Chapter 4 Data Analysis & Results

Transcription

1 Chapter 4 Data Analysis & Results

2 61 CHAPTER 4 DATA ANALYSIS AND RESULTS This chapter discusses the data analysis procedure used in the current research. The two major software packages used for the analysis in the current research are SPSS (Statistical Package for the Social Sciences) 16.0 and AMOS SPSS has been used to do the preliminary descriptive analysis and principal component analysis. AMOS has been used to do structural equation modelling and testing the proposed model fit. 4.1 Missing Value Analysis At the outset the missing value analysis was carried to locate the missing values and the apply the suitable statistical measures to replace these missing values. Missing value analysis is important as it help address many concerns caused by the missing data. Among the various available methods for missing value analysis expectationmaximization (EM) method was used for conducting missing value analysis. In expectation-maximization method all observed information about a parameter is used to produce the maximum likelihood estimation of parameters (Acock, 2005). The missing value analysis showed following result: There are no missing values. EM estimates are not computed. The reason for this is that while administering questionnaires clear-cut instructions were given to the subjects for filling all the details and before collecting the questionnaires it was ensured that they have responded to all the items properly. Further, data from improperly or incompletely answered questionnaires were not included in the data editor (there were 16 such cases) so as to select responses of only from those participants who were motivated enough to participate in the research process and complete the questionnaire. It was considered necessary keeping the non-experimental nature of the research inquiry.

3 Descriptive Results The results of descriptive statistics for various variables are shown in the table 4.1 below. The variables that were directly measured are age, gender, work experience, innovation as a heuristic and excellence. The variables search and adapt heuristic, fast and frugal heuristic and heuristic intelligence have been computed. Table 4.1 Descriptive statistics for the sample characteristics N Mean SD Skewness Kurtosis Statistic Statistic Std. Error Statistic Statistic Std. Error Statistic Std. Error age Work experience Search & Adapt Fast & Frugal Intelligence Innovation as a heuristic Excellence Valid N (listwise) 203 Although biographical variables like age, gender, and work experience were not required for further analysis but there descriptive statistics have been presented to offer a complete picture of the sample characteristics. Age and gender compositions have already been discussed in the methodology chapter (under sample characteristics) so here only a brief discussion will follow on them after which the descriptive characteristics of remaining variables will be discussed. The total sample size included in analysis was 203 of which 79 (39.3 %) were males, and 124 (60.6 %) were females. Looking at this we can say that females are a bit overrepresented as compared to males in the sample but gender based comparison was not part of any of the three main objectives (see Chapter 1 Introduction, for the detailed overview of objectives) of the current research, so the current sample was accepted for further analysis. The mean age of sample is (S.D. = 4.219) with

4 63 the average work experience of 2.05 years. The mean scores and standard deviations of other variables are also shown in the table 4.1. Another descriptive statistics measured were skewness and kurtosis. These two are the measures of symmetry, or more precisely asymmetry, of the distribution. Skewness refers to the extent to which a distribution departs from the symmetricity (Simpson & Kafka, 1971) or normal distribution. The range of skewness value varies from -3 to + 3 (Lomax, 2001). A positively skewed distribution will have positive skewness value, and the value of mean will be greater than median which in turn will be greater than mode (i.e. mean > median > mode), while a negatively skewed distribution will have negative skewness value, and the value of mean will be less than median which in turn will be less than mode (i.e. mean < median < mode). A symmetric distribution, such as a normal distribution, has a skewness of 0. Kurtosis, on the other hand shows the peakedness of distribution (Lomax, 2001). A distribution range from flat (platykurtic) shape to a slender, narrow or highly peaked (leptokurtic) shape. In between the these two types lie the bell-shaped normal distribution curve (mesokurtic). According to Field (2009), z-scores can also be computed by dividing the skewness and kurtosis scores with standard errors and their significance levels can be checked at the desired level. According to him an absolute value greater than 1.96 is significant at p <.05, above 2.58 is significant at p <.01, and absolute values above about 3.29 are significant at p <.001. However, large samples will give rise to small standard errors and so when sample sizes are big, significant values arise from even small deviations from normality. In case of large samples (i.e., 200 or more) it is more important to look at the shape of the distribution visually and to look at the value of the skewness and kurtosis statistics rather than calculate their significance (p.139). Hence, z-scores were not computed further. Again, Field (2009) has given a threshold value of 3.29 for these measures and if the values of variables under scrutiny are below this threshold we can proceed with further analysis. In present case the variables that were included in the

