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CHAPTER 4

RESEARCH METHODOLOGY

INTRODUCTION

According to Johan West, Research is a systematic activity directed towards the

discovery and development of an organized body of knowledge.

Curiosity and desire for searching new dimensions of the facts are the human nature.

Doing different things and doing things differently are related with the finding new

knowledge area and finding new ways of the existing processes. Research demands

systematic and scientific way of giving thoughts to an area of knowledge. Research

requires following proper methods, methodology, techniques, tools, approaches, and

design. Research begins with identification of a problem. It includes detailed

investigation into the facts of the problem or issue, analyze data, and come to the

conclusion. But research process does not end there. Research is a continuous process.

One piece of the research thrusts the other researchers to investigate further into the

same or similar issue. One research opens the avenues for the other problems to

research further. Understanding research methods helps in solving decision based

problems. However, research is not done arbitrarily. Research must be done

scientifically. Methodology tells us which methods, techniques, or tests are applicable

in certain situations. Research methodology also includes reasons and logic behind

how and why every step of the research process is followed. Research is cousin of

scientific thinking/method.

According to Karl Pearson, The scientific method is one and same in the branches

(of science) and that method is the method of all logically trained minds the unity

of all sciences consists alone in its methods, not its material; the man who classifies

facts of any kind whatever, who sees their mutual relation and describes their

sequences, is applying the Scientific Method and is a man of science.1 Bellenger et

1. Pearson Karl, The Grammar of Science, Part I, p 1012.

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al. say that a good research is systematic, logical, empirical, and replicable. All these

criteria should be checked while following steps such as defining research problem or

issue, undergoing literature, selecting research design and sample design, collecting,

analyzing and interpreting data, and presenting report or thesis. Methodology tells us

how to thinks about nature of the problem or issue and its solution. Induction and

deduction process also become helpful in this reasoning process. It helps the

researchers to use concepts, constructs, definitions etc. derived from the research

problem or issue. Variables and hypotheses help to build models and to develop

theories as a part of the research results. Objectives, motivations, and approaches of

the research are in the base for research methodology. It is always advisable to

understand and then follow prescribed steps and process of planning and conducting

research to avoid errors in the research conducted.

Following is the methodology followed in this research work:

4.1. RESEARCH OBJECTIVES:

4.1.1. To know the awareness about Permission Marketing and its related concepts

among people.

4.1.2. To measure customers preferences of using marketing communications

tools with permission marketing applied.

4.1.3. To judge the applicability of permission marketing among all the Integrated

Marketing Communication tools.

4.1.4. To find out whether customers take particular actions against marketing

companies for sending unwanted communication messages.

4.1.5. To know what difficulties may come across while implementing permission

marketing in India.

4.2. RESEARCH DESIGN:

Research design constitutes the blueprint for the collection, measurement, and

analysis of data1

1 Phillips Bernard, Social Research Strategy and Tactics, Macmillan Publishing, 2nd ed, 1971, p 93.

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A research design expresses both the structure of the research problem and the

plan of investigation used to obtain empirical evidence on relations of the

problem.1

Research design is a framework for the research. There are two types of major

research designs: exploratory and formal. Malhotra classifies it as exploratory and

conclusive also as presented below:2

Figure 7 Classifications of Research Design

(Source: Malhotra Naresh, Marketing Research, Pearson Education, 5th ed, 2007, p 109.)

Conclusive research is more structured compared to exploratory research. Exploratory

research does not involve testing hypotheses. It is not as structured as conclusive

research. Conclusive research helps in decision making. It is used with large samples

and there is a scope of quantitative analysis of data. It is further classified into

descriptive research and causal research. Questions such as who, what, when, where,

how etc. are addressed in descriptive research. Description of characteristics is major

focus in descriptive research. While determining cause and effect relationship is major

focus in causal research. Descriptive research is further classified as longitudinal

design and cross-sectional design. In longitudinal design, same sample is measured

repeatedly. The study is conducted for longer period of time. While in cross-sectional

1 Kerlinger Fred, Foundations of Behavioral Research, Harcourt College publishers, 3rd ed, p 279.2 Malhotra Naresh, Marketing Research: An Applied Orientation, Pearson Education, 5th ed, 2007, p109.

