Differentiating Categorical and Dimensional Data With Taxometric Analysis: Are Two Variables Better Than None?

作者：Ruscio John^*; Walters Glenn D

来源：Psychological Assessment, 2011, 23(2): 287-299.

DOI：10.1037/a0022054

• taxometrics
• MAMBAC
• MAXSLOPE
• comparison data
• CCFI

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.

• 出版日期2011-6