Robustness of Projective IRT to Misspecification of the Underlying Multidimensional Model. Issue 5 (July 2020)

• Record Type: Journal Article
• Authors: Strachan, Tyler; Ip, Edward; Fu, Yanyan; Ackerman, Terry; Chen, Shyh-Huei; Willse, John
• Is Part Of: Applied psychological measurement. Volume 44:Issue 5(2020)
• Page Start: 362
• Page End: 375
• Publication Date: 2020-07
• Subjects: multidimensional item response theory -- projective item response theory -- robustness -- misspecification; Psychometrics -- Periodicals; Psychological tests -- Periodicals; Psychology, Applied -- Periodicals; 150.1519505
• DOI: 10.1177/0146621620909894
• Languages: English
• ISSNs: 0146-6216

Abstract

As a method to derive a "purified" measure along a dimension of interest from response data that are potentially multidimensional in nature, the projective item response theory (PIRT) approach requires first fitting a multidimensional item response theory (MIRT) model to the data before projecting onto a dimension of interest. This study aims to explore how accurate the PIRT results are when the estimated MIRT model is misspecified. Specifically, we focus on using a (potentially misspecified) two-dimensional (2D)-MIRT for projection because of its advantages, including interpretability, identifiability, and computational stability, over higher dimensional models. Two large simulation studies (I and II) were conducted. Both studies examined whether the fitting of a 2D-MIRT is sufficient to recover the PIRT parameters when multiple nuisance dimensions exist in the test items, which were generated, respectively, under compensatory MIRT and bifactor models. Various factors were manipulated, including sample size, test length, latent factor correlation, and number of nuisance dimensions. The results from simulation studies I and II showed that the PIRT was overall robust to a misspecified 2D-MIRT. Smaller third and fourth simulation studies were done to evaluate recovery of the PIRT model parameters when the correctly specified higher dimensional MIRT or bifactor model was fitted with the response data. In addition, a real data set was used to illustrate the robustness of PIRT.