5 64 analysis to test hypothesis, i.e. search and adapt heuristic, fast and frugal heuristic, heuristic intelligence, innovation as a heuristic and business excellence, all has values below 2.58, and thus we can proceed with the further analysis. 4.3 Principal Component Analysis (PCA) Test for group differences and data sufficiency To explore the factors underlying innovation heuristic, a principle component analysis of innovation as a heuristic (IAH) questionnaire was carried out. However, before conducting factor analysis it was checked whether groups differ significantly on IAH variable based on their gender or age. Further, an attempt was made to observe whether the data was sufficient for doing factor analysis. To check the group difference a one way analysis of variance (ANOVA) was performed whose results show that there is no significant differences between the males and females (F (1, 200 ) =.665, p =.416), and people across two age groups, i.e., one, less than 25 years old and, two, more than 25 years old (F (1,201) =.052, p =.82) for measured IAH variable. Further, KMO and Bartlett s tests were conducted to check the data sufficiency and suitability of the sample for factor analysis. The results of these tests are shown on the next page: Table 4.2 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..952 Bartlett's Test of Sphericity Approx. Chi-Square df 351 Sig..000 The KMO test represents the ratio of the squared correlation between variables to the squared partial correlation between variables. The KMO statistic varies between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the sum of

6 65 correlations, indicating diffusion in the pattern of correlations (hence, factor analysis is likely to be inappropriate). A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser (1974) recommends accepting values greater than 0.5 as barely acceptable, values between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and values above 0.9 are superb (Hutcheson & Sofroniou, 1999). The KMO test value for current data.952 indicating the sufficient adequacy of sample for factor analysis. Bartlett s test, on the other hand, examines whether the population correlation matrix resembles an identity matrix. If the population correlation matrix resembles an identity matrix then it means that every variable correlates very badly with all other variables and in this case data is not factor analyzable. In our case Bartlett s test is significant which means our correlation matrix is significantly different from identity matrix and variables correlate well with each other. Hence, cluster can be formed and factor analysis should be performed to check this Scree Plot Scree plot is a graph between each eigen value (Y - Axis) against the factor it is associated. Scree plot is an important technique to determine whether or not an eigen value is large enough to represent a meaningful factor. As cited in Field (2009), Cattell (1966) has argued that cut-point for selecting the appropriate number of factors should be at the inflexion point of the curve. In figure 4.1, the scree plot shows that the inflexion point begins from component 2 onwards so meaningfully we can extract two factors. According to Stevens (1992) with sample size as large as 200 scree plot provides a fairly reliable criteria for selection of appropriate number of factors.

7 66 Cut point of Eigen value Figure 4.1 Scree Plot Summary of Principal Component Analysis A principal component analysis (PCA) was carried out on the 27 items of Innovation as a heuristic questionnaire (based on Manimala, 1992; Gigerenzer, 2000; Gigerenzer, 2002) with oblique rotation (direct oblimin). The Bartlett s test of sphericity χ² (351) = , p <.001, indicated that correlations between items were sufficiently large for conducting PCA. An initial analysis was run to obtain eigen values for each component in the data. It was found that two components had eigen values over Kaiser s criterion of 1 and in combination explained % of the variance. Given the KMO test of sampling adequacy for the two groups, and the convergence of the scree plot and Kaiser s criterion the two components having eigen values and 1.67 respectively were retained in the final analysis. Further, to observe the internal consistency of these two factors Chronbach s Alpha was computed for the two factors which came out to be α =.90 and α =.95 respectively. These components were names as search & adapt heuristic (SAH), and fast and frugal heuristic (FFH). For a detailed discussion on factor naming and related explanation see the discussion chapter.

