3. Research Methodology-Data Collection, Statistical Analysis

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1 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.5million people). These cards are offered by 28 different national and international banks together having 99 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 03

2 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, 998) 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 -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 -β, which is the power. The sample size required by power analysis for DA is 28 (. error margin) and for SEM is 468 (.2 error margin) (Cohen, 989). 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, 975). The dichotomous variable, which often violate multivariate normality, are not likely to affect conclusions based on DA (Klecka, 980).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 6 respondents more than twice the required sample size of 25 (Illustration- One dependent variable and four predictor variables * Five times=25). 04

3 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 33 (at.05 level) and 332 (at.0 level) which is more than sufficient to accept a model by chi-square (Schumacker & Lomax, 2004) (table 4). 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 (200), Oklahoma State University for reduced number of items. The original scale which was developed by Richins & Dawson (992) 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. 05

4 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, (200) Oklahoma State University. The above said scale itself was adapted from O Guinn & Faber compulsive buying screener scale (992). 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, 995). 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. 06

5 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, 995), which is originally adapted from Edwards (992). 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 07

6 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 6 in number, the potential defaulters (early warning stage) are 364 and the defaulters are 25.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, 997).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 08

7 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 The respondents of the final study were met from April 2008 to April 200. Analysis Plan: Descriptive Statistics and Distributions: The data was screened for the following defects.. 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 09

8 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 + and - 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. 0

9 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 Not normal Not normal Normal Normal Not normal Normal Normal Normal Normal CBB Not normal Not normal Normal Not normal Not normal Not normal Normal Normal Normal MAT Not normal Not normal Not normal ECCS Not normal

10 Not normal Normal Normal Normal Normal Not normal Not normal Not normal Not normal Not normal Normal Normal Not normal Normal CCFB *This is a lower bound of the true significance. a-lilliefors Significance Correction b-ccfb is constant when MCSUM = 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 Not normal Not normal Normal Not normal Not normal Normal Normal Normal Normal Normal CBB Normal MAT Not normal 2

11 Not normal Normal Normal Not normal Normal Normal Normal Normal Not normal Not normal Not normal Not normal Not normal Not normal Normal Normal Not normal Normal ECCS Not normal Not normal Not normal Not normal Not normal Normal Normal Normal Normal CCFB Normal *This is a lower bound of the true significance. b-ccfb is constant when MCSUM = It has been omitted. K-S test used when sample greater than 50 S-W test used when sample smaller than 50 3

12 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 Non-defaulter Not normal Potential defaulters (early warning stage) Not normal CBB Defaulter Normal Non-defaulter Not normal Potential defaulters (early warning stage) Not normal MAT Defaulter Not normal Non-defaulter Not normal Potential defaulters (early warning stage) Not normal ECCS Defaulter Not normal Non-defaulter Not normal Potential defaulters (early warning stage) Not normal CCFB Defaulter Not normal *This is a lower bound of the true significance. a-lilliefors Significance Correction Table: 8 Tests of Normality(b) based on Credit Default Category as used in Discriminant Analysis Shapiro-Wilk Type of Predictors Credit default category(dependent) Statistic Sample size(df) Sig. Distribution Non-defaulter Not normal Potential defaulters (early warning stage) Not normal CBB Defaulter Normal Non-defaulter Normal Potential defaulters (early warning stage) Not normal MAT Defaulter Not normal Non-defaulter Not normal Potential defaulters (early warning stage) Not normal ECCS Defaulter Not normal Non-defaulter Not normal Potential defaulters (early warning stage) Not normal CCFB Defaulter Normal *This is a lower bound of the true significance. 4

13 Table: 9 CBB Composite MAT Composite ECCS Composite CCFB Composite Descriptive Statistics for the Predictor Variables Statistic Std. Error Mean % Confidence Interval for Mean Lower Bound 2.08 Upper Bound 2.2 5% Trimmed Mean 2.0 Std. Deviation 0.78 Skewness Kurtosis Mean % Confidence Interval for Mean Lower Bound 2.88 Upper Bound % Trimmed Mean 2.95 Std. Deviation 0.86 Skewness Kurtosis Mean % Confidence Interval for Mean Lower Bound 3.2 Upper Bound % Trimmed Mean 3.22 Std. Deviation 0.99 Skewness Kurtosis Mean % Confidence Interval for Mean Lower Bound.93 Upper Bound % Trimmed Mean.96 Std. Deviation 0.72 Skewness Kurtosis

14 Figure: 4 Normal Q-Q Plot of CBB 6

15 Figure: 5 Normal Q-Q Plot of MAT 7

16 Figure: 6 Normal Q-Q Plot of ECCS 8

17 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. 9

18 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. A value close to 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 20

