The authors discuss the applicability of nonparametric item response theory (IRT) models to the construction and psychometric analysis of personality and psychopathology scales, and they contrast these models with parametric IRT models. They describe the fit of nonparametric IRT to the Depression content scale of the Minnesota Multiphasic Personality Inventory-2 (J. N. Butcher, W. G. Dahlstrom, J. R. Graham, A. Tellegen, & B. Kaemmer, 1989). They also show how nonparametric IRT models can easily be applied and how misleading results from parametric IRT models can be avoided. They recommend the use of nonparametric IRT modeling prior to using parametric logistic models when investigating personality data.Recently, several authors have introduced and discussed the advantages of applying item response theory (IRT) models (e.g., Embretson & Reise, 2000) to construct personality 1 scales and to explore the structure of personality data sets. For example, Waller, Tellegen, McDonald, and Lykken (1996) contrasted the use of IRT with principalcomponent factor analysis, and Reise and Waller (2003) discussed the choice of an IRT model to analyze psychopathology-test data. That is, they compared the fit of the two-parameter and three-parameter logistic models (PLMs) on 15 unidimensional factor scales from the Minnesota Multiphasic Personality Inventory-Adolescent (MMPI-A; Butcher et al., 1992). Most studies apply parametric IRT models (in particular, the 2PLM and 3PLM) to investigate the quality of personality and psychopathology tests (e.g., Panter, Swygert, Dahlstrom, & Tanake, 1997;Robie, Zickar, & Schmitt, 2001;Steinberg, 1994;Waller, Thompson, & Wenk, 2000).The aim of the present study was to illustrate the usefulness of nonparametric IRT (NIRT) to construct and to analyze psychopathology and personality scales and tests. In our opinion, the use of NIRT has been underexposed in the recent personality literature (for an exception, see Santor & Ramsay, 1998). We show that these models are very suitable to exploring the psychometric properties of personality data. Interesting in this context is a study by Chernyshenko, Stark, Chan, Drasgow, and Williams (2001), who explored the use of NIRT modeling in personality measurement. Chernyshenko et al. fitted the 2PLM, the 3PLM, a graded response model, and Levine's nonparametric maximum-likelihood formula scoring models to dichotomous and polytomous data of the Sixteen Personality Factor Questionnaire (Conn & Rieke, 1994). They concluded that the nonparametric model provided the best fit of the models considered. Chernyshenko et al. and also Reise and Waller (2003) concluded that the response process underlying personality measurement is less well-understood than the response process in the cognitive domain. This being the case, we argue that using NIRT models based on exploring the simple covariance structure between items and based on nonparametric regression will lead to useful information that (a) can be interpreted very easily by practitioners, (b) avoids forcing the data ...

The Dispositional Hope Scale (DHS; C. R. Snyder et al., 1991) consists of two subsets of items measuring Agency and Pathways. The authors used bifactor analysis to evaluate the dimensionality structure of the scale. Data from 676 persons (295 psychiatric patients, 112 delinquents, and 269 students) were analyzed. The authors conclude that although the Pathway items seem to explain some additional variance when the Hope scale variance is partionalized out, the DHS allows unidimensional measurement.

In replicating the findings of Gabrys, et al. (1985), the Zung Self-rating Depression Scale discriminated between depressed and nondepressed patients in a day-clinic population. There is, however, considerably more overlap in group scores which makes prediction difficult for the individual case. The influence of sex on the Zung total score, indicating women score higher than men, requires consideration be given to matching experimental groups.

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