UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

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Author: search.filters.author.Li, TongyunSubject: search.filters.subject.Educational tests & measurements

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    DIFFERENT APPROACHES TO COVARIATE INCLUSION IN THE MIXTURE RASCH MODEL
    (2014) Li, Tongyun; Jiao, Hong; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The present dissertation project investigates different approaches to adding covariates and the impact in fitting mixture item response theory (IRT) models. Mixture IRT models serve as an important methodology for tackling several important psychometric issues in test development, including detecting latent differential item functioning (DIF). A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model (MRM) with both dichotomous and continuous covariates are fitted to several MRMs with misspecified covariates to examine the effects of covariate inclusion on model parameter estimation. In addition, both complete response data and incomplete response data with different types of missingness are considered in the present study in order to simulate practical assessment settings. Parameter estimation is carried out within a Bayesian framework vis-à-vis Markov chain Monte Carlo (MCMC) algorithms. Two empirical examples using the Programme for International Student Assessment (PISA) 2009 U.S. reading assessment data are presented to demonstrate the impact of different specifications of covariate effects for an MRM in real applications.