3. Research Methodology-Data Collection, Statistical...

Research Methodology

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3. Research Methodology-Data Collection, Statistical Analysis

This chapter includes:

Type of study, Population and Sampling technique, Power analysis Instrumentation-Design of questionnaire Descriptive statistics and distributions Factor Analysis of measuring tools Reliability and Validity of measuring tools Discriminant Analysis-Steps followed to arrive at the final DA model Structural Equation Modeling-Confirmatory Factor Analysis and fit indexes for

all variables

Type of study-The study is descriptive and cross-sectional in nature using the survey

design.

Population

There are around 0.35 million credit card users in Kochi Municipal Corporation,

Kerala (Total of 1.5million people). These cards are offered by 28 different national

and international banks together having 199 different types of product offerings.

Sample frame and Sampling technique

A quota sample of 550 respondents is selected from among those who are using the

credit cards for a period greater than one year. This method is identified by the

researcher as a credible way of tracking credit card spending and financing patterns as

the respondents have completed a few billing cycles in the said period. Prior to this a

pilot study was conducted among 200 credit card users to measure their responses

and to validate the questionnaire. The number of final respondents from whom data

collection was done is 550.The quota sampling is a non-probability sampling technique

which can involve the judgement of the researcher as well to ensure quality of the


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responses. Non probability sample can be relevant for research to the extent that it

possesses the essential person and setting characteristics that define membership in

the intended target population (Sackett & Larsen, 1998)

Power Analysis: Power analysis is carried out a priori, during the design stage of the

study. A study can conclude that a null hypothesis was true or false. The real answer,

the state of the world, can be that the hypothesis is true or false. Given the three

factors alpha, sample size and effect size, beta can be calculated. Alpha is the

probability of type I error (rejection of a correct null hypothesis). Beta is the

probability of type II error (acceptance of a false null hypothesis). The probability of

correctly accepting the null hypothesis is equal to 1-α, which is fixed the probability of

incorrectly rejecting the null hypothesis, is β. The probability of correctly rejecting the

null hypothesis is equal to 1-β, which is the power. The sample size required by power

analysis for DA is 128 (.1 error margin) and for SEM is 468 (.2 error margin) (Cohen,

1989). These respective sample sizes were giving an 80 % or more power figure.

Sample Adequacy Test: For Discriminant Analysis (DA):

Discriminant Analysis (DA) is relatively robust even when there is modest violation of

equality of covariance assumption (Lachenbruch, 1975). The dichotomous variable,

which often violate multivariate normality, are not likely to affect conclusions based on

DA (Klecka, 1980).The condition that the smallest group in the DA should have at least

five times the total number of variables used in the study is fulfilled. The smallest

group Credit non-defaulters has 61 respondents more than twice the required sample

size of 25 (Illustration- One dependent variable and four predictor variables * Five

times=25).


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Sample Adequacy Test: For Structural Equation Modeling (SEM):

Hoelters critical N is used to judge if sample size is adequate or not in SEM. The

Hoelters N is 313 (at.05 level) and 332 (at.01 level) which is more than sufficient to

accept a model by chi-square (Schumacker & Lomax, 2004) (table 41). A sample size

greater than 500 is recommended to produce more robust model fit indexes (Lei, Ming

& Lomax Richard G, 2005).

Tools of Analysis- Two tests of normality viz; Kolmogorov-Smirnov (K-S) and Shapiro-

Wilk, Factor Analysis, Discriminant Analysis (DA) and Structural Equation Modeling

(SEM).

Instrumentation- Design of Questionnaire

Materialism (MAT) has 5 items measured on a 5 point Likert scale from strongly

disagree to strongly agree. Those who score three neither agree nor disagree to

materialistic nature. The scale was adopted from James Carl Stone IV (2001), Oklahoma

State University for reduced number of items. The original scale which was developed

by Richins & Dawson (1992) was abridged for more number of statements without

compromising the content validity. The following are the statements in the

questionnaire. (a) I enjoy buying expensive things (b) My possessions are important for

my happiness (c) I like to own nice things more than most people in my immediate and

comparable vicinity (d) Acquiring valuable things is important to me (e) I enjoy owning

luxury items. To avoid measurement error, in the beginning of the questionnaire it has

been quoted that respondents need to indicate what they do in daily life and not what

they think about.


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The Compulsive Buying (CBB) has 9 items measured on a 5 point scale from never to

very often. Those who score three are sometimes compulsive buyers. The following are

the statements in the questionnaire (a) I have bought things that I could not really

afford (b) I bought something to make myself feel better (c) I felt depressed after

shopping (d) I have gone on a buying spree without being able to stop (e) I bought

something and when I got home I wasn’t sure why I bought it (f) I felt anxious on days I

didn’t do shopping (g) I buy things simply because they are on sale (h) I just wanted to

buy and didn’t care what I bought (i) I have felt that others would be horrified if they

knew about my spending habits. The scale was adopted from James Carl Stone IV,

(2001) Oklahoma State University. The above said scale itself was adapted from

O’Guinn & Faber compulsive buying screener scale (1992). To avoid measurement

error, in the beginning of the questionnaire, it has been stated that respondents need

to indicate how frequently they engage in the following in daily life and not how they

wish they could.

Enhanced Credit Card Spending (ECCS) has 5 items measured on a five point Likert

scale from strongly disagree to strongly agree. Those who score three neither agree nor

disagree to be enhanced credit card spenders. The following are the statements in the

questionnaire (a) I end up buying more due to the possession of a credit card (b) When

I shop with credit card(s), I tend to make unplanned purchases (c) It is easy for me to

overspend when I shop in the presence of a credit card (d) Without a credit card, my

spending habits would not be different (e) If I did not have a credit card, I would

probably spend less. The scale was adopted from the credit card consumption scale

(Sahni, 1995). The fourth statement affected the overall scale reliability and hence was

removed. This does not affect the content validity as the fifth item is of similar nature.


