Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring.

Abstract

[Correction Notice: An Erratum for this article was reported in Vol 26(3) of Psychological Assessment (see record 2014-16017-001). The mean, standard deviation and alpha coefficient originally reported in Table 1 should be 74.317, 10.214 and .802, respectively. The validity coefficients in the last column of Table 4 are affected as well. Correcting this error did not change the substantive interpretations of the results, but did increase the mean, standard deviation, alpha coefficient, and validity coefficients reported for the Honesty subscale in the text and in Tables 1 and 4. The corrected versions of Tables 1 and Table 4 are shown in the erratum.] Item response theory (IRT) models were applied to dichotomous and polytomous scoring of the Self-Deceptive Enhancement and Impression Management subscales of the Balanced Inventory of Desirable Responding (Paulhus, 1991, 1999). Two dichotomous scoring methods reflecting exaggerated endorsement and exaggerated denial of socially desirable behaviors were examined. The 1- and 2-parameter logistic models (1PLM, 2PLM, respectively) were applied to dichotomous responses, and the partial credit model (PCM) and graded response model (GRM) were applied to polytomous responses. For both subscales, the 2PLM fit dichotomous responses better than did the 1PLM, and the GRM fit polytomous responses better than did the PCM. Polytomous GRM and raw scores for both subscales yielded higher test-retest and convergent validity coefficients than did PCM, 1PLM, 2PLM, and dichotomous raw scores. Information plots showed that the GRM provided consistently high measurement precision that was superior to that of all other IRT models over the full range of both construct continuums. Dichotomous scores reflecting exaggerated endorsement of socially desirable behaviors provided noticeably weak precision at low levels of the construct continuums, calling into question the use of such scores for detecting instances of "faking bad." Dichotomous models reflecting exaggerated denial of the same behaviors yielded much better precision at low levels of the constructs, but it was still less precision than that of the GRM. These results support polytomous over dichotomous scoring in general, alternative dichotomous scoring for detecting faking bad, and extension of GRM scoring to situations in which IRT offers additional practical advantages over classical test theory (adaptive testing, equating, linking, scaling, detecting differential item functioning, and so forth).