set of questionnaire. A questionnaire is a document prepared by the investigator containing a set of questions. These questions relate to the problem of enquiry directly or indirectly. Here first the questionnaires are mailed to the informants with a formal request to answer the question and send them back. For better response the investigator should bear the postal charges. The questionnaire should carry a polite note explaining the aims and objective of the enquiry, definition of various terms and concepts used there. Besides this the investigator should ensure the secrecy of the information as well as the name of the informants, if required.

Success of this method greatly depends upon the way in which the questionnaire is drafted. So the investigator must be very careful while framing the questions. The questions should be:

Short and clear Few in number Simple and intelligible Corroboratory in nature or there should be provision for cross check Impersonal, non-aggressive type Simple alternative, multiple-choice or open-end type

a) In the simple alternative question type, the respondent has to choose between alternatives such as ‘Yes or No’, ‘right or wrong’ etc.For example: Is Adam Smith called father of Statistics? Yes/No

b) In the multiple choice type, the respondent has to answer from any of the given alternatives.Example: To which sector do you belong? Primary Sector Secondary Sector Tertiary or Service Sector

c) In the Open-end or free answer questions the respondents are given complete freedom in answering the questions. The questions are like –

What are the defects of our educational system?The questionnaire method is very economical in terms of time, energy and money. The method is widely used when the scope of enquiry is large. Data collected by this method are not affected by the personal bias of the investigator. However the accuracy of the information depends on the cooperation and honesty of the informants. This method can be used only if the informants are cooperative, conscious and educated. This limits the scope of the method.

4) Schedule Method:

In case the informants are largely uneducated and non-responsive data cannot be collected by the mailed questionnaire method. In such cases, schedule method is used to collect data. Here the questionnaires are sent through the enumerators to collect information. Enumerators are persons appointed by the investigator for the purpose. They directly meet the informants with the questionnaire. They explain the scope and objective of the enquiry to the informants and solicit their cooperation. The enumerators ask the questions to the informants and record their answers in the questionnaire and compile them. The success of this method depends on the sincerity and efficiency of the enumerators. So the enumerator should be sweet-tempered, good-natured, trained and well-behaved.

Schedule method is widely used in extensive studies. It gives fairly correct result as the enumerators directly collect the information. The accuracy of the information depends upon the honesty of the enumerators. They should be unbiased. This method is relatively more costly and time-consuming than the mailed questionnaire method.

5) From Local Agents:

Sometimes primary data are collected from local agents or correspondents. These agents are appointed by the sponsoring authorities. They are well conversant with the local conditions like language, communication, food habits, traditions etc. Being on the spot and well acquainted with the nature of the enquiry they are capable of furnishing reliable information.

The accuracy of the data collected by this method depends on the honesty and sincerity of the agents because they actually collect the information from the spot. Information from a wide area at less cost and time can be collected by this method. The method is generally used by government agencies, newspapers, periodicals etc. to collect data.

Information is like raw materials or inputs in an enquiry. The result of the enquiry basically depends on the type of information used. Primary data can be collected by employing any of the above methods. The investigator should make a rational choice of the methods to be used for collecting data because collection of data forms the beginning of the statistical enquiry.

Question 3a. Analyse the case study and descriptive approach to research.b. Distinguish between research methods & research Methodology.

Answer:

a) Case Study and descriptive approach to research:

Descriptive research, also known as statistical research, describes data and characteristics about the population or phenomenon being studied. Descriptive research answers the questions who, what, where, when and how...

Although the data description is factual, accurate and systematic, the research cannot describe what caused a situation. Thus, Descriptive research cannot be used to create a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity.

The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are.

In short descriptive research deals with everything that can be counted and studied. But there are always restrictions to that. Your research must have an impact to the lives of the people around you e.g. finding the most frequent disease that affects the children of a town. The reader of the research will know what to do to prevent that disease thus; more people will live a healthy life.

Descriptive research does not fit neatly into the definition of either quantitative or qualitative research methodologies, but instead it can utilize elements of both, often within the same study. The term descriptive research refers to the type of research question, design, and data analysis that will be applied to a given topic. Descriptive statistics tell what is, while inferential statistics try to determine cause and effect.

A case study is a research method common in social science. It is based on an in-depth investigation of a single individual, group, or event. Case studies may be descriptive or explanatory. The latter type is used to explore causation in order to find underlying principles. They may be prospective, in which criteria are established and cases fitting the criteria are included as they become available, or retrospective, in which criteria are established for selecting cases from historical records for inclusion in the study.