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Credit Card Financing Behaviour (CCFB) has 6 items measured on a five point Likert

scale from strongly disagree to strongly agree. Those who score three neither agree nor

disagree to have credit card financing behaviour. The following are the statements in

the questionnaire (a) I exhaust the credit limit on my credit card(s) (b) When

purchasing I have been told that I have spent beyond the credit limit (c) The way I use

my credit card I always have enough credit (d) I manage bills in an effort to make

payments on my credit cards (e) I pay credit card bills after their due dates (f)

Creditors have threatened to cancel my credit cards. The scale was adopted from the

Credit card consumption scale (Sahni, 1995), which is originally adapted from Edwards

(1992). The fourth item was deleted as it affected the overall scale reliability. The

deleted item does not interfere with content validity due to the fact that there are

statements cross checking one’s ability to pay bills on time.

Credit Default Probability

A debt know how quiz (Master Card, 2006) is administered to measure the dependent

variable Credit default probability (CD). The set of questions were pertaining to credit

card bills and payment management patterns. The statements asked are (a) Do you

avoid looking at your bills and credit card balances? (b) Do you usually pay only the

minimum on your credit cards? (c) Do you sometimes pay your bills late or miss

payments entirely? (d) Do you use credit cards and store credit to make purchases

because you don’t have the money to pay for them at the time? (e) Is your paycheck

already spent before you receive it? (f) Do you choose the longest allowable payment

period or installment plan to make major purchases- for example, a car or major

appliance-affordable? (g) Have you taken out a home loan to pay down your debt and

already run up new consumer debts? (h) Do payments on your debt account for more


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than 20 percent of your household take-home pay each month (excluding your

mortgage or rent payment)? (i) Do you have savings to fall back on if something

unexpected happens, such as a car repair or medical emergency? (j) Do you spend

more time worrying about your bills than paying them? The questionnaire has a

nominal scale and the answer gets accumulated as points indicating probability of

credit default. All ten questions have a yes or no choice. In all questions, the option yes

has a score of one and no has a zero score. In question number nine option yes has a

zero score and if no a score of one. If all answers are yes then the total score is nine

and if all answers no then the total score is one. A score of zero means excellent, as

the credit default probability is nil. A score of one to four (both inclusive) indicates a

potential defaulters (early warning stage) and a score of five to ten (both inclusive)

indicates high on credit default probability. The respondents after their categorisation

fall into three groups, viz non-defaulters who are 61 in number, the potential

defaulters (early warning stage) are 364 and the defaulters are 125.This allocation is

used as part of the descriptive interpretation. In order to use Structural Equation

Modeling using AMOS 7, the dependent credit default was taken as a single item

measure by summing up the actual score of each respondent from zero to ten. The

higher the score the greater is the probability to default. The validity and reliability of

the single-item summated score from ten nominal questions is appropriate (Wanous &

Reichers, 1997).The dependent credit default score (metric) is used for further analysis

using SEM.

All the questionnaires were administered in English. The quota for target respondents

were identified by closed group networking using judgement. The business

management students of Rajagiri School of Management, Kochi were asked to identify

at least ten respondents in their family and friends circle who were voluntary and


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willing to cooperate with the study. In case of a few respondents there were some

clarifications regarding the questions and the student volunteers were more than

helpful in providing the same.

Procedure of data analysis and time frame:

A combination of descriptive (mean, standard deviation, distribution tests), and

inferential (t-test, Factor Analysis, Discriminant Analysis and SEM) analysis is

undertaken. The respondents of the pilot study were met from January 2007 to March

2008. The respondents of the final study were met from April 2008 to April 2010.

Analysis Plan:

Descriptive Statistics and Distributions:

The data was screened for the following defects.

1. Incorrect data entry and out-of range values and such errors were found absent.

2. Missing values were rectified.

3.Outliers-The composite score of the predictors (as averaged items reduces the probability of

outliers) viz, Compulsive Buying(CBB), Materialism (MAT), Enhanced Credit Card

Spending (ECCS) and Credit Card Financing Behaviour (CCFB) were filtered using the

new standardized scores as a condition and values outside -3 and 3 were identified.

There were six such cases arising due to CBB scores (Z score-CBB) and 2 cases arising

out of CCFB (Z score-CCFB) scores.

The Histograms, Box plots, skewness, kurtosis and normal Q-Q plots (observed values and

expected values) for each composite predictor variable was done separately with 542

samples and 550 samples and it was found that the identified eight cases was not

effecting the results substantially. If the respondent (subject) chose to respond with a

particular value, then that data was a reflection of reality and so removing the so


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called outliers is an antithesis of research, and hence the final sample was retained as

550 itself.

As a single method does not speak of all possible facets of normality in a given

sample, this was further followed by two tests of normality, Kolmogorov-Smirnov (K-

S) when sample was greater than 50 and Shapiro-Wilk when sample was smaller than

50 (when sub-groups of predictor variables were created based on dependent credit default categories). It

was found that there is not great difference in normality assumption when the whole

550 sample was divided based on categories generated by the dependent variable

credit default. Two separate categories of dependent variable, one for Discriminant

data set and other for SEM data set were used to generate separate K-S and S-W tests

(See table 5, 6, 7, 8). One limitation of the normality tests is that the larger the sample

size, the more likely to get significant results (p<.05, indicating non-normality). So a

slight deviation from normality will result in significance (p<.05) when sample is large.

This need not be an absolute deviation from normality (See table 5,6,7,8).The

researcher as said above has used various methods to check for normality and found

conforming (See Normal Q-Q Plot and descriptive of Predictors)(figure 4,5,6,7)(table 9).

In the normal Q-Q plot the black line indicates the values the sample should adhere to

if the distribution is normal. The dots are the actual data. In the descriptive table 9, the

5 % trimmed mean indicates the mean value after removing the top and bottom 5% of

scores. The skewness and kurtosis are zero for a normal distribution. The values

within +1 and -1 range are acceptable ranges for nearly fitting normal distributions.

