Analysis of the Pearson Correlation Coefficients - Assignment Example

Assessment 3.1 Principal Component Analysis: We have conducted the Principal component analysis of the 8 variables (Item 01-08 as provided in the data sheet) and used the SPSS software to get the results. The results can be interpreted by running over the various outputs received for basic condition, demand condition and facilitation from supporting industries. (A) For Basic Condition: The Kaiser-Meyer-Olkin measure of sample adequacy and Bartlett’s test of Sphericity (Fig. 1) in this case is .870 KMO and chi-square value of 315.201 indicating the appropriateness of applying PCA to the given dataset. Fig. 2 shows that SPSS has extracted 3 variables with Eigen value greater than 1 which in all explain approx. 74% of the variance. The communalities (Fig. 3) for example, for question 1 we can say that 71.1% of the variance associated with question 1 is common variance as the column ‘Extraction’ gives the common variance in a data structure. The ‘Component matrix’ in Fig. 4 shows the loading of each variable onto each factor. Communalities Initial Extraction ITEM01 1.000 .711 ITEM02 1.000 .724 ITEM03 1.000 .662 ITEM04 1.000 .646 ITEM05 1.000 .585 ITEM06 1.000 .705 ITEM07 1.000 .955 ITEM08 1.000 .963 Extraction Method: Principal Component Analysis. The Scree plot (Fig. 5) confirms the Kaiser criterion of extracting 3 factors. The ‘Rotated Component Matrix’ (Fig. 6) shows us that variable 01 to 06 all load to component 1 which explains around 47.718% of variance. All these variables have questions related to labor and raw material. Therefore, the component 1 can be given the nomenclature as ‘Basic Condition for Resources’. However, only variable 07 and variable 08 load to component 3 and 2 respectively which cannot be considered as very reliable. (B) For Demand Condition: A PCA conducted on 3 items gave (Fig. 7) KMO value (0.725) is between 0.7 and 0.8 and the Bartlett’s test is also significant with chi-square value 120.504 and p Read More