# Assignment: Exploratory Factor Analysis

**Topics:**Factor analysis, Principal component analysis, Eigenvalue, eigenvector and eigenspace

**Pages:**6 (1408 words)

**Published:**February 20, 2013

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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