This was cross checked with the sample of 542 after dividing them based on the

dependent variable category and still no significant difference existed.


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Separate Discriminant analysis and Structural Equation Modeling was done with

outliers eliminated (sample of 542) and not excluding the outliers (sample of 550). The

results were slightly better for Discriminant Analysis with a sample size of 550 and for

Structural Equation Modeling there was no difference either with 542 or 550 samples.

Hence the final distribution is close to normal without eliminating the outliers and the

final sample used in analysis is 550.

Table: 5

Tests of Normality(b) of Predictor Variables based on Credit Default Scores used in SEM Analysis

Predictor Category based on actual Kolmogorov-Smirnov(a) Type of

Variables default score (dependent) Statistic Sample size (df) Sig. Distribution

CBB

0.00 0.11 61.00 0.04 Not normal

1.00 0.12 115.00 0.00 Not normal

2.00 0.08 96.00 0.14 Normal

3.00 0.09 70.00 0.20 Normal

4.00 0.13 83.00 0.00 Not normal

5.00 0.09 52.00 0.20 Normal

6.00 0.08 25.00 0.20 Normal

7.00 0.11 19.00 0.20 Normal

8.00 0.16 23.00 0.12 Normal

9.00 0.16 4.00 .

10.00 0.26 2.00 .

MAT

0.00 0.14 61.00 0.01 Not normal

1.00 0.09 115.00 0.02 Not normal

2.00 0.09 96.00 0.05 Normal

3.00 0.12 70.00 0.01 Not normal

4.00 0.12 83.00 0.01 Not normal

5.00 0.15 52.00 0.00 Not normal

6.00 0.13 25.00 0.20 Normal

7.00 0.14 19.00 0.20 Normal

8.00 0.12 23.00 0.20 Normal

9.00 0.23 4.00 .

10.00 0.26 2.00 .

ECCS

0.00 0.15 61.00 0.00 Not normal

1.00 0.13 115.00 0.00 Not normal

2.00 0.13 96.00 0.00 Not normal

3.00 0.18 70.00 0.00 Not normal


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4.00 0.18 83.00 0.00 Not normal

5.00 0.12 52.00 0.05 Normal

6.00 0.13 25.00 0.20 Normal

7.00 0.16 19.00 0.19 Normal

8.00 0.14 23.00 0.20 Normal

9.00 0.24 4.00 .

10.00 0.26 2.00 .

CCFB

0.00 0.13 61.00 0.02 Not normal

1.00 0.12 115.00 0.00 Not normal

2.00 0.14 96.00 0.00 Not normal

3.00 0.17 70.00 0.00 Not normal

4.00 0.15 83.00 0.00 Not normal

5.00 0.10 52.00 0.20 Normal

6.00 0.13 25.00 0.20 Normal

7.00 0.20 19.00 0.04 Not normal

8.00 0.16 23.00 0.13 Normal

9.00 0.29 4.00 .

*This is a lower bound of the true significance.

a-Lilliefors Significance Correction

b-CCFB is constant when MCSUM = 10.00. It has been omitted.

K-S test used when sample greater than 50

S-W test used when sample smaller than 50

Table: 6

Tests of Normality(b) of Predictor Variables based on Credit Default Scores used in SEM Analysis

Predictor Category based on actual Shapiro-Wilk Type of

Variables default score (dependent) Statistic Sample size (df) Sig. Distribution

CBB

0.00 0.96 61.00 0.03 Not normal

1.00 0.94 115.00 0.00 Not normal

2.00 0.98 96.00 0.08 Normal

3.00 0.91 70.00 0.00 Not normal

4.00 0.96 83.00 0.01 Not normal

5.00 0.97 52.00 0.19 Normal

6.00 0.99 25.00 1.00 Normal

7.00 0.99 19.00 0.99 Normal

8.00 0.92 23.00 0.08 Normal

9.00 0.99 4.00 0.95 Normal

10.00

MAT

0.00 0.96 61.00 0.07 Normal

1.00 0.97 115.00 0.02 Not normal


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2.00 0.97 96.00 0.04 Not normal

3.00 0.96 70.00 0.05 Normal

4.00 0.97 83.00 0.08 Normal

5.00 0.95 52.00 0.04 Not normal

6.00 0.97 25.00 0.70 Normal

7.00 0.94 19.00 0.22 Normal

8.00 0.96 23.00 0.39 Normal

9.00 0.89 4.00 0.40 Normal

10.00

ECCS

0.00 0.95 61.00 0.02 Not normal

1.00 0.95 115.00 0.00 Not normal

2.00 0.95 96.00 0.00 Not normal

3.00 0.92 70.00 0.00 Not normal

4.00 0.94 83.00 0.00 Not normal

5.00 0.95 52.00 0.02 Not normal

6.00 0.96 25.00 0.41 Normal

7.00 0.94 19.00 0.22 Normal

8.00 0.91 23.00 0.03 Not normal

9.00 0.91 4.00 0.49 Normal

10.00

CCFB

0.00 0.92 61.00 0.00 Not normal

1.00 0.91 115.00 0.00 Not normal

2.00 0.93 96.00 0.00 Not normal

3.00 0.95 70.00 0.01 Not normal

4.00 0.97 83.00 0.04 Not normal

5.00 0.97 52.00 0.23 Normal

6.00 0.95 25.00 0.20 Normal

7.00 0.91 19.00 0.07 Normal

8.00 0.96 23.00 0.44 Normal

9.00 0.89 4.00 0.37 Normal


b-CCFB is constant when MCSUM = 10.00. It has been omitted.