Rather than using samples and following a rigid protocol (strict set of rules) to examine limited number of variables, case study methods involve an in-depth, longitudinal (over a long period of time) examination of a single instance or event: a case. They provide a systematic way of looking at events, collecting data, analyzing information, and reporting the results. As a result the researcher may gain a sharpened understanding of why the instance happened as it did, and what might become important to look at more extensively in future research. Case studies lend themselves to both generating and testing hypotheses.

Another suggestion is that case study should be defined as a research strategy, an empirical inquiry that investigates a phenomenon within its real-life context. Case study research means single and multiple case studies, can include quantitative evidence, relies on multiple sources of evidence and benefits from the prior development of theoretical propositions. Case studies should not be confused with qualitative research and they can be based on any mix of quantitative and qualitative evidence. Single-subject research provides the statistical framework for making inferences from quantitative case-study data

b) Distinction between research methods and research Methodology:

Research Methods Research MethodologyResearch methods are the various procedures, schemes, algorithms, etc. used in research. All the methods used by a researcher during a research study are termed as research methods. They are essentially planned, scientific and value-neutral. They include theoretical procedures, experimental studies, numerical schemes, statistical approaches, etc. Research methods help us collect samples, data and find a solution to a problem. Particularly, scientific research methods call for explanations based on collected facts, measurements and observations and not on reasoning alone. They ac- cept only those explanations which can be verified by experiments.

Research methodology is a systematic way to solve a problem. It is a science of studying how research is to be carried out. Essentially, the procedures by which researchers go about their work of describing, explaining and predicting phenomena are called research methodology. It is also defined as the study of methods by which knowledge is gained. Its aim is to give the work plan of research.

Question 4: Explain the important concepts in Research design?

Answer:

The research designer understandably cannot hold all his decisions in his head. Even if he could, he would have difficulty in understanding how these are inter-related. Therefore, he records his decisions on paper or record disc by using relevant symbols or concepts. Such a symbolic construction may be called the research design or model. A research design is a logical and systematic plan prepared for directing a research study. It specifies the objectives of the study, the methodology and techniques to be adopted for achieving the objectives. It constitutes the blue print for the plan is the overall scheme or program of research. A research design is the program that guides the investigator in the process of collecting, analysing and interpreting observations. It provides a systematic plan of procedure for the researcher to follow elltiz, Jahoda and Destsch and Cook describe, “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.”

Components of Research Design: It is important to be familiar with the important concepts relating to research design. They are:

1. Dependent and Independent variables:

A magnitude that varies is known as a variable. The concept may assume different quantitative values, like height, weight, income, etc. Qualitative variables are not quantifiable in the strictest sense of objectivity. However, the qualitative phenomena may also be quantified in terms of the presence or absence of the attribute considered. Phenomena that assume different values quantitatively even in decimal points are known as “continuous variables. But, all variables need not be continuous. Values that can be expressed only in integer values are called” non-continuous variables. In statistical term, they are also known as „discrete variable. For example, age is a continuous variable; whereas the number of children is a non-continuous variable. When changes in one variable depends upon the changes in one or more other variables, it is known as a dependent or endogenous variable, and the variables that cause the changes in the dependent variable are known as the independent or explanatory or exogenous variables. For example, if demand depends upon price, then demand is a dependent variable, while price is the independent variable.

And if, more variables determine demand, like income and prices of substitute commodity, then demand also depends upon them in addition to the own price. Then, demand is a dependent variable which is determined by the independent variables like own price, income and price of substitute.

2. Extraneous variable: The independent variables which are not directly related to the purpose of the study but affect the dependent variable are known as extraneous variables. For instance, assume that a researcher wants to test the hypothesis that there is relationship between children’s school performance and their self-concepts, in which case the latter is an independent variable and the former, the dependent variable. In this context, intelligence may also influence the school performance. However, since it is not directly related to the purpose of the study undertaken by the researcher, it would be known as an extraneous variable. The influence

caused by the extraneous variable on the dependent variable is technically called as an „experimental errors Therefore, a research study should always be framed in such a manner that the dependent variable completely influences the change in the independent variable and any other extraneous variable or variables. 3. Control:

One of the most important features of a good research design is to minimize the effect of extraneous variable. Technically, the term control is used when a researcher designs the study in such a manner that it minimizes the effects of extraneous independent variables. The term control is used in experimental research to reflect the restrain in experimental conditions.

4. Confounded relationship:

The relationship between dependent and independent variables is said to be confounded by an extraneous variable, when the dependent variable is not free from its effects.

Research hypothesis:

When a prediction or a hypothesized relationship is tested by adopting scientific methods, it is known as research hypothesis. The research hypothesis is a predictive statement which relates a dependent variable and an independent variable. Generally, a research hypothesis must consist of at least one dependent variable and one independent variable. Whereas, the relationships that are assumed but not be tested are predictive statements that are not to be objectively verified are not classified as research hypothesis.

