Understanding and Using Factor Scores: Considerations for the Applied Researcher

Abstract

Following an exploratory factor analysis, factor scores may be computed and used in subsequent analyses. Factor scores are composite variables which provide information about an individual’s placement on the factor(s). This article discusses popular methods to create factor scores under two different classes: refined and non-refined. Strengths and considerations of the various methods, and for using factor scores in general, are discussed. Exploratory factor analysis (EFA) has been used as an analytical tool in educational research. The methods may be used with novel or exploratory research scenarios as a precursor to latent variable modeling or confirmatory factor analyses (CFA) (Schumaker & Lomax, 2004). However, in many research situations, EFA is used as the focal methodology. Practitioners may use EFA for a variety of purposes such as reducing a large number of items from a questionnaire or survey instrument to a smaller number of components, uncovering latent dimensions underlying a data set, or examining which items have the strongest association with a given factor. Once a researcher has used EFA and has identified the number of factors or components underlying a data set, he/she may wish to use the information about the factors in subsequent analyses (Gorsuch, 1983). For example, researchers may want to identify an individual’s placement or ranking on the factor(s), use the information with hypothesis tests to determine how factor scores differ between groups, or to incorporate factor information as part of a regression or predictive analysis. To use EFA information in follow-up studies, the researcher must create scores to represent each individual’s placement on the factor(s) identified from the EFA. These