K-S test used when sample greater than 50

S-W test used when sample smaller than 50


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Table: 7

Tests of Normality(b) based on Credit Default Category as used in Discriminant Analysis

Predictors Credit default category(dependent) Kolmogorov-Smirnov(a) Type of

Statistic Sample size(df) Sig. Distribution

CBB

Non-defaulter 0.11 61.00 0.04 Not normal

Potential defaulters (early warning stage) 0.11 364.00 0.00 Not normal

Defaulter 0.06 125.00 0.20 Normal

MAT



Defaulter 0.10 125.00 0.01 Not normal

ECCS




CCFB





a-Lilliefors Significance Correction

Table: 8

Tests of Normality(b) based on Credit Default Category as used in Discriminant Analysis

Predictors Credit default category(dependent)

Shapiro-Wilk Type of

Statistic Sample size(df) Sig. Distribution

CBB



Defaulter 0.99 125.00 0.50 Normal

MAT

Non-defaulter 0.96 61.00 0.07 Normal



ECCS




CCFB



Defaulter 0.98 125.00 0.10 Normal *This is a lower bound of the true significance.


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Table: 9

Descriptive Statistics for the Predictor Variables

Statistic Std. Error

CBB Composite

Mean 2.14 0.03

95% Confidence Interval for Mean Lower Bound 2.08

Upper Bound 2.21

5% Trimmed Mean 2.10

Std. Deviation 0.78

Skewness 0.91 0.10

Kurtosis 0.70 0.21

MAT Composite

Mean 2.96 0.04


Upper Bound 3.03


Std. Deviation 0.86

Skewness -0.04 0.10

Kurtosis -0.30 0.21

ECCS Composite

Mean 3.20 0.04


Upper Bound 3.28


Std. Deviation 0.99

Skewness -0.38 0.10

Kurtosis -0.67 0.21

CCFB Composite

Mean 2.00 0.03


Upper Bound 2.06


Std. Deviation 0.72

Skewness 0.65 0.10

Kurtosis -0.11 0.21


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Figure: 4

Normal Q-Q Plot of CBB


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Figure: 5

Normal Q-Q Plot of MAT


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Figure: 6

Normal Q-Q Plot of ECCS


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Figure: 7

Normal Q-Q Plot of CCFB

Factor Analysis of the measuring tools

The response error or systematic error includes researcher error, interviewer error and

respondent error. These errors can affect the scale reliability and vice versa. The

researcher error arises due to population definition error, measurement error and

surrogate information error. Further, Interviewer error arises due to response selection

error, questioning error, recording error and cheating error. The respondent error

arises due to inability error and unwillingness error. The systemic error is minimized

by ensuring good quality measuring tool and good implementation.


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The random error or noise is taken care by large sample size. The reliability of the

scale is one of the measures of the quality of the tool. The reliability of the scale is

affected by researcher error and interviewer error. The scale items with maximum

Cronbachs Alpha value for each variable are subjected to factor analysis, to confirm

the validity of the existing standardised scale.

Factor Analysis is used to cross validate the existing standardised questionnaire. At

the early stage the attempt is to eliminate any variables that don’t correlate with any

other variables or that correlate very highly with other variables (R>0.9).

As part of the final study the questionnaires as modified by the results of the pilot

study had a total of 25 items spread across four predictor variables. One statement

(indicator) from enhanced credit card spending behaviour scale and one statement

from credit card financing behaviour scale was removed as reported earlier as a

preliminary analysis using scale reliability showed that these two respective items were

reducing the overall scale reliability. The total items across four predictor variables are

23. The dependent variable credit default being measured on a nominal scale was not

subject to factor.

A re-factor analysis is done using 23 items across four predictor variables

viz, materialism (5 items), compulsive buying (9 items), enhanced credit card spends (4

items), credit card financing behaviour (5 items). The appropriateness of factor

analysis model with the given data or whether the data were suitable for conducting

factor analysis was tested using Kaiser-Meyer-Olkin measure of sampling adequacy

and Bartlett’s test of Sphericity. The KMO static varies between 0 and 1. A value close

to 1 indicates that the patterns of correlations are relatively compact and so factor

analysis should yield distinct and reliable factors. Values above 0.9 are excellent


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(Hutcheson & Sofroniou, 1999).A significant test tells us that the R-matrix is not an

identity matrix; therefore there are some relationships between the variables we hope

to include in the analysis. Therefore factor analysis is appropriate (table 10).

Table: 10 KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.93

Bartlett's Test of Sphericity Approx. Chi-Square 5935.13

df 253.00

Sig. 0.00

Looking at the correlation matrix the correlation values are less than or equal to 0.65

and in majority (but 6 inter-correlation values were insignificant (p>0.05) of the cases the

correlations are significant (p<0.05).The determinant of the correlation matrix is

greater than the necessary value of 0.00001, the reported value is 0.000017 indicating

that multi-collinearity is not a problem for these data.

The factors extracted were four (23 statements belonging to four predictor variables) whose

Eigen values were greater than 1.The average communalities of 23 statements (of four

predictor variables) were 0.60 which is a good value when sample size exceeds

250.Hence all the factors were retained using Kaiser’s criterion (table 11).


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Table: 11

Communalities

Communalities

Initial Extraction

CB1 1.00 0.56

CB2 1.00 0.59

CB3 1.00 0.34

CB4 1.00 0.61

CB5 1.00 0.60

CB6 1.00 0.65

CB7 1.00 0.64

CB8 1.00 0.46

CB9 1.00 0.61

Mat 1 1.00 0.59

Mat 2 1.00 0.58

Mat 3 1.00 0.58

Mat 4 1.00 0.61

Mat 5 1.00 0.64

ECCS1 1.00 0.75

ECCS2 1.00 0.72

ECCS3 1.00 0.73

ECCS5 1.00 0.56

CCFB1 1.00 0.61

CCFB2 1.00 0.71

CCFB3 R 1.00 0.39

CCFB5 1.00 0.61

CCFB6 1.00 0.48

Extraction Method: Principal Component Analysis.

The rotated component matrix is clearly loading into four factor which are

materialism (5 items),compulsive buying ( 9 items) ,enhanced credit card spending ( 4

items) and credit card financing behaviour (5 items).The varimax rotation was adopted

as the four factors were considered theoretically independent (table 12).


