title: Differentiating categorical and dimensional data with taxometric analysis: are two variables better than none?
abstract: Taxometric analyses have proven helpful for distinguishing categorical and dimensional data. Many taxometric procedures require at least 3 variables for analysis. What if a construct is defined by only 2 conceptually nonredundant characteristics or a data set contains only 2 empirically nonredundant variables? In Study 1, we performed extensive simulations to determine whether informative results can be obtained when only 2 variables are available for taxometric analysis. The mean above minus below a cut (MAMBAC) and maximum slope (MAXSLOPE) procedures, used with parallel analyses of comparison data, successfully differentiated categorical and dimensional structure. With just 2 variables, it seems especially important that indicators vary across as many distinct values as possible and that investigators obtain as large a sample as possible. Additional findings address questions about the most effective way to implement taxometric analyses. In Study 2, the potential utility of 2-variable taxometric analysis is illustrated using data on proactive and reactive childhood aggression, where the results provided strong support for dimensional structure. As long as high-quality data are available, it appears that one can have confidence in the results of taxometric analyses performed with only 2 variables.
authors: Department of Psychology, The College of New Jersey, P.O. Box 7718, Ewing, NJ 08628, USA. ruscio@tcnj.edu
authors: John
authors: J
authors: Ruscio
authors: Glenn D
authors: GD
authors: Walters
mesh_terms: D035841:Behavioral Research / Q000379:methods; D003627:Data Interpretation, Statistical; D036262:Empirical Research; D005163:Factor Analysis, Statistical; D006801:Humans; D015233:Models, Statistical; D011581:Psychological Tests; D011584:Psychology; D018401:Sample Size