8 67 Table 4.3 Summary of principal component analysis (N = 203) SN Symbol Item Rotated Factor Loadings Search & Fast & Adapt Frugal 1 Hi9 Be flexible in one s ideas and plans Hi2 Ideas are the most important resource. Look for them everywhere Hi3 Look for new (product) ideas among personal contacts (friends, hobby.866 clubs, professional associations, customer complaints, previous job contacts, etc.). 4 Hi14 Never stop searching for new ideas and opportunities Hi16 Introduce new products, modify existing products, and/or change.813 strategies periodically. 6 Hi4 Look for new (product) ideas among technological developments abroad.774 especially among new, rare, or specialized products developed abroad. 7 Hi11 Never be constrained by rigid plans and the narrow visions. Act.763 according to opportunities. 8 Hi17 Keep the organization fresh and dynamic by periodically inducting young.710 people into it who have new ideas and the drive to implement them. 9 Hi5 Look for new (product) ideas among one s own vision of the future,.698 special talents, and innovative research findings, or among the special skills of one s associates and staff. 10 Hi10 Do not get stuck to one idea. Be prepared to leave it at the slightest.694 indication of failure, and develop new ideas. 11 Hi18 Launch new products on a trial basis, receive feedback, and slowly widen.671 the market. 12 Hi1 Be a pioneer in the choice of products. Avoid highly competitive, low.669 margin, run of-the-mill products. 13 Hi15 Never set any geographical limits to one s search for ideas and.636 opportunities. 14 Hi8 Look for new (product) ideas in the general environment (existing practices and changes in the legal, political, religious, social, and cultural.616

9 68 domains). 15 Hi13 Never be complacent about successes, but keep on striving for excellence through new ideas (Do not repeat success strategies until they fail). 16 Hi6 Look for new (product) ideas among the components, substitutes, complements, neglected ranges, supply gaps, deficiencies, and inadequacies of existing products. 17 Hi12 Treat personal problems/handicaps/ mishaps as indications to change one s line of thinking/occupation. 18 Hi24 Innovation is driving the market toward smaller but more efficient products/services. The evolution of smart phones, tablets and nano-cars is case in point. 19 Hi22 The best innovative product/service in a domain is one that accomplish the domain specific task in minimum number of steps and maximum simplicity. 20 Hi23 Product/service improvisation means identifying and eliminating all unnecessary steps in design and use. 21 Hi27 A faster way to challenge and involve employees to give them time to explore new ideas/products on their own. 22 Hi25 When I make changes in my product I focus on how fast & simple it will become for customers while adopting it. 23 Hi21 Innovation is the quickest way to create an uncontested market and beat competition. 24 Hi26 I welcome all new ideas but ideas which are fast and frugal in bringing returns are likely to be funded and supported first than those which promise only long term benefits. Eigen Value % of variance Cronbach s Alpha Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. Items with factor loading <.4 have been suppressed. Factors with Eigen value < l & explaining less than 5% variance have been omitted

10 Component Plot The component plot shows the emergence of two broad factors after PCA. The items that cluster on component 1& 2 are shown in the figure 4.2. Excluding item no. 7, item 1 to item number 18 cluster on component 1, while item no. 21 to 27 cluster on component 2. Item no. 7, 19, and 20 were deleted as they are not related to any of these factors. Figure 4.2 Component plot 4.4 Correlational Analysis To study whether innovation heuristic has a significant correlation with business excellence a correlation analysis was carried out among variables whose results are summarized in the table 4.4 below. The two extracted factors were correlated with total excellence scores and it was found that :

11 70 1) The Pearson s correlation coefficient between Search & Adapt and Business Excellence scores is r =.78 (p <.01); and 2) The Pearson s correlation coefficient between Fast & Frugal and Business Excellence scores is r =.65 (p <.01); and Table 4.4 Correlations between extracted components and total excellence scores Search & Adapt Fast & Frugal Intelligence Business Excellence Search & Adapt **.979 **.781 ** Fast & Frugal **.647 ** ** Intelligence Business Excellence 1 ** Correlation is significant at the.01 level (2 - tailed) 3) The Pearson s correlation coefficient between Intelligence and Business Excellence scores is r =.79 (p <.01). 4.5 Structural Equation Modelling To explore the structural model through which innovation heuristic and excellence interact with each other structural equation modelling(sem) was carried out using AMOS SEM is essentially a combination of exploratory factor analysis and multiple regression analysis (Ullman 2001) techniques which is used for testing and estimation of the causal relationship among variables within a proposed model. The model tested in the current research consisted of observing the structural relationship

