TY - JOUR
T1 - A comparison of mean partialing and dual-hypothesis testing to evaluate stereotype effects when assessing profile similarity
AU - Conroy, David E.
AU - Pincus, Aaron L.
N1 - Funding Information:
Funding for this research was provided in part by a grant from the National Institute of Mental Health (MH65230). Portions of these analyses were presented at the May 2002 meeting of the Society for Interpersonal Theory and Research, Toronto, Ontario, Canada. Thanks to Camille Cassidy, Jordi Ciambrone, Jon Metzler, Mark Ruiz, and Jason Willow for help with data management.
PY - 2006/4
Y1 - 2006/4
N2 - Assessing profile similarity is an important task in research and clinical practice, but conclusions about profile similarity may be confounded by stereotype effects that create artificially similar profiles. In this article, we review the impact of stereotype effects on profile similarity and a conventional approach to addressing this confound (i.e., mean partialing). We argue that a dual-hypothesis testing approach distinguishing the no-effect null hypothesis (i.e., is the observed similarity different from zero?) from the chance-effect null hypothesis (i.e., is the observed similarity different from chance given the distribution of profile elements?) can provide a more nuanced understanding of profile similarity. To compare results from these 2 perspectives, we analyzed data from 2 samples using within-persons correlations as indexes of similar profile shapes. Results indicated that a dual-hypothesis testing approach led to more conservative conclusions about profile similarity (i.e., fewer Type 1 errors) than mean partialing and may be especially valuable when dealing with moderate-sized stereotype effects. Both approaches led to identical conclusions when stereotype effects were largest. Conclusions emphasize the relative merits and limitations of the dual-hypothesis testing approach as well as potential future applications in the personality assessment domain.
AB - Assessing profile similarity is an important task in research and clinical practice, but conclusions about profile similarity may be confounded by stereotype effects that create artificially similar profiles. In this article, we review the impact of stereotype effects on profile similarity and a conventional approach to addressing this confound (i.e., mean partialing). We argue that a dual-hypothesis testing approach distinguishing the no-effect null hypothesis (i.e., is the observed similarity different from zero?) from the chance-effect null hypothesis (i.e., is the observed similarity different from chance given the distribution of profile elements?) can provide a more nuanced understanding of profile similarity. To compare results from these 2 perspectives, we analyzed data from 2 samples using within-persons correlations as indexes of similar profile shapes. Results indicated that a dual-hypothesis testing approach led to more conservative conclusions about profile similarity (i.e., fewer Type 1 errors) than mean partialing and may be especially valuable when dealing with moderate-sized stereotype effects. Both approaches led to identical conclusions when stereotype effects were largest. Conclusions emphasize the relative merits and limitations of the dual-hypothesis testing approach as well as potential future applications in the personality assessment domain.
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U2 - 10.1207/s15327752jpa8602_03
DO - 10.1207/s15327752jpa8602_03
M3 - Review article
C2 - 16599788
AN - SCOPUS:33645950108
SN - 0022-3891
VL - 86
SP - 142
EP - 149
JO - Journal of Personality Assessment
JF - Journal of Personality Assessment
IS - 2
ER -