Factor Analysis: Unlocking the Blueprint of Human Behavior
Scientifically Reviewed · Dr. Marwa Abd-Alazim · September 24, 2026
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Factor loading is the measure of the correlation between the observed variable and the factor. It is used to determine the strength of the association between the observed variable and the factor in factor analysis. In factor analysis, factor loadings are used to determine how much the observed variables are associated with the latent variables or factors.
Factor loading is calculated by the Pearson correlation coefficient between the observed variable and the factor. It is a numerical value that ranges from -1 to +1. A factor loading of +1 indicates that the observed variable is perfectly correlated with the factor, while a factor loading of -1 indicates that the observed variable is perfectly inversely correlated with the factor. A factor loading of 0 indicates that the observed variable is not associated with the factor.
The factor loading is used to determine which variables are associated with which factors. A high factor loading indicates that the observed variable is strongly associated with the factor, while a low factor loading indicates that the observed variable is weakly associated with the factor. Variables with high factor loadings are retained in the factor analysis, while variables with low factor loadings are dropped from the analysis.
In addition, factor loadings can also be used to determine the relative importance of the factors in the factor analysis. Factors with higher factor loadings are considered to be more important than factors with lower factor loadings.
Factor loading is an important concept in factor analysis and is used to determine the strength of the association between the observed variable and the factor. It is a numerical value that ranges from -1 to +1 and is used to determine which variables are associated with which factors. In addition, factor loadings can also be used to determine the relative importance of the factors in the factor analysis.
References
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7). Retrieved from http://pareonline.net/getvn.asp?v=10&n=7