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Table: 12

Rotated Component Matrix

Rotated Component Matrix(a)

Component

1.00 2.00 3.00 4.00

CB7 0.75

CB6 0.75

CB9 0.72

CB4 0.72

CB5 0.71

CB2 0.70

CB1 0.65

CB8 0.65

CB3 0.42

Mat 5 0.77

Mat 2 0.74

Mat 4 0.74

Mat 3 0.74

Mat 1 0.69

ECCS3 0.82

ECCS1 0.81

ECCS2 0.79

ECCS5 0.73

CCFB5 0.75

CCFB2 0.73

CCFB6 0.67

CCFB3 R 0.61

CCFB1 0.61


a-Rotation converged in 5 iterations.

Rotation Method: Varimax with Kaiser Normalization.


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Figure: 8

Component Plot in Rotated Space

ECCS3ECCS2ECCS1

ECCS5

CB1

CB2CB5CB4CCFB1

Mat1CB3

CCFB2

Mat4Mat5Mat2 Mat3

CCFB5CCFB6CCFB3R

Component 31.0 0.5 0.0 -0.5 -1.0

Com

pone

nt 2

1.0

0.5

0.0

-0.5

-1.0

Component 11.00.50.0-0.5-1.0

Component Plot in Rotated Space

The factors identified by factor analysis are subject to reliability analysis and their

values are reported below. In the reliability statistics, the Cronbachs Alpha of a scale


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for all the included variables are greater than 0.7, as recommended by J.C. Nunnelly

(1978). Hence the constructs can be used together as a scale (table 13).

Table: 13

Number of items in each scale and their Cronbach Alpha Value

Scales No of items after deletion to

enhance Alpha Cronbach on

standardised items

Compulsive Buying (CBB) 9 0.894

Materialism (MAT) 5 0.836

Enhanced Credit Card Spending (ECCS)

4 0.852

Credit Card Financing Behaviour (CCFB)

5 0.776

Table: 14

Inter-Item Correlation Matrix for Compulsive Buying Scale

CB1 CB2 CB3 CB4 CB5 CB6 CB7 CB8 CB9

CB1 1.00 0.66 0.42 0.52 0.45 0.49 0.48 0.42 0.52

CB2 0.66 1.00 0.42 0.56 0.48 0.52 0.54 0.43 0.52

CB3 0.42 0.42 1.00 0.38 0.31 0.28 0.34 0.21 0.36

CB4 0.52 0.56 0.38 1.00 0.57 0.61 0.56 0.40 0.53

CB5 0.45 0.48 0.31 0.57 1.00 0.62 0.63 0.43 0.57

CB6 0.49 0.52 0.28 0.61 0.62 1.00 0.58 0.49 0.59

CB7 0.48 0.54 0.34 0.56 0.63 0.58 1.00 0.49 0.56

CB8 0.42 0.43 0.21 0.40 0.43 0.49 0.49 1.00 0.47

CB9 0.52 0.52 0.36 0.53 0.57 0.59 0.56 0.47 1.00


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Table: 15

Inter-Item Correlation Matrix for Materialism Scale

Mat 1 Mat 2 Mat 3 Mat 4 Mat 5

Mat 1 1.00 0.45 0.47 0.50 0.62

Mat 2 0.45 1.00 0.48 0.52 0.46

Mat 3 0.47 0.48 1.00 0.51 0.49

Mat 4 0.50 0.52 0.51 1.00 0.56

Mat 5 0.62 0.46 0.49 0.56 1.00

Table: 16

Inter-Item Correlation Matrix for ECCS scale

ECCS1 ECCS2 ECCS3 ECCS5

ECCS1 1.00 0.70 0.66 0.49

ECCS2 0.70 1.00 0.65 0.48

ECCS3 0.66 0.65 1.00 0.55

ECCS5 0.49 0.48 0.55 1.00

Table: 17

Inter-Item Correlation Matrix for CCFB scale

CCFB1 CCFB2 CCFB3 R CCFB5 CCFB6

CCFB1 1.00 0.65 0.35 0.45 0.30

CCFB2 0.65 1.00 0.40 0.54 0.41

CCFB3 R 0.35 0.40 1.00 0.28 0.22

CCFB5 0.45 0.54 0.28 1.00 0.47

CCFB6 0.30 0.41 0.22 0.47 1.00

Reliability –Test Retest, Parallel Form, and Internal Consistency

1. Test-retest – To ensure stability over a period of time and to negate the mood of the

respondent a test-retest is done, by going back to a few respondents who were part of

the pilot study. 40 respondents who were from Kochi Municipal Corporation limits

(Thripunithura, Kakkanad, Edapally, and Ernakulam) were met. Their original


127

responses were compared to the new set of responses and found conforming. This

reposes the measuring capability and consistency of the tool.

2. Parallel form- 2 different measures involved (2 forms used) to check for reliability,

particularly for the variable compulsive buying as the confirmatory factor analysis at

the pilot study ,showed some form of inconsistency in the compulsive buying and

impulsiveness scale (CBIS or CB) (nine items), developed by James Carl Stone, 2001,

Oklahoma State University. Compulsive buying impulsive scale was complimented with

compulsive buying screener of Faber & O’Guinn (1992) so as to check for the parallel

form consistency. The CBIS scale with 550 respondents in the final study was re-

subjected to factor analysis as reported. The scale items were grouping into a single

factor in rotated component matrix analysis. The CBIS has also a reported reliability

Cronbachs alpha at 0.894.The compulsive buying trait was also measured with the

Faber scale. The Faber scale reported reliability is 0.836 (Cronbach Alpha). The

percentage of compulsive buyers identified by CBIS scale is 14.54% of respondents (out

of 550) and the percentage of compulsive buyers identified by Faber scale is 13.93 % of

respondents (out of 359). Among the common 359 respondents for both the scales, 58

respondents were classified as compulsive buyers by both the scales (CBIS and Faber

scale). Of the 58 respondents it was found that 44 respondents (75.86%) were classified

as compulsive buyers by Faber scale and the CBIS scale classified all 58 as compulsive

buyers. This is evidently conforming the parallel form reliability of the compulsive

buying tool.

