This paper “Factors Affecting Job Motivation, Satisfaction and Performance” explains the data analysis techniques and results through written text, figures, and/or other means, assessing the relationship amongst factors affecting job motivation, satisfaction and performance…
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Factor analysis is a variable reduction strategy whose motivation is coming up with a subset of the data explaining much of the variability. In this case, EFA was achieved using Principal Components Analysis (PCA). To assess the adequacy of the sampling, Keiser-Meier-Okin (KMO) statistic was applied with a value of for the test being 0.60. Since KMO is above the 0.5 cut-off, we conclude that EFA is valid. On the same note, Barlett’s test of sphericity was signifivant (Chi square value=584.589 and p-value of 0.00), hence we conclude that there are significant correlations in the variables (Johnson & Wichern, 2007).
A cut-off for including variables was based on an Eigen value of one or more. Results for the total variance explained indicated that eight variables had an Eigen value greater than one, with a cumulative total variance explained of 70% (Table 2). As a rule of the thumb, In PCA a cut-off value of the total variance explained of 70% is deemed good enough.
Varimax rotation with Kaiser normalization was applied to the factors in a bid to simplify the covariance structure. In principle, rotation aims at ensuring that a particular variable has a high loading on one factor while it has an almost zero loading on all other factors. The results of the rotation are presented in table 3 below, from which it is evident that the eight components with Eigen values above one are selected (Johnson & Wichern, 2007).
A look at the components reveals that there are some reported high correlations between the components and the variables as may be expected. Looking at the first component for instance, it contributes 19.345% of the total variability (Table 2).
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