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design, study is conducted only once, without repetition. It is further classified as

single cross-sectional design and multiple cross-sectional design. When data are

collected from one sample and only once, it is known as single-cross-sectional design

while when data are collected from more than one sample and only once, it is known

as multiple-cross-sectional design. Descriptive single cross-sectional design is used in

this research.

4.3. SAMPLING DESIGN:

There are two types of sampling design: probability sampling design and non

probability sampling design. Both types of designs have restricted or unrestricted

element selection. When each sample element is selected individually and directly

from the population, they are known as unrestricted samples. In probability sampling,

Simple Random sampling is unrestricted and in non probability sampling design,

convenience sampling is unrestricted sampling. Random selection principle, where

each population element has a known non-zero chance of selection, is used in

probability sampling. While in non probability sampling design, each population

element does not have a known nonzero chance of selection and researchers own

judgment is used. In this research Convenience sampling is used.

4.3.1. Population:

Some elements of the population are selected to conclude and apply

result to the population. Population in this research is a set of

customers receiving marketing communications such as recorded calls,

live calls, SMS (text messages), spam/junk, and catalogs/brochures.

4.3.2. Extent: Gujarat State (Ahmedabad, Anand, Vadodara, Jamnagar,

Rajkot, and Surat city)

4.3.3. Sampling Frame:

For the given research customers at point of purchase like banks, retail

stores, telecom companys outlets and households receiving personal

selling communication for consumer durables and financial products

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have been selected. Sampling frame is the list of elements from where

the sample is to be selected.

4.3.4. Sampling Unit:

A set of residents of Ahmedabad, Anand, Vadodara, Jamnagar, Rajkot,

and Surat city who are receiving marketing communications such as

recorded calls, live calls, SMS, spam/junk, and catalogs/brochures.

4.3.5. Sampling Method:

Convenience sampling as a part of non-probability sampling methods

has been used for selecting the sample.

4.3.6. Sample Size: 462

4.4. DATA COLLECTION METHOD:

4.4.1. Primary data source:

Primary data source is data collected through Survey method.

Questionnaire with non-disguised structured questionnaire with Likert-

type, dichotomous, Multiple Choice, and open-ended questions is used.

4.4.2. Secondary data:

Secondary Data source: Books, Journals, Published Reports,

Newspapers, Magazines, other library and electronic data.

4.5. Variables

Following variables are selected and analyzed with reference to customers

awareness, perceptions, and preferences of permission marketing.

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Table 6 List of Variables used in the research

Sr. No Variables

1 Age of the Respondents

2 Awareness about CDRC office

3 Awareness about Consumer Dispute Redress Commission

4 Awareness about Consumer Protection Act

5 Awareness about CPA contact details

6 Awareness about Do Not Call Registry

7 Awareness about Permission Marketing

8 City of the Respondents

9 Disturbance Rank given to Brochures/Catalogs

10 Disturbance Rank given to Live Calls

11 Disturbance Rank given to Recorded Calls

12 Disturbance Rank given to SMS

13 Disturbance Rank given to Spam/Junk

14 Disturbed by Catalogs/Brochures

15 Disturbed by SMS

16 Disturbed by Spam/Junk emails

17 Disturbed by the Calls

18 Education of the Respondents

19 Expectations from the Government

20 Finding Calls interesting

21 Finding Calls useful

22 Finding Catalogs/Brochures interesting

23 Finding Catalogs/Brochures useful

24 Finding SMS interesting

25 Finding SMS useful

26 Finding Spam/Junk emails interesting

27 Finding Spam/Junk emails useful

28 Frequency of receiving Catalogs/Brochures

29 Frequency of receiving Live Calls

30 Frequency of receiving Recorded Calls

31 Frequency of receiving SMS

32 Gender of the Respondents

33 Income of the Respondents

34 Internet Access Point

35 Internet Usage

36 Internet Users

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37 No. of days since Stop Call request sent

38 No. of days since Stop SMS request sent

39 No. of E-mail Ids

40 Occupation of the Respondents

41 Place of receiving Catalogs/Brochures

42 Reading Catalogs/Brochures completely

43 Thinking Calls should be banned

44 Thinking Catalogs/Brochures should be banned

45 Thinking SMS should be banned

46 Thinking Spam/Junk should be banned

47 Treatment with Spam/Junk

48 Want to stop Calls

49 Want to stop Catalogs/Brochures

50 Want to stop SMS

51 Want to stop Spam/Junk

4.6. Hypotheses

Hypotheses are formulated on the basis of theories and concept developed from the

previous studies. Study conducted by Sheehan Kim and Hoy Mariea (1999) shows

consumers responses to privacy1. They found that as concern for privacy increased;

respondents provided incomplete information to websites, notified ISP about

unsolicited e-mail, requested for opting out from mail list, sent negative messages,

registered for websites requesting information.