19 (Hutcheson & Sofroniou, 999).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 0). Table: 0 KMO and Bartlett's Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df Sig 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 , the reported value is 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.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 ). 2

20 Table: Communalities Communalities Initial Extraction CB CB CB CB CB CB CB CB CB Mat Mat Mat Mat Mat ECCS ECCS ECCS ECCS CCFB CCFB CCFB3 R CCFB CCFB 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 2). 22

21 Table: 2 Rotated Component Matrix Rotated Component Matrix(a) Component CB CB CB CB CB5 0.7 CB CB 0.65 CB CB Mat Mat Mat Mat Mat 0.69 ECCS ECCS 0.8 ECCS ECCS CCFB CCFB CCFB CCFB3 R 0.6 CCFB 0.6 Extraction Method: Principal Component Analysis. a-rotation converged in 5 iterations. Rotation Method: Varimax with Kaiser Normalization. 23

22 Figure: 8 Component Plot in Rotated Space Component Plot in Rotated Space Component Mat2 Mat3 Mat4 Mat5 MatCB3 CB ECCSCCFB2 CCFB CB4 ECCS2 CB5 CB2 ECCS3 CCFB6 CCFB5 ECCS5 CCFB3R Component Component 3 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 24

23 for all the included variables are greater than 0.7, as recommended by J.C. Nunnelly (978). Hence the constructs can be used together as a scale (table 3). Table: 3 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) Materialism (MAT) Enhanced Credit Card Spending (ECCS) Credit Card Financing Behaviour (CCFB) Table: 4 Inter-Item Correlation Matrix for Compulsive Buying Scale CB CB2 CB3 CB4 CB5 CB6 CB7 CB8 CB9 CB CB CB CB CB CB CB CB CB

24 Table: 5 Inter-Item Correlation Matrix for Materialism Scale Mat Mat 2 Mat 3 Mat 4 Mat 5 Mat Mat Mat Mat Mat Table: 6 Inter-Item Correlation Matrix for ECCS scale ECCS ECCS2 ECCS3 ECCS5 ECCS ECCS ECCS ECCS Table: 7 Inter-Item Correlation Matrix for CCFB scale CCFB CCFB2 CCFB3 R CCFB5 CCFB6 CCFB CCFB CCFB3 R CCFB CCFB Reliability Test Retest, Parallel Form, and Internal Consistency. 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 26

25 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, 200, Oklahoma State University. Compulsive buying impulsive scale was complimented with compulsive buying screener of Faber & O Guinn (992) so as to check for the parallel form consistency. The CBIS scale with 550 respondents in the final study was resubjected 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 The compulsive buying trait was also measured with the Faber scale. The Faber scale reported reliability is (Cronbach Alpha). The percentage of compulsive buyers identified by CBIS scale is 4.54% of respondents (out of 550) and the percentage of compulsive buyers identified by Faber scale is 3.93 % 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 27

26 matrix was greater than reported at 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 8). Table: 8 Pattern Matrix for CBIS and Faber Scale Pattern Matrix(a) for CBIS and Faber Scale Checking for Convergent validity Component 2 CB FCBB6R CB CB FCBB4R CB CB FCBBR CB CB CB FCBB3R FCBB2R FCBB5R CB FCBB7R Extraction Method: Principal Component Analysis. 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 28

27 bring in high reliability (Cronbach Alpha values) (see inter-item correlation tables 4-7 and Cronbach alpha table 3). 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 3). 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 (994) 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. 29

28 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 -4 means- Potential defaulters (early warning stage). Score of 5-0 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 to 4), and high defaulter (score of 5 to 0). 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 30

29 (predictor variables under study) are orthogonal (unrelated). Four factors clearly emerged as seen in the factor analysis (table 2). 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 3

30 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 6, potential defaulters (early warning stage) are 364 and of defaulters are 25. 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 5.8 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 3.28 (sig.0 & df 4 with four independent variables), but for thirteen cases. A separate DA was run eliminating the 3 cases and using the within group covariance matrix. The original group classified is 50.8% and the cross-validated 32

31 grouped cases correctly classified are 50.%. 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 5.82% in separate group covariance analysis). The researcher decided to proceed further analysis with the full sample of 550 without eliminating the 3 outliers as the original group classified is 52.7% and cross validated grouped cases correctly classified at 5.8 %. 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 9). Table: 9 Variables in the Analysis for DA Variables in the Analysis Step Predictors Tolerance Sig. of F to Remove Min. D Squared Between Groups.00 ECCS ECCS and CCFB and.00 ECCS and.00 CCFB and CBB and.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 33

32 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, 975). The dichotomous variable, which often violate multivariate normality, are not likely to affect conclusions based on DA (Klecka, 980). Table: 20 Wilks' Lambda for DA Wilks' Lambda Exact F Step Number of Variables Lambda df df2 df3 Statistic df df2 Sig 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. 34

