Exam 12: Approaches to the Study of Intelligence

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Factor analysis is a statistical procedure used to identify underlying relationships between observed variables. It is commonly used in the field of psychology, sociology, and other social sciences to understand the structure of relationships between different variables.

The main goal of factor analysis is to identify the underlying factors or latent variables that explain the patterns of correlations among observed variables. These underlying factors are not directly measured, but are inferred from the patterns of correlations among the observed variables.

The procedure begins by collecting data on a set of observed variables. These variables are then analyzed to identify the underlying factors that best explain the patterns of correlations among them. This is done through a process of data reduction, where the original set of observed variables is transformed into a smaller set of factors that capture the essential information in the data.

There are different methods for conducting factor analysis, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). In EFA, the goal is to explore the underlying structure of the data and identify the number and nature of the underlying factors. In CFA, the goal is to test a specific theoretical model of the underlying factor structure.

Factor analysis can be used to address a variety of research questions, such as identifying the underlying dimensions of a construct, developing and validating measurement scales, and reducing the number of variables in a dataset. It is a powerful tool for understanding the complex relationships among variables and can provide valuable insights into the structure of data.