Assignment: Exploratory Factor Analysis

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  • Topic: Factor analysis, Principal component analysis, Eigenvalue, eigenvector and eigenspace
  • Pages : 6 (1408 words )
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  • Published : February 20, 2013
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4.5. EXPLORATORY FACTOR ANALYSIS

Table 4.14 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy..896
Bartlett's Test of SphericityApprox. Chi-Square4.120E3
df528
Sig..000

Bartlett's test of sphericity indicates whether the correlation matrix is an identity matrix, which would indicate that the variables are unrelated. The significance level gives the result of the test. Very small values (less than .05) indicate that there are probably significant relationships among the variables. A value higher than about .10 or so may indicate that this data are not suitable for factor analysis. Hence, the researcher concludes the data is suitable for factor analysis since Kaiser-Meyer-Olkin Measure of Sampling Adequacy value is .896.

Table 4.15 Total Variance Explained
ComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
Total% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative % 127.67949.42749.42727.67949.42749.42711.27920.14120.141 25.92210.57660.0035.92210.57660.00310.88019.42939.570 34.5028.03968.0424.5028.03968.0428.52815.22854.798

43.4296.12374.1653.4296.12374.1656.08710.87065.668
52.8885.15779.3232.8885.15779.3233.9577.06772.734
62.5394.53583.8582.5394.53583.8583.2635.82678.561
71.9963.56487.4221.9963.56487.4223.1855.68884.249
81.4192.53389.9551.4192.53389.9552.5704.58988.838
91.1672.08392.0391.1672.08392.0391.7923.20192.039
Extraction Method: Principal Component Analysis.

The above table gives eigenvalues, variance explained, and cumulative variance explained for the factor solution. The first panel gives values based on initial eigenvalues. For the initial solution, there are as many factors as there are variables. The "Total" column gives the amount of variance in the observed variables accounted for by each factor. The "% of Variance" column gives the percent of variance accounted for by each specific factor, relative to the total variance in all the variables. The "Cumulative %" column gives the percent of variance accounted for by all factors up to and including the current one. For instance the Cumulative % for the second factor is the sum of the % of Variance for the first and second factors. In the above table , there are a few factors that explain a lot of the variance which is a sign of good factor analysis and the rest of the factors explain relatively small amounts of variance. The Extraction Sums of Squared Loadings group gives information regarding the extracted factors or components. In the "Rotation Sums of Squared Loadings" group, the variance accounted for by rotated factors or components may be different from those reported for the extraction, but the Cumulative % for the set of factors or components will always be the same. Table 4.16 Rotated Component Matrixa

Component
1234 56789
Excellence of quality relative to price.705
National Brands Provide More Value.647
Highstock Level To be Maintained.845
Private brands Provide Greater Value.887
Availability of Fresh Foods.585
Availability of Organic Foods.539
Labels with Nutritional Information.576
Simple and easy-to-read labeling.668
Colours and Logos-culturally Appropriate.654
Attractive Image in Labels.668
Availability of Entire Grocery Items.683
New Product Information.804
Organise Grocery List.577
Provide Location assistance to identify Grocery Items.839 Offer targeted promotions at the front door .735
Customer Stated Promotion.583
Provide Accrual Promotions and Redemption Promotion.611 Provide Discounts.816
Sending Greetings to Customers.792
Novel Technology to attract Customers.587
Provide Store Credit...
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