12 71 among two exogenous variables i.e., search & adapt heuristic and fast & frugal heuristic, one latent variable i.e. heuristic intelligence, and one endogenous variable i.e. business excellence (for a detailed description of the variables please refer to the methodology chapter). Keeping in mind the problem associated with the multivariate normality and continuity of the data bootstrap method was used (West et al., 1995; Yung & Bentler, 1996; Zhu, 1997) Mediation Analysis Further, To check whether the heuristic intelligence has any mediating effect between exogenous variables (Search & Adapt, and, Fast & Frugal ) and Endogenous variables (Business Excellence) mediation analysis through Bootstrap Method was carried out. However, before establishing any mediating effect it is necessary to see whether exogenous variables (Search & Adapt, and, Fast & Frugal ) have any direct significant effect on endogenous variable (i.e., Business Excellence). The results of the test of direct effect is shown on the following page. Table 4.5 Direct Effects (Two tailed significance values) Fast & Frugal Search & Adapt Business Excellence.021*.001*** Since both values are significant (Table 4.5) so we can say that exogenous variables (Search & Adapt, and, Fast & Frugal ) have a direct significant effect on endogenous variable (i.e., Business Excellence). So, the sample can further be analyzed to see whether mediation effect is present. After this latent variable was introduced, and it was found that when mediated by latent variable (i.e., Intelligence) the effects were again found significant (p <.001). This means that

13 72 Intelligence as a intervening variable plays a significant role, and, thus can be included for further analysis. After this the proposed model was tested to see the type of mediation (full vs partial) whose results was displayed below: Figure 4.3 Analysis of the type of mediation (partial vs. full) in the proposed model Table 4.6 Direct effects after mediation - two tailed significance values Fast & Frugal s Search & Adapt Intelligence.001***.001*** Business Excellence * The result shows that path from both heuristics (i.e., Search & Adapt, and Fast & Frugal ) to latent variable (i.e., Intelligence) is significant. Path from Search & Adapt to endogenous variable (i.e., Business Excellence) is significant (p <.05), this means mediation effect of Intelligence between Search & Adapt and Business Excellence is partial (i.e., partial mediation). Further, path from Fast & Frugal to endogenous variable (i.e., Business Excellence ) is not significant (p value =.276), this means mediation effect of Intelligence between Fast & Frugal heuristic and Business Excellence is Full.

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15 The Proposed Model After the initial analysis a model was proposed in which both SAH and FFH are fully mediated through heuristic intelligence in bringing business excellence, the structural equation output of the proposed model along with estimates are shown in the figure 4.4. However, before performing the SEM some practical considerations were checked to see the suitability of data for SEM analysis Practical issues involved in the proposed SEM Before testing the proposed model the practical issues, like sample size and missing values, continuity of chosen scales, univariate and multivariate normality, assessment of linearity assumption, and test of outliers, related to the analysis were looked into. The results related to practical issues are discussed below: Sample Size and Missing Value The data for the proposed model was collected from sample/cases of 203 participants. There were 4 observed variables and 8 parameters to be estimated for the proposed model. The ratio of sample size to the observed variable is 50.75: 1, and the ratio of sample size to the number of parameters estimated is 25.36: 1. Bentler & Chou (1987) are of the view that if these ratios are in the range of 10:1 or more, then we can consider the sample size sufficient for a meaningful analysis. Stevens (1996) has considered 15 cases per predictor/parameter as sufficient for meaningful analysis. The current sample characteristics makes it suitable for SEM according to both the criteria. Hair et al. (1995) has recommend a sample size of at least one hundred observations to achieve adequate power in structural equation modelling. As we increase the sample size above 100 the Maximum Likelihood Estimation (MLE) becomes increasingly sensitive to the differences in the data, and for exceedingly large samples (N > ) MLE becomes too sensitive and even a small difference in data may turn all goodness-of-fit indices showing poor fit (Hair et. al, 1995). The current sample size of 203 can be

16 75 considered appropriate (neither too small nor too big to turn MLE sensitive) for sufficient power of SEM results. Further, the data was collected in a manner that each participant have to submit the questionnaire only after completion along with eliminating any dubious/incomplete questionnaire, so there were no missing values Continuous Scales As cited in Li (2006), whether we should treat such categorical scales as Likert-type or semantic differential scales as continuous in statistical analysis has been a common concern (Byrne 2001; Rundle-Thiele 2005). It has been suggested that this problem may not be an issue when the number of categories is large (Byrne 2001) (p. 136). In case of current sample a 7-point Likert scale has been used to measure all the variables. Thus the measurement meets the criteria of large categories as specified by Byrne (2001). Li (2007) has also used a 7 point Likert type scale in his model and consider it sufficient as meeting the criteria specified by Byrne (2001) regarding the large number of categories in the scale Univariate and Multivariate Normality The tests of univariate normality has been already discussed under the descriptive statistics section of this chapter and it was found that the variables that were included in the analysis to test hypothesis, i.e. search & adapt heuristic, fast & frugal heuristic, heuristic intelligence, innovation-as-a-heuristic and business excellence, have all their z values below Field (2009) has reported values up to 3.29 within the acceptable limits for testing the normality assumption. Thus we can say that the univariate normality characteristics of the sample falls within the acceptable limits. However, when tested statistically both Kolmogorov Smirnov test and Shapiro-Wilk test were significant for all the observed variables (p <.001) showing all the observed variables being significantly skewed.