Two different measures of compulsive buying (CBIS and Faber) were having negative

correlations between them in the correlation matrix because, the CBIS scale was

positive in nature and the Faber scale was negative in nature. The determinant of the


128

matrix was greater than 0.00001 reported at 0.0000128. Both the scale items were

converging into a single factor in the direct oblimin rotation. The direct oblimin

rotation was used as the CBIS scale and Faber scale are both measuring the same

variable (table 18).

Table: 18 Pattern Matrix for CBIS and Faber Scale

Pattern Matrix(a) for CBIS and Faber Scale

Checking for Convergent validity

Component

1 2

CB6 0.9445

FCBB6R -0.8553

CB5 0.81503

CB4 0.80031

FCBB4R -0.7607

CB7 0.75417

CB9 0.66724

FCBB1R -0.6527

CB1 0.62399

CB8 0.62381

CB2 0.60981

FCBB3R -0.5667

FCBB2R -0.5194

FCBB5R 0.8621

CB3 -0.832

FCBB7R


Rotation Method: Oblimin with Kaiser Normalization.

a-Rotation converged in 6 iterations.

3. Internal consistency- It is the most important method, when large numbers of items

are there, aiding to look for suspect testing effect. Factor analysis and t-test for

elimination is done. No indicator (statements) was having high correlations values of

0.8 or 0.9. In all four predictors the inter-item correlation between was sufficient to


129

bring in high reliability (Cronbach Alpha values) (see inter-item correlation tables 14 -

17 and Cronbach alpha table 13).

4. Average inter-item correlation- The language and scale correction is done to ensure

an inter item correlation between all the statements in a variable to the level so as to

ensure high Cronbachs Alpha value. Item total correlation (at random) of odd -even

items was analyzed to check for consistency. The statements (factors) in each

predictor variable ensured face and content validity.

5. Cronbach Alpha- It exposes all possible split. Above .9 is undesirable, and in none

of the measuring instruments the Cronbachs Alpha is above 0.90 in the final study

(table 13).

6. Training of the data collector was important and was ensured, before final data

collection. The data collectors were a group of marketing research students at Rajagiri

School of Management. Their training has ensured minimum interviewer error like

response selection error, questioning error, recording error and cheating error

.The respondent error like inability error and unwillingness error was also minimized.

Construct Validity-Convergent, Discriminant

Construct validity-Convergent validity is indicated by high correlations in the different

items of the same concept using different method of measurement. This was not

achievable as the process of tracking a compulsive buyer as explicitly reported in the

Diagnostic Criteria for Compulsive Buying from McElroy et al (1994) was difficult and

beyond the scope of the study. It was also not possible for variables materialism,

enhanced credit card spending and credit card financing behaviour.


130

In the dependent variable case- A debt know how quiz of Master Card was

administered to find the credit default probability of the respondents. This is a

nominal scale with ten items, the answer gets accumulated as points indicating

probability of credit default. All ten questions have a Yes or No choice. In Question

number nine if yes then zero score and if No then a score of one. In all other questions

yes has one score and No has zero score. If all answers are yes then total score is nine

and if all answers no then total score is one.

Zero score means – excellent, as the credit default probability is nil.

Score of 1-4 means- Potential defaulters (early warning stage).

Score of 5- 10 means- High credit default probability.

As in this self response form, its very delicate and subtle to classify a respondent as

non defaulter (zero score), Potential Defaulters (Early Warning Stage) (score of 1 to 4),

and high defaulter (score of 5 to 10). Therefore convergent validity was to be looked

into by finding out the actual credit default status of a deliberately chosen respondent

set from the banks/financial institution (Initial proposed number was 275). This was

attempted at the sample design stage by selecting a known group of defaulters from

banks. The researcher tried to get the help from various bank officials to disclose their

list of defaulters. Many banks were curious at the discussion stage but when it came to

actual disclosure of list of defaulters or even helping to indirectly collect data from

their defaulters through their own recovery executives, they were reluctant.

Discriminant validity-The different concepts, i.e., four predictor variables measured

in the study using the same method of survey design should have low correlation

showing that these constructs are different and discriminated. The four factors


131

(predictor variables under study) are orthogonal (unrelated). Four factors clearly

emerged as seen in the factor analysis (table 12). The second type of discriminant

validity using a different method of measurement of the five variables understudy

(four predictor and one criterion) could not be done for the paucity of time, and scope

being beyond the researchers cost considerations.

Content validity

The tools of measurement are standardised and already used elsewhere. The tools

were developed after profound focus group idea generation and hence can rate

statements of respondents to agree or disagree on a scale. Further, calculating the

mean of ranking across questions by different judges, ensure the content validity

which was already done as part of standardised questionnaire development. The

literature review also ensures that the tools are in line to the available body of

knowledge in the concerned domains.

External validity

External validity is the degree to which the conclusions in the study would hold for

other persons in other places and at other times. This is ensured as due to the above

mentioned steps as well as a scientific sample design as reported in the present

chapter. The present study undertaken by the researcher is done in a different, distinct

and contrasting cultural setting , the typical India specific culture viz-a-viz the typical

American or European cultures where near similar studies were done in the past .At

the same time the similarity between the Indian economy, a fast developing globalised

competitive economy, versus the European and American, which are post-modern

globalised developed economies are many like the new retail environment, credit card

availability and usage, changing family structure and the work culture, their spending


132

pattern, attitude to consumerism and their impact on the social milieu are worth

mentioning. The study in the present form and its finding would have a standing

impact on the already available body of knowledge, their inter-domain relationships

and generalisability.

Conclusion validity

Conclusion validity- is the degree to which conclusions we reach about relationships

in our data are reasonable and believable due the rigor in the process of research as

mentioned above.