Experimental and control groups:

When a group is exposed to usual conditions in an experimental hypothesis-testing research, it is known as „control group. On the other hand, when the group is exposed to certain new or special condition, it is known as an „experimental group. In the afore-mentioned example, the Group A can be called a control group and the Group B an experimental one. If both the groups A and B are exposed to some special feature, then both the groups may be called as „experimental groups. A research design may include only the experimental group or the both experimental and control groups together.

Treatments:

Treatments are referred to the different conditions to which the experimental and control groups are subject to. In the example considered, the two treatments are the parents with regular earnings and those with no regular earnings. Likewise, if a research study attempts to examine through an experiment regarding the comparative impacts of three different types of fertilizers on the yield of rice crop, then the three types of fertilizers would be treated as the three treatments.

Experiment:

An experiment refers to the process of verifying the truth of a statistical hypothesis relating to a given research problem. For instance, experiment may be conducted to examine the yield of a certain new variety of rice crop developed. Further, Experiments may be categorized into two types namely, absolute experiment and comparative experiment. If a researcher

wishes to determine the impact of a chemical fertilizer on the yield of a particular variety of rice crop, then it is known as absolute experiment. Meanwhile, if the researcher wishes to determine the impact of chemical fertilizer as compared to the impact of bio-fertilizer, then the experiment is known as a comparative experiment.

Experiment unit:

Experimental units refer to the predetermined plots, characteristics or the blocks, to which the different treatments are applied. It is worth mentioning here that such experimental units must be selected with great caution.

Question 5: What are the differences between observation and interviewing as methods of data collection? Give two specific examples of situations where either observation or interviewing would be more appropriate.

Answer:

Observation vs. interviewing as Methods of Data Collection:

Collection of data is the most crucial part of any research project as the success or failure of the project is dependent upon the accuracy of the data. Use of wrong methods of data collection or any inaccuracy in collecting data can have significant impact on the results of a study and may lead to results that are not valid. There are many techniques of data collection along a continuum and observation and interviewing are two of the popular methods on this continuum that has quantitative methods at one end while qualitative methods at the other end. Though there are many similarities in these two methods and they serve the same basic purpose, there are differences that will be highlighted in this article.

Observation:

Observation, as the name implies refers to situations where participants are observed from a safe distance and their activities are recorded minutely. It is a time consuming method of data collection as you may not get the desired conditions that are required for your research and you may have to wait till participants are in the situation you want them to be in. Classic examples of observation are wild life researchers who wait for the animals of birds to be in a natural habitat and behave in situations that they want to focus upon. As a method of data collection, observation has limitations but produces accurate results as participants are unaware of being closely inspected and behave naturally.

Interviewing:

Interviewing is another great technique of data collection and it involves asking questions to get direct answers. These interviews could be either one to one, in the form of questionnaires, or the more recent form of asking opinions through internet. However, there are limitations of interviewing as participants may not come up with true or honest answers depending upon privacy level of the questions. Though they try to be honest, there is an element of lie in answers that can distort results of the project.

Though both observation and interviewing are great techniques of data collection, they have their own strengths and weaknesses. It is important to keep in mind which one of the two will produce desired results before finalizing.

Observation vs. interviewing:

Observation InterviewingObservation requires precise analysis by the researcher and often produces most accurate results although it is very time consuming.

Interviewing is easier but suffers from the fact that participants may not come up with honest replies.

Interview format:

Interviews take many different forms. It is a good idea to ask the organisation in advance what format the interview will take.

Competency/criteria based interviews:

These are structured to reflect the competencies or qualities that an employer is seeking for a particular job, which will usually have been detailed in the job specification or advert. The interviewer is looking for evidence of your skills and may ask such things as: µGive an example of a time you worked as part of a team to achieve a common goal.

Technical interviews:

If you have applied for a job or course that requires technical knowledge, it is likely that you will be asked technical questions or has a separate technical interview. Questions may focus on your final year project or on real or hypothetical technical problems. You should be prepared to prove yourself, but also to admit to what you do not know and stress that you are keen to learn. Do not worry if you do not know the exact answer - interviewers are interested in your thought process and logic.

Academic interviews:

These are used for further study or research positions. Questions are likely to centre on your academic history to date.

Structured interviews:

The interviewer has a set list of questions, and asks all the candidates the same questions.

Formal/informal interviews:

Some interviews may be very formal, while others will feel more like an informal chat about you and your interests. Be aware that you are still being assessed, however informal the discussion may seem.