Carroll, Barnes, and Eusebio investigated consumer's perceptions and attitudes

towards mobile marketing via SMS through a sequential, mixed method investigation

identifying four factors - permission, content, wireless service provider (WSP)

control, and the delivery of the message.2 It was proved that all these factors have a

significant impact on mobile marketing acceptance. Karjaluoto at el. found that

perceived usefulness, perceived ease of use, and perceived trust affect attitude toward

advertising and intention to engage in permission based mobile communications with

1 Sheehan Kim and Hoy Mariea, Flaming, Complaining, Abstaining: How Online Users Respond toPrivacy Concerns, Journal of Advertising, vol 28, No. 3, Fall 1999, p 37-51.2 Carroll Amy , Barnes Stuart J, and Scornavacca Eusebio, Consumers Perceptions and Attitudestowards SMS Mobile Marketing in New Zealand, Proceedings of the International Conference onMobile Business, IEEE Computer Society, USA , 2005 , p 434 440.

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a firm.1 Moreover, compared to men, women have a stronger relationship between

mobile marketing communications with intentions to visit and actual visits. Jon Ingall

(2008) says that increasing demand of 3G handsets show potential of cellphones as

media for advertising.2 A survey conducted by Bluestreak in the U.K. and the U.S.

found that respondents open one of three permission-based e-mails.3 In the same

survey, the respondents in the U.S. said SMS marketing was the most unpopular form

of communications. 80 % respondents felt negative for SMS marketing by the

advertisers.

Rogers (1996) examined trends in mail advertising from a study of the U.S. household

mail communications inspecting how consumers feel, perceive, and respond to the

advertising mail received by them.4 It was found that type of mail is important.

Catalogs and newsletters found to be the most valued advertising mail. Customers

found these useful and interesting. Catalogs have both advantages and disadvantages.5

Catalogs can be made targeted, these are easy to read, having complete information

and are convenient. But at the same time, catalogs are costly, viewed with negative

perceptions, having low response rate, and require updating database frequently.

Rowley (2002) provided tips for effective e-mail communication.6 E-mail

communication should be relevant and targeted. It should be timely, infrequent, and

personalized. E-mails must have features such as auto-response, opt-out etc. Training

is required for customer service agents to understand effect and legality of e-mail

correspondence. The response must be quick.

Following hypotheses are formulated for this research:

1. H0: There is no significant relationship between customers awareness about Do

Not Call Registry and Education and Occupation of the customers.

1Karjaluoto Heikki, Lehto Heikki, Leppniemi Matti, and Jayawardhena Chanaka, Exploring GenderInfluence on Customers Intention to Engage Permission-Based Mobile Marketing, Electronic Markets,vol 18, No. 3, 2008, p 242-259.2 Ingall Jon, Why Permission is the Biggest Issue, Precision Marketing, 8th Aug, 2008, p 14.3 Email is Most Acceptable Channel for Permission-Based Marketing, NMA, 9th Nov, 2006, p 9.4 Rogers Jean, Mail Advertising and Consumer Behavior, Psychology and Marketing, vol 13, No. 2,March 1996, p 211233.5 Wells William, Burnett John and Moriarty Sandra, Advertising: Principles & Practice, 6th ed, PHI,New Delhi, 2005, p 23.6 Rowley Jennifer, E-business Principles and practice, Palgrave, New York, 2002.

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H1: There is significant relationship between customers awareness about Do Not

Call Registry and Education and Occupation of the customers.

2. H0: There is no significant relationship between customers awareness about Do

Not Call Registry and City and Gender of the customers.

H1: There is significant relationship between customers awareness about Do Not

Call Registry and City and Gender of the customers.

3. H0: There is no significant relationship between disturbance ranks given to

marketing communications by the customers and customers preferences to stop

them.

H1: There is significant relationship between disturbance ranks given to marketing

communications by the customers and customers preferences to stop them.

