title: Exploratory factor analysis in behavior genetics research: factor recovery with small sample sizes.
abstract: Results of a Monte Carlo study of exploratory factor analysis demonstrate that in studies characterized by low sample sizes the population factor structure can be adequately recovered if communalities are high, model error is low, and few factors are retained. These are conditions likely to be encountered in behavior genetics research involving mean scores obtained from sets of inbred strains. Such studies are often characterized by a large number of measured variables relative to the number of strains used, highly reliable data, and high levels of communality. This combination of characteristics has special consequences for conducting factor analysis and interpreting results. Given that limitations on sample size are often unavoidable, it is recommended that researchers limit the number of expected factors as much as possible.
authors.affiliation: Department of Psychology, The Ohio State University, Columbus 43210-1222, USA. preacher.2@osu.edu
authors.forename: Kristopher J
authors.initials: KJ
authors.lastname: Preacher
authors.forename: Robert C
authors.initials: RC
authors.lastname: MacCallum
mesh_terms: D000818:Animals; D005163:Factor Analysis, Statistical; D005824:Genetics, Behavioral / Q000706:statistics & numerical data*; D008957:Models, Genetic; D009010:Monte Carlo Method; D012494:Sampling Studies