17 76 However, according to West, Finch, & Curran (1995) the researcher should be concerned about his results only if skewness > 2 and kurtosis > 7, and the skewness and kurtosis values of all the variables in the current sample is well within these limits prescribed limits. In view of these results it was considered safer to treat the data for controlling the effect of multivariate non-normality. This is because univariate normality is necessary but not sufficient condition for multivariate normality, i.e. even if the individual variables are normally distributed (univariate normality) their joint distribution (multivariate ) can be non-normal (Stevens, 1992; Field, 2009; Newsom, 2012) Bootstrapping as an aid to nonnormal data Byrne (2001) has considered bootstrapping as an important aid to deal with non-normal data. Bootstrapping is an increasingly popular and promising approach to correcting standard errors (Newsom, 2012). It is a kind of resampling procedure in which multiple subsamples of the same size from the parent sample are drawn randomly, with replacement by considering the original sample as its population (Byrne, 2001). The researcher can assess the stability of parameter estimates with greater accuracy through this large number of randomly selected subsamples generated by bootstrapping. The Bollen Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modelling(sem) applications due to nonnormal data (Enders, 2005). For the current sample 2000 bootstrap sub-samples were generated through Bollen-Stine Bootstrap method (Bollen & Stine, 1992) and the obtained Bollen- Stine Bootstrap p-value was.094 for the tested null hypothesis that model is correct. Since the obtained Bollen-Stine p value is not significant so we accept the null hypothesis, i.e., there is no significant difference between the proposed model and sample behaviour, i.e., the proposed model is correct. The other major model-fit indices are presented in the forthcoming sections of this chapter which also indicate toward good model fit.

18 77 To sum up we can say that in actual practice in research nonnormnal data is quite common, and in fact some authors believe that in practice normal data is more an exception than reality, for e.g., Micceri (1989) has euphemistically termed normal curve as an improbable creature. Although, it is reasonable to ask whether normality violations are problematic for maximum likelihood analyses the research literature suggests that non-normal data tend to have a minimal impact on the parameter estimates themselves but can bias standard errors and distort the likelihood ratio test (Enders, 2010). To control these distortions Bollen-Stine bootstrapping has been used in the current research whose result indicate the model fit, so the problems arising out of non-normality has been accommodated and rectified Linearity Assumption Linearity among predictor variables (SAH, FFH, HI) and dependent variables (BE) were assessed by both graphical (scatter plot) and statistical method (correlation method). The scatter plots show a linear pattern and significant correlations among all the variables under consideration i.e. SAH & BE (R squared linear =.611), FFH & BE (R squared linear =.419), HI & BE (R squared linear =.612), SAH & HI (R squared linear =.946), FFH & HI (R squared linear =.745). So, the linearity of relationship assumption has also been satisfied by the current sample Outliers An outlier refers to an observation that deviates markedly from rest of the observations from the sample (Grubbs, 1969), and arouses suspicion that it was generated by a different mechanism (Hawkins, 1980). Univariate outliers refer to the cases in the sample having extreme values on a variable. Tabachnick & Fidell (Tabachnick & Fidell, 2001; Tabachnick & Fidell, 2007) have considered a standardized z value over ±3.29 (p <.001) as potential outlier. Further, AMOS computes Mahalanobis distance (Mahalanobis d-squared) to detect multivariate outliers, i.e extreme values coming from a combination of two or more variables. Although 3 cases (observation no. 129, 47 & 165) were found

19 78 to be having large values of Mahalanobis distance (Mahalanobis d-squared) but the relative distance among these values were not big so they were retained for further analysis. 4.6 The SEM output & estimates The output terms the current model as the recursive model. A recursive model is one that specifies direction of cause from one direction only (Byrne, 2010), which is true for the proposed model which is shown below: Figure 4.4 The proposed model with output values after confirmatory analysis The table 4.7 shows the various estimates for the path diagram for the hypothesized model. The higher the value of estimate, the more variation it explains in the dependent variable Table 4.7 Regression weights of various paths in proposed model Estimate S.E. C.R. p value _ Intelligence <--- Search & Adapt ***