4. H0: There is no significant relationship between customers finding marketing

communications interesting and finding the marketing communications useful.

H1: There is significant relationship between customers finding marketing

communications interesting and finding the marketing communications useful.

5. H0: There is no significant relationship between customers awareness about

Permission Marketing and Education, Occupation, and City of the customers.

H1: There is significant relationship between customers awareness about

Permission Marketing and Education, Occupation, and City of the customers.

4.7. IMPORTANCE OF THE RESEARCH:

Customers can opt for not receiving any marketing communication from marketers

and thus protect themselves from time consuming, irritating, and irrelevant

information. Customers can select specific information, in which they are interested

and filter out the information, which they feel useless. Marketers can allocate their

attention, budget, and time on sending relevant and the messages which interest the

customers by knowing customers preferences. Marketers can avoid wrath of the

customers, if customers get irritated from the marketing messages. Marketers can

avoid creating negative impressions in the mind of their potential or existing

customers. Taking prior permission by the marketers from the customers can be

included in the law as a part of consumerism movement. The customers can have

evidence of their willingness to receive particular information in the case of any legal

matter.

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4.8. LIMITATIONS OF THE RESEARCH:

4.8.1. Geographic limit:

The survey is limited to geographic boundaries of Gujarat state. Moreover,

each and every city, town, or village is not possible to include in the study

due to time and cost constraints.

4.8.2. Respondents bias:

As this research uses non probability sampling design, there are chances of

existence of bias and this may twist the findings.

4.8.3. Novelty of Concept:

Researcher will have to utilize the respondents initial time in explaining

the emerging concept of permission marketing.

4.8.4. Exclusion of Element:

The general population, which does not receive any kind of marketing

communication, such as illiterate population cannot be included in the

study. So, they are excluded. Moreover, the study is limited to Phone calls,

SMS, Catalogs, and spam. Media such as broadcast (TV and Radio), other

print medium (magazines), and outdoor media are excluded.

4.8.5. New Vista:

This is the kind of issue upon which much work has not been done so far.

So, researcher has to rely less on secondary data to research this emerging

concept.

4.9. RESEARCH GAP

Permission marketing is not widely studies in India. The research has been carried out

to know the awareness, preferences, and perceptions related to permission marketing.

The inter-relationship among with various Integrated Marketing Communication

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components, such as direct marketing, advertising, sales promotion, public relations,

personal selling, and electronic marketing has not established yet. Legal implications

of permission marketing are not understood so far. The attempt has been made to

fulfill this gap by this research.

Taking base of this research, further study can be undertaken regarding permission

marketing and new media, role of government in practicing permission marketing,

prospective legislation needed to be applied in various countries, socio-psychological

impact of permission marketing on customers, applicability of permission marketing

to broadcast media, magazines, and outdoor media etc.

4.10. PILOT STUDY

The attempts are made to know what customers think about marketing

communications they receive through phone calls, SMS, e-mails or printed matter.

Simple, undisguised, structured questionnaires are run among the respondents who are

First Year Undergraduate students. Students, particularly youngsters are more likely

to use cell phones and internet. Hypotheses are formed to know awareness about

permission marketing concept, awareness about Do Not Call Registry, awareness

about Permission Marketing, perceptions and preferences towards marketing

communications, Consumer Dispute Redressal Commission and Consumer Protection

Act awareness etc.

4.10.1. Methodology of Pilot Study:

After checking normality of the sample distribution, it was found that sample is

not normally distributed. Data do not satisfy assumptions of parametric tests.

Thus, Chi-square (2) Test is used. Most commonly adopted 0.05 Level of

Significance is taken.

4.10.2. Pilot Study Results

The pilot study was conducted among First year BBA students. So the result may

show similar response pattern for data such as age, education, occupation, income,

city etc. However, the same pattern will not be observed in the large sample

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actual research. It is found that there is no major changes required in the design of

the data collection instrument i.e. questionnaire, in sampling frame or in method

of the survey. Pilot study helped to find out more relationships among variables.

Clear planning became possible before collecting survey data.

From hypotheses testing the following results are derived:

1. There is no significant relationship between the frequency of marketing calls

and the frequency of SMS. The customers who receive marketing calls more

frequently may not necessarily receive marketing SMS very often and vice a

versa.