Portfolio based interviews:

If the role is within the arts, media or communications industries, you may be asked to bring a portfolio of your work to the interview, and to have an in-depth discussion about the pieces you have chosen to include.

Senior/case study interviews:

These ranges from straightforward scenario questions (e.g. µWhat would you do in a situation where to the detailed analysis of a hypothetical business problem. You will be evaluated on your analysis of the problem, how you identify the key issues, how you pursue a particular line of thinking and whether you can develop and present an appropriate framework for organising your thoughts.

Specific types of interview

The Screening Interview:

Companies use screening tools to ensure that candidates meet minimum qualification requirements. Computer programs are among the tools used to weed out unqualified candidates. (This is why you need a digital resume that is screening-friendly. See our resume centre for help.) Sometimes human professionals are the gatekeepers. Screening interviewers often have honed skills to determine whether there is anything that might disqualify you for the position. Remember they do not need to know whether you are the best fit for the position, only whether you are not a match. For this reason, screeners tend to dig for dirt. Screeners will hone in on gaps in your employment history or pieces of information that look inconsistent. They also will want to know from the outset whether you will be too expensive for the company.

  Some tips for maintaining confidence during screening interviews:

Highlight your accomplishments and qualifications.

Get into the straightforward groove. Personality is not as important to the screener as verifying your qualifications. Answer questions directly and succinctly. Save your winning personality for the person making hiring decisions!

Be tactful about addressing income requirements. Give a range, and try to avoid giving specifics by replying, "I would be willing to consider your best offer."

If the interview is conducted by phone, it is helpful to have note cards with your vital information sitting next to the phone. That way, whether the interviewer catches you sleeping or vacuuming the floor, you will be able to switch gears quickly

The Informational Interview:

On the opposite end of the stress spectrum from screening interviews is the informational interview. A meeting that you initiate, the informational interview is underutilized by job-seekers who might otherwise consider themselves savvy to the merits of networking. Jobseekers ostensibly secure informational meetings in order to seek the advice of someone in their current or desired field as well as to gain further references to people who can lend insight. Employers that like to stay apprised of available talent even when they do not have current job openings, are often open to informational interviews, especially if they like to share their knowledge, feel flattered by your interest, or esteem the mutual friend that connected you to them. During an informational interview, the jobseeker and employer exchange information and get to know one another better without reference to specific job opening.

This takes off some of the performance pressure, but be intentional nonetheless:

Come prepared with thoughtful questions about the field and the company.

Gain references to other people and make sure that the interviewer would be comfortable if you contact other people and use his or her name.

Give the interviewer your card, contact information and resume.· Write a thank you note to the interviewer.

The Directive Style:

In this style of interview, the interviewer has a clear agenda that he or she follows unflinchingly. Sometimes companies use this rigid format to ensure parity between interviews; when interviewers ask each candidate the same series of questions, they can more readily compare the results. Directive interviewers rely upon their own questions and methods to tease from you what they wish to know. You might feel like you are being steam-rolled, or you might find the conversation develops naturally. Their style does not necessarily mean that they have dominance issues, although you should keep an eye open for these if the interviewer would be your supervisor. Either way, remember:· Flex with the interviewer, following his or her lead.· Do not relinquish complete control of the interview. If the interviewer does not ask you for information that you think is important to proving your superiority as a candidate, politely interject it.

The Meandering Style:

This interview type, usually used by inexperienced interviewers, relies on you to lead the discussion. It might begin with a statement like "tell me about yourself," which you can use to your advantage. The interviewer might ask you another broad, open-ended question before falling into silence. This interview style allows you tactfully to guide the discussion in a way that best serves you. The following strategies, which are helpful for any interview, are particularly important when interviewers use a non-directive approach:

Come to the interview prepared with highlights and anecdotes of your skills, qualities and experiences. Do not rely on the interviewer to spark your memory-jot down some notes that you can reference throughout the interview.

Remain alert to the interviewer. Even if you feel like you can take the driver's seat and go in any direction you wish, remain respectful of the interviewer's role. If he or she becomes more directive during the interview, adjust.

Ask well-placed questions. Although the open format allows you significantly to shape the interview, running with your own agenda and dominating the conversation means that you run the risk of missing important information about the company and its needs.

Question 6: Strictly speaking, would case studies be considered as scientific research? Why or why not?

Answer:

Case studies are a tool for discussing scientific integrity. Although one of the most frequently used tools for encouraging discussion, cases are only one of many possible tools. Many of the principles discussed below for discussing case studies

can be generalized to other approaches to encouraging discussion about research ethics.

