Factor Analysis

Characteristics of Factor Analysis

Factor analysis is not a single statistical method. Rather, it involves a complex array of statistical procedures that provide a way to identify interrelationships among a large set of observed variables. Much subjectivity and artistry are involved in this technique. The goal of factor analysis is to arrive at a parsimonious set of factors that have common characteristics and that summarize and describe the structural interrelationships among a set of identified items in a concise and understandable way. A factor is a cluster of related, observed variables that represent the underlying dimension of a construct that is as distinct as possible from other factors in the solution.

Assumptions of Exploratory Factor Analysis

A basic assumption of EFA is that within a collection of observed variables, there exists a set of underlying factors, smaller in number than the observed variables, that can explain the interrelationships among those variables. Because the initial steps of EFA are performed using Pearson product moment correlations, many of the assumptions associated with this correlation coefficient are applicable to factor analysis (e.g., large sample sizes, continuous distributions and sufficient variation within the items, and linear relationships among the correlated variables). Since the response categories for each individual item are often constructed using dichotomous yes, no responses or ordinal-level Likert scales, normality of distributions is not always a strict requirement.

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