Human Development & Quantitative Methodology
Permanent URI for this communityhttp://hdl.handle.net/1903/2248
The departments within the College of Education were reorganized and renamed as of July 1, 2011. This department incorporates the former departments of Measurement, Statistics & Evaluation; Human Development; and the Institute for Child Study.
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Item SUBSCORE REPORTING FOR DOUBLE-CODED INNOVATIVE ITEMS EMBEDDED IN MULTIPLE CONTEXTS(2018) Li, Chen; Jiao, Hong; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Reporting subscores is a prevalent practice in standardized tests to provide diagnostic information for learning and instruction. Previous research has developed various methods for reporting subscores (e.g. de la Torre & Patz, 2005; Wainer et al., 2001; Wang, Chen, & Cheng, 2004; Yao & Boughton, 2007; Yen, 1987). However, the existing methods are not suitable for reporting subscores for a test with innovative item types, such as double-coded items and paired stimuli. This study proposes a two-parameter doubly testlet model with internal restrictions on the item difficulties (2PL-DT-MIRID) to report subscores for a test with double-coded items embedded in paired-testlets. The proposed model is based on a doubly-testlet model proposed by Jiao and Lissitz (2014) and the MIRID (Butter, De Boeck, & Verhelst, 1998). The proposed model has four major advantages in reporting subscores— (a) it reports subscores for a test with double-coded items in complex scenario structures, (b) it reports subscores designed for content clustering, which is more common than subscores based on construct dimensionality in standardized tests, (c) it is computationally less challenging than the Multidimensional Item Response Theory (MIRT) models when estimating subscores, (d) it can be used to conduct Item Response Theory (IRT) based number-correct scoring (NCS, Yen, 1984a). A simulation study is conducted to evaluate the model parameter recovery, subscore estimation and subscore reliability. The simulation study manipulates three factors: (a) the magnitude of testlet effect variation, (b) the correlation between testlet effects for the dual testlets and (c) the percentage of double-coded items in the test. Further, the study compares the proposed model with other underspecified models in terms of model parameter estimation and model fit. The result of the simulation study has shown that the proposed 2PL-DT-MIRID yields more accurate model parameter and subscore estimates, in general, when the testlet effect variation is small, the dual testlets are weakly correlated and there are more double-coded items in a test. Across the study conditions, the proposed model outperforms other competing models in model parameter estimation. The reliability yielded from models ignoring dual testlets are spuriously inflated, the 2PL-DTMIRID produces higher overall score reliability and subscore reliability than models ignoring double-coded items, in most study conditions. In terms of model fit, none of the model fit indices investigated in this study (i.e. AIC, BIC and DIC) can achieve satisfactory rates of identifying the proposed true model as the best fitting model.Item A MIXTURE RASCH MODEL WITH A COVARIATE:A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATION(2009) Dai, Yunyun; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mixtures of item response theory models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying structures. In particular, the impact of auxiliary variables, or covariates, for examinees in estimation has not been systematically explored. The goal of this dissertation is to carry out a systematically designed simulation study to investigate the performance of mixture Rasch model (MRM) under Bayesian estimation using Markov Chain Monte Carlo (MCMC) method. The dependent variables in this study are (1) the proportion of cases in which the generating mixture structure is recovered, and (2) among those cases in which the structure is recovered, the bias and root mean squared error of parameter estimates. The foci of the study are to use a flexible logistic regression model to parameterize the relation between latent class membership and the examinee covariate, to study MCMC estimation behavior in light of effect size, and to provide insights and suggestions on model application and model estimation.Item The Multidimensional Generalized Graded Unfolding Model for Assessment of Change across Repeated Measures(2008-05-13) Cui, Weiwei; Roberts, James S; Dayton, Chan M; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A multidimensional extension of the generalized graded unfolding model for repeated measures (GGUM-RM) is introduced and applied to analyze attitude change across time using responses collected by a Thurstone or Likert questionnaire. The model conceptualizes the change across time as separate latent variables and provides direct estimates of both individual and group change while accounting for the dependency among latent variables. The parameters and hyperparameters of GGUM-RM are estimated by fully Bayesian estimation method via WinBUGS. The accuracy of the estimation procedure is demonstrated by a simulation study, and the application of the GGUM-RM is illustrated by the analysis of attitude change toward abortion among college students.