Cases are designed to confront readers with specific real-life problems that do not lend themselves to easy answers. Case discussion demands critical and analytical skills and, when implemented in small groups, also fosters collaboration (Pimple, 2002). By providing a focus for discussion, cases help trainees to define or refine their own standards, to appreciate alternative approaches to identifying and resolving ethical problems, and to develop skills for analyzing and dealing with hard problems on their own. The effective use of case studies is comprised of many factors, including:

appropriate selection of case(s) (topic, relevance, length, complexity) method of case presentation (verbal, printed, before or during discussion) format for case discussion (Email or Internet-based, small group, large

group) leadership of case discussion (choice of discussion leader, roles and

responsibilities for discussion leader) outcomes for case discussion (answers to specific questions, answers to

general questions, written or verbal summaries)

Research methods don't seem so intimidating when you're familiar with the terminology. This is important whether you're conducting evaluation or merely reading articles about other studies to incorporate in your program. To help with understanding, here are some basic definitions used.

Variable: Characteristics by which people or things can be described. Must have more than one level; in other words, to be able to change over time for the same person/object, or from person to person, or object to object. Some variables, called attributes, cannot be manipulated by the researcher (e.g., socioeconomic status, IQ score, race, gender, etc.). Some variables can be manipulated but are not in a particular study. This occurs when subjects self-select the level of the independent variable, or the level is naturally occurring (as with ex post facto research).

Manipulation: Random assignment of subjects to levels of the independent variable (treatment groups).

Independent variable: The treatment, factor, or presumed cause that will produce a change in the dependent variable. This is what the experimenter tries to manipulate. It is denoted as "X" on the horizontal axis of a graph.

Dependent variable: The presumed effect or consequence resulting from changes in the independent variable. This is the observation made and is denoted by "Y" on the vertical axis of a graph. The score of "Y" depends on the score of "X."

Population: The complete set of subjects that can be studied: people, objects, animals, plants, etc.

Sample: A subset of subjects that can be studied to make the research project more manageable. There are a variety of ways samples can be taken. If a large enough random samples are taken, the results can be statistically similar to taking a census of an entire population--with reduced effort and cost.

Case Study:

A case study is conducted for similar purpose as the above but is usually done with a smaller sample size for more in-depth study. A case study often involves direct observation or interviews with single subjects or single small social units such as a family, club, school classroom, etc. This is typically considered qualitative research.

Purpose: Explain or Predict

Type of Research to Use: Relational Study

In a relational study you start with a research hypothesis, that is, is what you're trying to "prove."

Examples of research hypotheses for a relational study:

The older the person, the more health problems he or she encounters. 4-H members attending 4-H summer camp stay enrolled in 4-H longer. The greater the number of money management classes attended, the

greater the amount of annual savings achieved.

Types of relational studies include correlational studies and ex post facto studies.

Correlational Study:

A correlational study compares two or more different characteristics from the same group of people and explains how two characteristics vary together and how well one can be predicted from knowledge of the other.

A concurrent correlational study draws a relationship between characteristics at the same point in time. For example, a student's grade point average is related to his or her class rank.

A predictive correlational study could predict a later set of data from an earlier set. For example, a student's grade point average might predict the same student's grade point average during senior year. A predictive correlational study could also use one characteristic to predict what another characteristic will be at another time. For example, a student's SAT score is designed to predict college freshman grade point average.

Ex Post Facto (After the Fact) Study:

An ex post facto study is used when experimental research is not possible, such as when people have self-selected levels of an independent variable or when a treatment is naturally occurring and the researcher could not "control" the degree of its use. The researcher starts by specifying a dependent variable and then tries to identify possible reasons for its occurrence as well as alternative (rival) explanations such confounding (intervening, contaminating, or extraneous) variables are "controlled" using statistics.

This type of study is very common and useful when using human subjects in real-world situations and the investigator comes in "after the fact." For example, it might be observed that students from one town have higher grades than students from a different town attending the same high school. Would just "being from a certain town" explain the differences? In an ex post facto study, specific reasons for the differences would be explored, such as differences in income, ethnicity, parent support, etc. It is important to recognize that, in a relational study, "cause and effect" cannot be claimed. All that can be claimed is that that there is a relationship between the variables.

For that matter, variables that are completely unrelated could, in fact, vary together due to nothing more than coincidence. That is why the researcher needs to establish a plausible reason (research hypothesis) for why there might be a relationship between two variables before conducting a study. For instance, it might be found that all football teams with blue uniforms won last week. There is no likely reason why the uniform color had any relationship to the games' outcomes, and it certainly was not the cause for victory. Similarly, you must be careful about claiming that your Extension program was the "cause" of possible results.