Factor Analysis on Possum Dataset to Simplify Many Independent Variables Into Fewer Factors
Abstract
This research aims to apply factor analysis to possum data with the aim of simplifying many independent variables into fewer factors. The factor analysis steps begin by grouping the variables to be analyzed and compiling a correlation matrix using the Bartlett test and the Kaiser-Meyer-Olkin (KMO) test. From the test results, it was found that the variables had sufficient correlation to proceed to factor analysis. After that, factor extraction was carried out using three criteria, namely eigenvalues, diversity percentage, and scree plot, which concluded that the number of factors formed was two. Next, factor rotation was carried out using the varimax method to simplify interpretation. The results show that certain variables have high loadings on certain factors, making it easier to identify patterns. In conclusion, factor analysis succeeded in simplifying the relationship between variables into two factors that can be interpreted more easily.
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