33 The Structural Equation Model (SEM) depicting the relationship among the variables (see figure 9 to 2) 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 3). 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, 998). 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 2 to 32) (figure 9 to 2) 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.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.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.96 (p<.05) (Graham 2006). However, two error 35

34 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.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 e0 e e2 e3 e4 Mat Mat 2 Mat 3 Mat 4 Mat MAT.65 e 36

35 Table: 2 Standardized Regression Weights for MAT Standardized Regression Weights for MAT* Estimate Mat <--- MAT Mat2 <--- MAT 0.65 Mat3 <--- MAT Mat4 <--- MAT Mat5 <--- MAT 0.77 * SRW greater than 0.70 with their critical ratio (C.R) greater than.96 (p<.05) Table: 22 Variances for MAT Variances for MAT Estimate S.E. C.R. e e e e e e Estimate greater than 0.5 with their critical ratio greater than.96 (p<.05) Table: 23 Fit Indexes for MAT NFI RFI IFI TLI Delta rho Delta2 rho2 CFI MAT Model Fit: CMIN/DF 4.729, GFI 0.982, AGFI

36 Figure 0 Compulsive Buying: Confirmatory Factor Analysis CBB.62 e cbb CB9 CB8 CB7 CB6 CB5 CB4 CB3 CB2 CB e8 e7 e6 e5 e4 e3 e2 e e0 Table: 24 Standardized Regression Weights for CBB Standardized Regression Weights for CBB* Estimate CB <--- cbb CB2 <--- cbb CB3 <--- cbb CB4 <--- cbb 0.75 CB5 <--- cbb CB6 <--- cbb 0.77 CB7 <--- cbb 0.76 CB8 <--- cbb CB9 <--- cbb * SRW greater than 0.70 with their critical ratio (C.R) greater than.96 (p<.05) 38

37 Table: 25 Variances of CBB Variances of CBB Estimate S.E. C.R. e e e e e e e e e e Estimate greater than 0.5 with their critical ratio greater than.96 (p<.05) Table: 26 Fit Indexes for CBB NFI RFI IFI TLI Delta rho Delta2 rho2 CFI CBB Model Fit: CMIN/DF 5.705, GFI 0.935, AGFI

38 Figure Enhanced Credit Card Spending: Confirmatory Factor Analysis ECCS e30 e3 e32 e33 ECCS ECCS2 ECCS3 ECCS Eccs.06 e3 Table: 27 Standardized Regression Weights for ECCS Standardized Regression Weights for ECCS* Estimate ECCS <--- Eccs ECCS2 <--- Eccs 0.82 ECCS3 <--- Eccs ECCS5 <--- Eccs 0.62 * SRW greater than 0.70 with their critical ratio (C.R) greater than.96 (p<.05) 40

39 Table: 28 Variances of ECCS Variances of ECCS Estimate S.E. C.R. e e e e e Estimate greater than 0.5 with their critical ratio greater than.96 (p<.05) Table: 29 Fit Indexes for ECCS NFI RFI IFI TLI Delta rho Delta2 rho2 CFI ECCS Model Fit: CMIN/DF 6.495, GFI 0.988, AGFI

40 Figure 2 Credit Card Financing Behaviour: Confirmatory Factor Analysis CCFB e20 e2 e22 e23 e24 CCFB CCFB2 CCFB3 R CCFB5 CCFB CCFB.67 e2 Table: 30 Standardized Regression Weights for CCFB Standardized Regression Weights for CCFB* Estimate CCFB <--- CCFB CCFB2 <--- CCFB CCFB3R <--- CCFB CCFB5 <--- CCFB CCFB6 <--- CCFB * SRW greater than 0.70 with their critical ratio (C.R) greater than.96 (p<.05) 42

41 Table: 3 Variances of CCFB Variances of CCFB Estimate S.E. C.R. e e e e e e Estimate greater than 0.5 with their critical ratio greater than.96 (p<.05) Table: 32 Fit Indexes for CCFB NFI RFI IFI TLI Delta rho Delta2 rho2 CFI CCFB Model Fit: CMIN/DF 8.02, GFI 0.97, AGFI

42 Figure 3: Final SEM model as originally drawn using AMOS software 50 MCSUM cd e30 ECCS e3 ECCS2 e32 ECCS3 e33 ECCS5 e40 CCFB e4 e42 e43 CCFB2 CCFB3 R CCFB5 e44 CCFB6 eccs ccfb e3 e4 e20 e2 e22 e23 e24 e25 e26 e27 e28 CB CB2 CB3 CB4 CB5 CB6 CB7 CB8 CB9 cbb e2 e0 e e2 e3 e4 Mat Mat 2 Mat 3 Mat 4 Mat 5 mat e 44