Discriminant Analysis

The dependent variable is non-metric, accumulated to a score based on which the

sample is categorised as credit non-defaulters, potential defaulters (early warning

stage) and defaulters. The respondents with a score of zero are credit non-defaulters,

and between one to four (both inclusive) are potential defaulters (early warning stage).

Those with a score between five and ten (both inclusive) are defaulters. The number of

credit non-defaulters is 61, potential defaulters (early warning stage) are 364 and of

defaulters are 125. A debt know-how quiz of Master Card was administered to find the

credit default probability of the respondents. A baseline discriminant analysis, using

step-wise method is completed. The original group cases, correctly classified are 52.7

percent. The cross-validated grouped cases, correctly classified are 51.8 percent. The

predictor variables are normally distributed (table 7, 8 & 9, explanation as in figure 4

to 7) and the Mahalanobis D square to the most likely group is not greater than the

critical value of 13.28 (sig .01 & df 4 with four independent variables), but for thirteen

cases. A separate DA was run eliminating the 13 cases and using the within group

covariance matrix. The original group classified is 50.8% and the cross-validated


133

grouped cases correctly classified are 50.1%. Since the Box’s M test of equality of

covariance matrices is significant (P<.05), the DA was re-run using the separate group

covariance matrix. The original group correctly classified is only 49.5% which is not

more than 2 % of 50.8% when DA was run using within group covariance matrix (target

value was 51.82% in separate group covariance analysis). The researcher decided to

proceed further analysis with the full sample of 550 without eliminating the 13

outliers as the original group classified is 52.7% and cross validated grouped cases

correctly classified at 51.8 %.

In the DA using 550 samples, in the tests of equality of group means, the F value

shows significance (p<.05) for the discriminant model as a whole rejecting the null

hypothesis that the predictors have no impact in categorizing the dependent outcome.

But further results of discriminant analysis include 3 variables viz; enhanced credit

card spends (ECCS), credit card financing behaviour (CCFB) and compulsive buying

behaviour (CBB). The predictor variables are not having multi-collinearity (table 19).

Table: 19 Variables in the Analysis for DA

Variables in the Analysis

Step Predictors Tolerance Sig. of F to Remove Min. D Squared Between Groups

1.00 ECCS 1.00 0.00

2.00

ECCS 0.95 0.00 0.08 .00 and 1.00

CCFB 0.95 0.00 0.14 .00 and 1.00

3.00

ECCS 0.86 0.06 0.09 .00 and 1.00

CCFB 0.80 0.00 0.15 .00 and 1.00

CBB 0.72 0.00 0.19 .00 and 1.00

The Box’s M test of equality of within group covariance matrices is significant (p<.05).

The log determinants between the three dependent categories are close, indicating

equality of covariance. As separate-group covariance matrices for classification is less


134

accurate (less than 2% from within group covariance matrix) the baseline within-group

covariance data output is used for further analysis. The original group classified in

separate group covariance matrices is 53.5% which is less than the target value of

53.75%, which is a 2% increase from 52.7% (within group covariance classification percentage).

Discriminant analysis (DA) is relatively robust even when there is modest violation of

equality of covariance assumption (Lachenbruch, 1975). The dichotomous variable,

which often violate multivariate normality, are not likely to affect conclusions based on

DA (Klecka, 1980).

Table: 20 Wilks' Lambda for DA

Wilks' Lambda

Step Number of Variables Lambda df1 df2 df3

Exact F

Statistic df1 df2 Sig.

1.00 1.00 0.92 1.00 2.00 547.00 22.76 2.00 547.00 0.00

2.00 2.00 0.77 2.00 2.00 547.00 38.10 4.00 1092.00 0.00

3.00 3.00 0.67 3.00 2.00 547.00 40.40 6.00 1090.00 0.00

One sample t-test for each variable shows, there is no significant difference between

sample mean and population mean, indicating minimal regression to the mean (p>.05).

Structural Equation Modeling

The data analysis is done in two stages, descriptive statistics followed by structural

equation modeling. Structural equation modeling is used in the study as it is robust

due to its ability to model mediating variables and also to test the overall model rather

than coefficients individually. It includes confirmatory factor analysis (measurement

model) and full model testing.


135

The Structural Equation Model (SEM) depicting the relationship among the variables

(see figure 9 to 12) are modeled using covariances. The hypothesised relationship

between the variables, which includes their indicators and error terms, was used to

draw the full model (figure 13). With the theoretical grounding firmly in place, the

change in Modification Indices (MI) was used to assign the covariate relations between

error terms within the indicators of the respective variables. This enables the

researcher to find the most optimal model or combination of the variables that fits

well with the data on which it is built and serves as a purposeful representation of the

reality from which the data has been extracted, and provides a parsimonious

explanation of the data (Kline, 1998).

Confirmatory Factor Analysis of the final questionnaire using SEM

The modified questionnaire is used to collect the final data and then it was subjected

to Confirmatory Factor Analysis (CFA) to recheck for reliability and validity (table 21 to

32) (figure 9 to 12) for exact values.

In the confirmatory factor analysis the standardised regression weights for all the

variables viz, materialism (5items), compulsive buying (9 items), enhanced credit card

spend (4 items) and credit card financing behaviour (5 items) had a factor loading for

each indicator which is greater than 0.70 with their critical ratio (C.R) greater than 1.96

(p<.05), which shows good construct validity (Schumacker & Lomax, 2004). However,

two indicators each in compulsive buying scale (CB), one in enhanced credit card

spends (ECCS) and two items in credit card financing behaviour (CCFB) had factor

loading greater than 0.5 but the critical ratio was greater than 1.96 (p<.05). The

variances extracted from each of the error terms of the indicators were greater than

0.5 with their critical ratio greater than 1.96 (p<.05) (Graham 2006). However, two error


136

terms of an indicator in the enhanced credit card spends scale, three error terms of

compulsive buying scale and one error term of credit card financing behaviour scale

had variances extracted just above 0.4 with their critical ratio greater than 1.96

(p<.05).The goodness of fit measures like IFI and CFI are indicating good fit with values

>0.90 (table 23, 26, 29, 32).