2. Those who want to stop the calls have surely attempted to stop them. Out of

those who are willing to stop marketing calls, not a single respondent has

given up to stop marketing calls by informing the Mobile Service Providers.

There is no gap in the mind of respondents in thinking to stop calls and taking

action to stop calls.

3. Those who want to stop the SMS have surely attempted to stop them. Out of

those who are willing to stop marketing SMS, not a single respondent has

requested to stop marketing SMS. There is no gap in the mind of respondents

in thinking to stop SMS and taking action to stop SMS.

4. Those who have attempted to stop the calls have not attempted to stop SMS

simultaneously. It is found that there are some respondents who have

attempted to stop calls but not SMS. At the same time there are respondents

who have tried to stop SMS but not marketing calls.

5. Those who are aware about DNCR (Do Not Call Registry) are ready to stop

calls than others. Comparison is made between two types of respondents

respondents aware about DNCR and who are unaware about DNCR. The

result shows that those who are aware about DNCR are much ready to stop

SMS than others.

6. There is a significant relationship between customers finding call detail and

SMS details interesting. Perceived usefulness of calls has a relationship with

perceived usefulness of SMS. Those who are very much disturbed by the calls

do not think that calls should be banned.

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7. There is a significant relationship between customers thinking of ban over

call and over SMS. Those who are very much disturbed by the SMS think that

SMS should be banned.

After analyzing results of the pilot study, it was required to make correction in the list

of questions. Necessary changes were made in the questions and more questions were

added in the questionnaire. Filled up questionnaires are collected from 462

respondents. The respondents are from six cities of Gujarat, India. The cities are

Ahmedabad, Vadodara, Surat, Rajkot, Jamnagar, and Anand.

After, coding the data, following tests are conducted with the help of SPSS (Statistical

Package for the Social Sciences) software:

1. Measures of Central tendency or location (mean, median, mode), measures of

variability or spread (range, standard deviation, variance), measures of shape

(kurtosis, and skewness)

2. Frequency with Histogram with Normal curve

3. Kolmogorov-Smirnov, and Shapiro-Wilk Tests

Frequency with Histogram with Normal curve, and Kolmogorov-Smirnov and

Shapiro-Wilk Tests are conducted to check normality of the population. Chi-Square

test is used for hypotheses testing and Spearmans Rho is used for checking existence

of relationship between different variables. Finally, clustered bar charts are displayed

to make cross-comparison among different variables.

4.11. HYPOTHESES TESTING

Hypotheses testing require formulating null hypothesis and alternative hypothesis,

selections of appropriate testing test(s), choice of parametric or nonparametric tests,

and finally making interpretation for decision making.

4.11.1. Selection of statistical tests and techniques for Hypotheses testing

Deciding which statistical technique and tests to use for testing hypotheses depends

upon factors such as defined research problem, research design, sampling design,

scale of measurement used in data collection, distribution of data etc. First step is to

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check whether each variable is to be analyzed in isolation or many variables are to be

analyzed simultaneously. When there is a single measurement in the sample and each

variable is to be analyzed separately, Univariate technique is used, whereas when

more than one variable are to be analyzed simultaneously, Multivariate techniques are

used. Secondly, If measurement of variables are at nominal or ordinal scale

(nonmetric data), nonparametric (distribution free) tests are used. In case

measurement of variables are at interval level at least (metric data), parametric tests

are used. Selection of tests and technique also depends on the fact whether

observations are from one samples or from two samples.

To check normality of the data, histograms, and Kolmogorov-Smirnov (K-S) test are

used. If data are normally distributed then we can assume that the data belong to

normally distributed population. Histograms give overview of whether variables have

bell-shaped symmetrical curve or not. Variables with bell- symmetrical shaped curve

are normally distributed. We can interpret that sample distribution does not deviate

from normal. Though, histograms do not show magnitude of the deviation. Therefore,

Kolmogorov-Smirnov, and Shapiro-Wilk tests are conducted to check whether data

are normally distributed or not. When p value in these tests is greater than 0.05,

then sample distribution is normal. When p value is less than 0.05, then the

distribution is not normal.

4.11.2. Assumptions of Parametric Tests

There are four assumptions of parametric tests:

1. Normally distributed data

2. Data measured at least Interval scale.

3. Independent data

4. Equal Variance

The observations in this research come from one samples. Data are measured at

nominal and ordinal scales. Histograms show variables do not have normality curve.

