Number of Components to Extract

There are a number of ways to determine the number of factors to extract. The Kaiser criterion suggests that one should retain any factors with eigenvalues greater than one. Scanning the Total Variance Explained (see Appendix C), the first eleven components (λ or eigenvalues) are above one. The total variance explained for the first two components is 27.683 and 7.598 percent of the variance, respectively. The total variance explained for the first two factors is 5.281 percent. Total variance explained by the first eleven factors is 72.012 percent. The Kaiser technique is only reliable, however, when the number of variables is less than thirty and the communalities are greater than .7. Inspection of the communalities shows twenty three of the 54 communality coefficients are greater than .7. Communalities (h2) “represent how much of the variance of a measured variable was useful in delineating the extracted factors” (Thompson, 2004, p. 61). These communalities also represent the R-square (R2) between the factor scores (latent variable scores) and measured scores on the measured variable.

The Catell (1996) technique suggests keeping factors above the elbow in the Scree plot (see Figure 1 in Appendix B). In this study, the scree plot suggests two factors be retained. Breaks also appear for 3, 4, 5, and 7 components. The seven factor solution supports the theoretical model with seven factors. When this model was specified, however, the majority of the variables loaded onto the first factor and the model made no theoretical sense. Theoretical considerations supported a nine factor model. The pattern matrix is shown in Table 4.

The items cluster cleanly into the communication, commitment, citizenship, cognition based trust, and resistance to change factors. Nearly all the affect based trust measures hang together with the exception of abt6 (This person approaches his/her job with professionalism and dedication. (ABT-6)) which is strongly correlated with the items that measure cognition based trust. The variable, organizational justice, includes three subfactors, fairness, employee voice and justification. The first four items for fairness cluster together and appear to measure that concept. The questions are shown below. Survey questions Orgjv6, Orgjv7, Orgjv8 and OrgjJ11 cluster together to form the Employee Voice measure. Similarly, Orgjv5, Orgjv8, and OrgjJ10 load onto Justification. These subscales will be utilized as one scale, organization justice, in the computation of Cronbach’s alpha.

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