Graduation Year
2013
Document Type
Dissertation
Degree
Ph.D.
Degree Granting Department
Psychology
Major Professor
Stephen Stark
Keywords
Forced Choice, Item Response Theory, Multidimensional IRT, Noncognitive Assessment
Abstract
The assessment of noncognitive constructs poses a number of challenges that set it apart from traditional cognitive ability measurement. Of particular concern is the influence of response biases and response styles that can influence the accuracy of scale scores. One strategy to address these concerns is to use alternative item presentation formats (such as multidimensional forced choice (MFC) pairs, triads, and tetrads) that may provide resistance to such biases. A variety of strategies for constructing and scoring these forced choice measured have been proposed, though they often require large sample sizes, are limited in the way that statements can vary in location, and (in some cases) require a separate precalibration phase prior to the scoring of forced-choice responses. This dissertation introduces new item response theory models for estimating item and person parameters from rank-order responses indicating preferences among two or more alternatives representing, for example, different personality dimensions. Parameters for this new model, called the Hyperbolic Cosine Model for Rank order responses (HCM-RANK), can be estimated using Markov chain Monte Carlo (MCMC) methods that allow for the simultaneous evaluation of item properties and person scores. The efficacy of the MCMC parameter estimation procedures for these new models was examined via three studies. Study 1 was a Monte Carlo simulation examining the efficacy of parameter recovery across levels of sample size, dimensionality, and approaches to item calibration and scoring. It was found that estimation accuracy improves with sample size, and trait scores and location parameters can be estimated reasonably well in small samples. Study 2 was a simulation examining the robustness of trait estimation to error introduced by substituting subject matter expert (SME) estimates of statement location for MCMC item parameter estimates and true item parameters. Only small decreases in accuracy relative to the true parameters were observed, suggesting that using SME ratings of statement location for scoring might be a viable short-term way of expediting MFC test deployment in field settings. Study 3 was included primarily to illustrate the use of the newly developed IRT models and estimation methods with real data. An empirical investigation comparing validities of personality measures using different item formats yielded mixed results and raised questions about multidimensional test construction practices that will be explored in future research. The presentation concludes with a discussion of MFC methods and potential applications in educational and workforce contexts.
Scholar Commons Citation
Seybert, Jacob, "A New Item Response Theory Model for Estimating Person Ability and Item Parameters for Multidimensional Rank Order Responses" (2013). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/4942
