Assigning cases to groups using taxometric results: an empirical comparison of classification techniques.
Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic and dimensional models, but it is not clear how best to assign individuals to groups using taxometric results. The present study compares the performance of two classification techniques-Bayes' theorem and a base-rate technique--across a wide range of data conditions. The base-rate technique achieves greater classification accuracy and a more even balance between sensitivity and specificity. In addition, the base-rate classification technique is easier to implement than Bayes' theorem and is more versatile in that it can be used when the context of assessment requires that cases be classified despite the absence of latent classes.
Ruscio
John
J
Department of Psychology, The College of New Jersey, Ewing, NJ 08628, USA. ruscio@tcnj.edu
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002965:Classification / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011594:Psychometrics / Q000145:classification / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D013223:Statistics as Topic / Q000379:methods