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Su, Shiyang; Wang, Chun; Weiss, David J. – Educational and Psychological Measurement, 2021
S-X[superscript 2] is a popular item fit index that is available in commercial software packages such as "flex"MIRT. However, no research has systematically examined the performance of S-X[superscript 2] for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was…
Descriptors: Statistics, Goodness of Fit, Test Items, Models
Zhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Journal of Educational Measurement, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Zhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Grantee Submission, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Cho, April E.; Wang, Chun; Zhang, Xue; Xu, Gongjun – Grantee Submission, 2020
Multidimensional Item Response Theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that…
Descriptors: Item Response Theory, Mathematics, Statistical Inference, Maximum Likelihood Statistics
Wang, Chun; Xu, Gongjun; Zhang, Xue – Grantee Submission, 2019
When latent variables are used as outcomes in regression analysis, a common approach that is used to solve the ignored measurement error issue is to take a multilevel perspective on item response modeling (IRT). Although recent computational advancement allow efficient and accurate estimation of multilevel IRT models, we argue that a two-stage…
Descriptors: Error of Measurement, Item Response Theory, Regression (Statistics), Evaluation Methods
Wang, Chun; Zhang, Xue – Grantee Submission, 2019
The relations among alternative parameterizations of the binary factor analysis (FA) model and two-parameter logistic (2PL) item response theory (IRT) model have been thoroughly discussed in literature (e.g., Lord & Novick, 1968; Takane & de Leeuw, 1987; McDonald, 1999; Wirth & Edwards, 2007; Kamata & Bauer, 2008). However, the…
Descriptors: Test Items, Error of Measurement, Item Response Theory, Factor Analysis
Wang, Chun; Nydick, Steven W. – Journal of Educational and Behavioral Statistics, 2020
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and…
Descriptors: Item Response Theory, Longitudinal Studies, Comparative Analysis, Models
Wang, Chun; Chen, Ping; Jiang, Shengyu – Grantee Submission, 2019
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence questions remain as to how to…
Descriptors: Adaptive Testing, Test Items, Item Response Theory, Maximum Likelihood Statistics
Wang, Chun; Xu, Gongjun; Shang, Zhuoran; Kuncel, Nathan – Journal of Educational and Behavioral Statistics, 2018
The modern web-based technology greatly popularizes computer-administered testing, also known as online testing. When these online tests are administered continuously within a certain "testing window," many items are likely to be exposed and compromised, posing a type of test security concern. In addition, if the testing time is limited,…
Descriptors: Computer Assisted Testing, Cheating, Guessing (Tests), Item Response Theory
Zhang, Xue; Wang, Chun; Tao, Jian – Grantee Submission, 2018
Testing item-level fit is important in scale development to guide item revision/deletion. Many item-level fit indices have been proposed in literature, yet none of them were directly applicable to an important family of models, namely, the higher order item response theory (HO-IRT) models. In this study, chi-square-based fit indices (i.e., Yen's…
Descriptors: Item Response Theory, Models, Test Items, Goodness of Fit
Wang, Chun; Nydick, Steven W. – Grantee Submission, 2019
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve (LGC) model (e.g., McArdle, 1988) and extended the assessment of growth to multidimensional IRT models (e.g., Hsieh, von Eye, & Maier, 2010; Huang, 2013) and higher-order IRT models…
Descriptors: Longitudinal Studies, Item Response Theory, Comparative Analysis, Models
Lu, Jing; Wang, Chun – Journal of Educational Measurement, 2020
Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we…
Descriptors: Item Response Theory, Test Items, Standardized Tests, Responses