20 79 Intelligence <--- Fast & Frugal *** Business Excellence <--- Intelligence *** Looking at p values we can say all the paths in hypothesized model is significant. Estimates tell that all the effects are positive. Both the heuristics lead to approximately the same amount of variation in Intelligence, for e.g., one unit change in Search & Adapt heuristic would lead to 1.1 units change in Intelligence, while one unit change in fast & Frugal heuristics leads to 1.2 unit change in Intelligence. Similarly, 1 unit change in heuristic Intelligence leads to.58 unit increase in Business excellence. The standard error of estimate (S.E) is a measure of error of prediction. Values of SE are low for the 3 paths, showing less error of prediction. Critical ratio (C.R.) is obtained by dividing the estimate of a variable with its S.E. In large samples, CR can be referred as standard normal distribution. Thus, a value of CR higher than ± 1.96 (at.05 level) and ± 2.56 (at.01 level) is considered as significant (Hox & Bechger, 1998). All the CRs are significant for the 3 paths. Covariance: The covariance between Search and Adapt and Fast and Frugal is estimated to be (p <.001) which is significant. Table 4.8 The covariance estimate between Search and Adapt and Fast and Frugal Estimate S.E. C.R. P Search & <--> Fast & Frugal *** Adapt heuristic

21 80 Variance: The variance scores of variables are shown in the table 4.9 on the next page. All the variances are significant (p <.001). However, variance of SAH is much larger as compared to FFH. This may be partly due to larger number of items on SAH (n=17) as compared to FFH (n=8). Also, the nature of question were more varied in case of SAH scale which may have further increased its variance. Further, a larger value of r 2 (173.9) as compared to r 1 (4.77) indicates that sample-population difference in prediction of Business Excellence can be higher as compared to Intelligence. Table 4.9 Variance estimate of Search and Adapt and Fast and Frugal, and residuals Estimate S.E. C.R. P Search and Adapt *** Fast and Frugal *** r *** r *** 4.7 Indices of Model Fit The proposed model was tested using AMOS 18.0 and various indices of model fit were observed. The indices of fit that were used for testing the model along with their obtained and desired values are shown in the table 4.10 on the following page. All the indices except RMSEA show good model fit. A detailed interpretation and discussion of these indices have been offered in the discussion chapter. In acknowledgement of multivariate non-normality Bollen Stine bootstrap values have been observed for the Chi- square test of model fit. The major indices of model fit that have been reported include Chi Square (χ 2 ), χ 2 /df ratio, GFI (Goodness-of-fit Index), AGFI (Adjusted Goodness-of-fit Index), PGFI (Parsimony Goodness of Fit Index), SRMR (Standardized

22 81 Root Mean square Residual), NFI (Normed Fit Index), TLI (Tucker Lewis Index), CFI (Comparative Fit Index), RMSEA (Root Mean Square Error of Approximation). Table 4.10 A summary of Indices of fit for the proposed model Index Obtained value Accepted range of values for model fit χ χ 2 is not significant (p = df 2.066, p Bollen - Stine =.094), so P.066 (p Bollen - Stine =.094) we accept null hypothesis, i.e., Ho: There is no significant difference between sample covariance matrix and population covariance matrix. hence the default model is acceptable. χ 2 /df ratio to 3 GFI (Goodness-of-fit Index) AGFI (Adjusted Goodness-offit Index) PGFI (Parsimony Goodness of.193 <.5, should be less than.5 Fit Index) SRMR (Standardized Root (perfect fit) to 1 (unfit) Mean square Residual) NFI (Normed Fit Index)

23 82 TLI (Tucker Lewis Index) CFI (Comparative Fit Index) RMSEA (Root Mean Square Error of Approximation) to Chapter Summary The current chapter discussed the procedure applied for data analysis and the obtained results have been presented. A missing value analysis showed no m-pattern as while collecting data it was ensured each participant has answered all the items properly and improperly answered questionnaires were excluded from the analysis. A principal component analysis showed the emergence of two major factors underlying innovation heuristic: search & adapt heuristic explaining % of variance, and fast & frugal heuristic explaining 6.19 % of variance. Jointly these two factors explain % of variance. A model was proposed and tested using Amos 18.0 which showed various indices of model fit supporting the proposed model.