Factor Analysis: A Comprehensive Overview
Factor Analysis is a statistical technique used to explain and interpret the observed variables in terms of a smaller set of underlying factors. It is an important tool for multivariate data analysis, and has been widely used in fields such as psychology, sociology, and economics. This article provides an overview of Factor Analysis, including its history, uses, and common methods.
History
Factor Analysis was first introduced in 1904 by Charles Spearman, who developed the technique to measure general intelligence. Spearman used Factor Analysis to analyze the results of intelligence tests, and discovered that intelligence was composed of two factors: a “general factor” and “specific factors” (Spearman, 1904). Since then, Factor Analysis has been used extensively in various disciplines, including psychology, economics, sociology, and marketing.
Uses
Factor Analysis is used to identify common underlying factors or patterns in a set of observed variables. It is a useful tool for data reduction and simplification, as it helps to reduce a large number of variables into a smaller set of factors that explain the majority of the variance in the data. Factor Analysis is also used to identify the relationships between variables, and to uncover hidden underlying factors in a dataset.
Common Methods
There are two main types of Factor Analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is used to identify the underlying structure of a dataset, while CFA is used to test a pre-specified hypothesis about the data structure. Both methods involve the use of factor rotation techniques to identify the most meaningful factors in the data.
Conclusion
Factor Analysis is a powerful statistical technique for multivariate data analysis. It is used to identify underlying factors in a dataset, and has been widely used in various disciplines, including psychology, sociology, and economics. The two main types of Factor Analysis are Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), and both involve the use of factor rotation techniques to identify the most meaningful factors in the data.
References
Spearman, C. (1904). “General intelligence, objectively determined and measured”. American Journal of Psychology, 15(2), 201-293.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). “Evaluating the use of exploratory factor analysis in psychological research”. Psychological Methods, 4(3), 272-299.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Harlow, UK: Pearson Education.