Abstract
Respondents are often requested to provide a response to Likert-type or rating-scale items during the assessment of attitude, interest, and personality to measure a variety of latent traits. Extreme response style (ERS), which is defined as a consistent and systematic tendency of a person to locate on a limited number of available rating-scale options, may distort the test validity. Several latent trait models have been proposed to address ERS, but all these models have limitations. Mixture random-effect item response theory (IRT) models for ERS are developed in this study to simultaneously identify the mixtures of latent classes from different ERS levels and detect the possible differential functioning items that result from different latent mixtures. The model parameters can be recovered fairly well in a series of simulations that use Bayesian estimation with the WinBUGS program. In addition, the model parameters in the developed models can be used to identify items that are likely to elicit ERS. The results show that a long test and large sample can improve the parameter estimation process; the precision of the parameter estimates increases with the number of response options, and the model parameter estimation outperforms the person parameter estimation. Ignoring the mixtures and ERS results in substantial rank-order changes in the target latent trait and a reduced classification accuracy of the response styles. An empirical survey of emotional intelligence in college students is presented to demonstrate the applications and implications of the new models.
Highlights
Likert-type scales and rating scales are widely used for self-report surveys in the social sciences and psychological assessments to measure a variety of latent traits, such as personality, interest, and attitude
Several approaches have been proposed to control for the influence of Extreme response style (ERS) on item responses, and each has applicability and practicability limitations in that respondents’ tendencies toward ERS cannot be jointly quantified and classified, and the role that ERS play in the creation of differential item functioning (DIF) is not clear
Latent DIF may coincide with ERS, so we developed a new class of mixture item response theory (IRT) models in this study to simultaneously identify latent classes with respect to different response styles and to detect possible latent DIF among these classes
Summary
Likert-type scales and rating scales are widely used for self-report surveys in the social sciences and psychological assessments to measure a variety of latent traits, such as personality, interest, and attitude. The scale ratings by an individual may exhibit a consistent and systematic tendency to locate on a limited number of the available rating-scale options. Under these circumstances, we say that the individual exhibits a particular response style. We say that the individual exhibits a particular response style Such styles can potentially distort the reliability and validity of investigative measures (De Jong et al, 2008; De Beuckelaer et al, 2010; Bolt and Newton, 2011; Plieninger and Meiser, 2014). Various response styles have been noted (Baumgartner and Steenkamp, 2001), which are assumed to be independent of the item content (Nunnally, 1978; Paulhus, 1991) and to be stable respondent characteristics over time (Berg, 1953; Hamilton, 1968; Bachman and O’Malley, 1984; Weijters et al, 2010)
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