It is interpreted that sample data are not normally distributed and they do not belong

to normally distributed population. Mean, median, and mode are not identical.

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Further, K-S (Kolmogorov-Smirnov) test, and Shapiro-Wilk tests are used. Results

show that distribution in not normal. Parametric tests are not applicable. Kolmogorov-

Smirnov test is one-sample test. It is goodness of fit test. The comparison is made

between observed and theoretical sample distribution. When calculated value is

greater than the critical value, the null hypothesis is rejected. It is more powerful than

Chi-Square test.

4.11.3. Chi-Square Test ( 2 )

Pearson Chi-Square test is used for testing relationship between two or more

categorical variables. It is widely used nonparametric test to check significance

difference between the observed frequency (fo) and the expected frequency (fe) with

reference to the null hypothesis.

Degree of freedom (df) is important in Chi-Square test. For every degree of freedom,

Chi-Square distribution is different. Degrees of freedom are calculated by multiplying

number of rows minus 1 and number of columns minus 1. For 2 X 2 table (a table

with 2 rows and 2 columns), degree of freedom will be (2 1) X (2 1) = 1.

df = (r 1) X (c 1)

The null hypotheses are formulated in form of there is no significant relationship

between two variables. When the calculated value of Chi-Square statistics is greater

than the critical value, null hypothesis is rejected. Thus, it is interpreted that there is

significant relationship between two variables. Data that are in percentage form must

be converted into absolute number form to calculate Chi-Square statistics.

One of the limitations of the Chi-Square test is it does not indicate strength of

association among the variables. Chi-Square test cannot be conducted if any cell in

the contingency table has frequency of less than 5.

4.11.4. Descriptive Statistics

Descriptive statistics include measures of central tendency, measures of dispersion,

and measures of shape. Mean, Median, and Mode are common measures of location

(measures of central tendency). Variance, Standard Deviation, and Range are parts of

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n measures of spread (measures of dispersion or variability). Skewness and Kurtosis

are measures of shape.

1. Mean

In other words, Means is an arithmetic average. Mean is an average for

interval or ratio scaled data. It is calculated by dividing total of the values of

all observations/items by the total number of observations/items.

2. Median

Median is middle value of midpoint of the data calculated after arranging the

frequencies in ascending or descending order. It is the most appropriate

measure for ordinally scaled data.

3. Mode

Mode is the value occurring most frequently in data. It is also used for

categorical data.

4. Range

Range is calculated as the difference between the highest and the lowest

value among the observations. For ordinal data, range is very important

statistic.

5. Variance

Variance is mean squared deviation from mean. Deviation from the mean is

the difference between the mean and observed value.

6. Standard Deviation

Standard deviation is calculated by taking square-root of the average of

deviations squares. It is positive square-root of variance. It is used for

interval and ratio scaled data.

7. Skewness

If sample data does not have mean, median, and mode at the same point, data

are skewed. Data can be positively or negatively skewed.

8. Kurtosis

Kurtosis is the measure of shape. It tells how flat the sample distribution is. It

can have negative or zero or positive value. A bell-shaped or normal curve is

neither too peaked nor too flat.

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4.11.5. Results and Interpretations of Hypotheses testing:

1. Hypothesis that there is no significant relationship between customers awareness

about Do Not Call Registry and Education and Occupation of the customers is

rejected. There is significant relationship between customers awareness about Do

Not Call Registry and customers education, and occupation.

2. Hypothesis that there is no significant relationship between customers awareness

about Do Not Call Registry and city and gender of the customers is not rejected.

There is no significant relationship between customers awareness about Do Not

Call Registry and customers city, and gender.

3. Hypothesis that there is no significant relationship between disturbance ranks

given to marketing communications by the customers and customers preferences

to stop them is rejected. There is significant relationship between disturbance

ranks given to marketing communications by the customers and customers

preferences to stop communications.

4. Hypothesis that there is no significant relationship between customers finding

marketing communications interesting and finding the marketing communications

useful is rejected. There is significant relationship between customers finding

marketing communications interesting and finding marketing communications

useful.

5. Hypothesis that there is no significant relationship between customers awareness

about Permission Marketing and education, occupation, and city of the customers

is rejected. There is significant relationship between customers awareness about

Permission Marketing and education, occupation, and city of the customers.

