2004 Articles

A Taxometric Analysis of Experimenter-Induced Response Bias on Self-Report Data

Neubauer, Jessica; McGrath, Robert E.

Taxometric analysis was developed to help determine whether a latent variable best conforms to a dimensional or categorical (taxonic) model. One study (Beauchaine & Waters, 2003) found that the taxonic structure of ratings could be influenced through instructional set. Their findings raise questions about whether taxometric analysis is necessarily identifying the organic structure of ratings-based latent variables. However, the study was limited to ratings of target individuals unknown to the rater. The present study was conducted to determine whether self-ratings are equally susceptible to manipulation. Undergraduate students were asked to complete a battery of four selfreport personality measures which previous research indicated may tap a common latent variable. Participants randomly received an instructional set that implied either a taxonic or dimensional structure for self-ratings. The results suggest that self-ratings may be more resistant to instructional set than ratings of others, as data from both groups conformed to a dimensional model rather than a taxonic structure.