Figure 9

Materialism

Confirmatory Factor Analysis –MAT

MAT

Mat 1

.58

e10

1.00

1

Mat 2

.63

e11

.84

1

Mat 3

.66

e12

.91

1

Mat 4

.61

e13

1.05

1

Mat 5

.53

e14

1.09

1

.65

e1

1


137

Table: 21 Standardized Regression Weights for MAT

Standardized Regression Weights for MAT* Estimate

Mat1 <--- MAT 0.728

Mat2 <--- MAT 0.651

Mat3 <--- MAT 0.669

Mat4 <--- MAT 0.736

Mat5 <--- MAT 0.771 * SRW greater than 0.70 with their critical ratio (C.R) greater than 1.96 (p<.05) Table: 22 Variances for MAT Variances for MAT Estimate S.E. C.R.

e1 0.652 0.071 9.154

e10 0.578 0.045 12.943

e11 0.625 0.044 14.23

e12 0.663 0.047 13.982

e13 0.608 0.048 12.754

e14 0.526 0.044 11.855 Estimate greater than 0.5 with their critical ratio greater than 1.96 (p< .05)

Table: 23 Fit Indexes for MAT

MAT

NFI RFI IFI TLI

CFI Delta1 rho1 Delta2 rho2

0.976 0.952 0.981 0.961 0.981 Model Fit: CMIN/DF 4.729, GFI 0.982, AGFI 0.946


138

Figure 10

Compulsive Buying:

Confirmatory Factor Analysis –CBB

Table: 24

Standardized Regression Weights for CBB

Standardized Regression Weights for CBB* Estimate

CB1 <--- cbb 0.697

CB2 <--- cbb 0.728

CB3 <--- cbb 0.466

CB4 <--- cbb 0.75

CB5 <--- cbb 0.747

CB6 <--- cbb 0.77

CB7 <--- cbb 0.761

CB8 <--- cbb 0.598

CB9 <--- cbb 0.742 * SRW greater than 0.70 with their critical ratio (C.R) greater than 1.96 (p<.05)

cbb

CB1

.66

e10

1.00

1CB2

.61

e11

1.05

1CB3

.96

e12

.66

1CB4

.51

e13

1.03

1

CB5

.54

e14

1.05

1CB6

.39

e15

.96

1CB7

.43

e16

.98

1

CB8

.61

e17

.74

1CB9

.49

e18

.99

1

.62

e1

1


139

Table: 25

Variances of CBB

Variances of CBB Estimate S.E. C.R.

e1 0.621 0.069 9.015

e10 0.657 0.044 14.911

e11 0.609 0.042 14.59

e12 0.964 0.06 16.085

e13 0.511 0.036 14.304

e14 0.543 0.038 14.345

e15 0.393 0.028 14.004

e16 0.432 0.031 14.145

e17 0.614 0.039 15.596


Table: 26 Fit Indexes for CBB

CBB

NFI RFI IFI TLI




140

Figure 11

Enhanced Credit Card Spending:

Confirmatory Factor Analysis –ECCS

Eccs

ECCS1

.47

e30

1.00

1

ECCS2

.47

e31

.95

1

ECCS3

.50

e32

.93

1

ECCS5

.79

e33

.68

1

1.06

e3

1

Table: 27 Standardized Regression Weights for ECCS

Standardized Regression Weights for ECCS* Estimate

ECCS1 <--- Eccs 0.833

ECCS2 <--- Eccs 0.82

ECCS3 <--- Eccs 0.803

ECCS5 <--- Eccs 0.62 * SRW greater than 0.70 with their critical ratio (C.R) greater than 1.96 (p<.05)


141

Table: 28 Variances of ECCS

Variances of ECCS Estimate S.E. C.R.

e3 1.06 0.094 11.269

e30 0.467 0.044 10.592

e31 0.467 0.042 11.163

e32 0.503 0.043 11.822


Table: 29 Fit Indexes for ECCS

ECCS

NFI RFI IFI TLI




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Figure 12

Credit Card Financing Behaviour:

Confirmatory Factor Analysis –CCFB

CCFB

CCFB1

.57

e20

1.00

1

CCFB2

.27

e21

1.10

1

CCFB3 R

.81

e22

.57

1

CCFB5

.55

e23

.76

1

CCFB6

.51

e24

.50

1

.67

e2

1

Table: 30 Standardized Regression Weights for CCFB

Standardized Regression Weights for CCFB* Estimate

CCFB1 <--- CCFB 0.735

CCFB2 <--- CCFB 0.865

CCFB3R <--- CCFB 0.462

CCFB5 <--- CCFB 0.644

CCFB6 <--- CCFB 0.496 * SRW greater than 0.70 with their critical ratio (C.R) greater than 1.96 (p<.05)


143

Table: 31 Variances of CCFB

Variances of CCFB Estimate S.E. C.R.

e2 0.671 0.073 9.133

e20 0.569 0.046 12.267

e21 0.27 0.039 6.961

e22 0.808 0.051 15.715

e23 0.551 0.039 14.187


Table: 32 Fit Indexes for CCFB

CCFB

NFI RFI IFI TLI




144

Figure 13: Final SEM model as originally drawn using AMOS software

mat

Mat 1

e10

1

1

Mat 2

e111

Mat 3

e121

Mat 4

e131

Mat 5

e141

cbb

CB1

e20

1

1

CB2

e211

CB3

e221

CB4

e231

CB5

e241

CB6

e251

CB7

e261

CB8

e271

CB9

e281

eccs

ECCS1

e30

1

1

ECCS2

e311

ECCS3

e321

ECCS5

e331

ccfb

CCFB1

e40

1

1

CCFB2

e411

CCFB3 R

e421

CCFB5

e431

CCFB6

e441

cd

MCSUM

50

1

1

e3 e4

e2

e1

1

1

1

1

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