4.11.6. Crosstabulation

With the help of Crosstabulation, relationship between two or more categorical

variables can be examined. Crosstabulation tables are also knows as Contingency

Tables. Categorical variables have categories of people, places, objects, situations etc.

For example, Gender, City etc. It is also possible to check various measures of

association such as Chi-Square, Phi, Contingency Coefficient, Cramers V etc. with

the help of Crosstabulation. Chi-Square statistic is used for crosstabulating two or

more variables with nominal categories. Phi Coefficient is used to check strength of

association of 2 X 2 contingency table. Contingency Coefficient is used to check

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strength of association of a contingency table of any number of rows and any number

of columns. Cramers V is used to check strength of association of the tables larger

than 2 X 2 (tables with more than 2 rows and 2 columns). Crosstabulation is done by

taking two or more variables present in the hypotheses formulated.

4.11.7. Correlation

Correlation helps to know linear relationship between variables. Two variables can be

positively related or not related or negatively related. For example, higher the

awareness about the product, higher the interest level in buying (positive relation). It

can be higher the product awareness, lesser the interest in buying (negative relation)

or product awareness and buying interest are independent, there is no change in level

of buying interest with changing product awareness (no relation). If variables have

correlation, it is called that they covary. When one variable deviates from mean and

the other variable also deviates from the mean, these variables are related. Variance of

a variable is a measure of level of data that vary from the mean of those data. When

one variable deviates from mean and the other variable also deviates from the mean in

the same direction, the variance is positive. For example, if interest in the product

increases with the increase in product awareness, variance is positive. When one

variable deviates from mean and the other variable deviates from the mean in the

opposite direction, then variance is negative. For example, interest in the product

decreases with increase in the product awareness.

Variables may have different scales of measurement. Standardization is used when

covariance is used for measuring relationship between the types of variables having

different scales of measurement. Standardization will convert different units into the

same unit of measurement. Covariance value, after standardization, lies between -1

and 1 and is called Pearson product-moment correlation coefficient (r or R). -1 value

says that relationship between variables is a perfect negative, 1 says it is a perfect

positive relationship. 0 shows no relationship between variables.

4.11.8. Spearmans Rho

For nonparametric data, instead of Pearsons correlation Spearmans correlation

coefficient is used. In Spearmans test, data are ranked first, and then Pearsons

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statistic is applied to these ranks. If the significant value is less than 0.05, then it is

interpreted that a significant relationship between two variables exists. If the value is

more than 0.05, then there is no significant relationship between two variables. If the

correlation is positive, then change in one variable result in the change of the other

variable in the same direction.

4.11.9. Phi

Phi is used for 2 X 2 contingency tables to measure the strength of association. Phi

coefficient is calculated as below:

When value of Phi is zero, there is no any association between variables. When this

value is 1, there is perfect association between the variables.

4.11.10. Contingency Coefficient

Contingency Coefficient (C) measures strength of association between variables for

the table of any size. Unlike Phi coefficient, it is not limited for 2 X 2 table only. The

value of this statistic lies between 0 and 1. 0 indicates no association. Contingency

coefficient is calculated as below:

\

4.11.11. Cramers V

Cramers V is used for the tables with more than two rows and more than two

columns. The larger the value of V, the higher the association. Cramers V is

calculated as below:

2

V = min (r 1), (c - 1)

77

Chapter Bibliography

Books

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New Delhi, 1999.

5. Creswell John, Research Design, 2nd ed, SAGE Publications. California, 2003.

6. Kothari C R, Research Methodology, 2nd ed, New Age International Publishers,

Delhi, 2004.

7. Krishnaswami O P and Ranganatham M, Methodology of Research in Social

Sciences, 2nd ed, Himalaya Publishing House, Mumbai, 2006.

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9. Luck David J and Rubin Ronald S, Marketing Research, 7th ed, PHI, New Delhi,

2006.

10. Malhotra Naresh, Marketing Research: an Applied Orientation, 5th ed, Pearson

Education, New Delhi, 2007.

11. Oliver Paul, Writing your thesis, SAGE, 2004.

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Implementation & Control, 3rd ed, MacMillan India Ltd., 2005.

13. Teitelbaum Harry, How to write a thesis, 5th ed, Thomson Arco, 2003.