Fitting measurement models to vocational interest data: Are dominance models ideal?

Abstract

In this study, the authors examined the item response process underlying 3 vocational interest inventories: the Occupational Preference Inventory (C.-P. Deng, P. I. Armstrong, & J. Rounds, 2007), the Interest Profiler (J. Rounds, T. Smith, L. Hubert, P. Lewis, & D. Rivkin, 1999; J. Rounds, C. M. Walker, et al., 1999), and the Interest Finder (J. E. Wall & H. E. Baker, 1997; J. E. Wall, L. L. Wise, & H. E. Baker, 1996). Item response theory (IRT) dominance models, such as the 2-parameter and 3-parameter logistic models, assume that item response functions (IRFs) are monotonically increasing as the latent trait increases. In contrast, IRT ideal point models, such as the generalized graded unfolding model, have IRFs that peak where the latent trait matches the item. Ideal point models are expected to fit better because vocational interest inventories ask about typical behavior, as opposed to requiring maximal performance. Results show that across all 3 interest inventories, the ideal point model provided better descriptions of the response process. The importance of specifying the correct item response model for precise measurement is discussed. In particular, scores computed by a dominance model were shown to be sometimes illogical: individuals endorsing mostly realistic or mostly social items were given similar scores, whereas scores based on an ideal point model were sensitive to which type of items respondents endorsed.

Fitting IRT Models to Dichotomous and Polytomous Data: Assessing the Relative Model–Data Fit of Ideal Point and Dominance Models

“…The findings are consistent with past literature examining the relative fits of dominance and ideal point IRT models to personality, attitudes, and vocational interest data, where there is a theoretical expectation for an ideal point response process (see Tay et al, 2009)-the GGUM fit such data much better than the 2PLM. Because the examination of personality, attitudinal, or vocational interest data are based on smaller sample sizes, it is believed that the present simulation studies effectively capture the range of conditions used by many researchers when evaluating model-data fit.…”

Detecting Differential Item Functioning of Polytomous Items for an Ideal Point Response Process

“…personality, attitudes, and vocational interests) are most appropriately characterized by an ideal point response process (Drasgow, Chernyshenko, & Stark, 2010a, 2010bRoberts, Laughlin, & Wedell, 1999;Stark, Chernyshenko, Drasgow, & Williams, 2006;Tay & Drasgow, 2012;Tay, Drasgow, Rounds, & Williams, 2009).…”

Investigation of Vocational Interest and Preference in Terms of Gender and Socio-economic Status

“…It can be asserted that these findings are in conformity with the gender-vocational interest studies. Many studies show that the interest of the women for social vocations, which require working with people, is higher, while interest of men for the vocations which require working with objects and abstract concepts is higher (Lippa, 1998(Lippa, , 2005Low et al, 2005;Rounds, 1995;Su et al,2009;Tay et al, 2009;Yılmaz, 2006). In addition, according to Low and Rounds (2007) women and men tend to be interested in activities and vocations which are stereotyped for their gender.…”

“…Gender is the most explicit variable in distinguishing interests (Erdil, 2006;Lippa, 1998Lippa, , 2005Low et al, 2005;Rounds, 1995;Sayın, 2000;Su, Rounds, & Armstrong, 2009;Tay, Drasgow, Rounds, & Williams, 2009). Many studies, literature reviews and meta analyses show that men are interested in vocations concerning objects, whereas women are interested in vocations in which you work with people.…”