|Title:||Multilevel Item Bifactor Models with Semi-Nonparametric Latent Densities|
|Principal Investigator:||Yang, Ji Seung||Awardee:||University of Maryland, College Park|
|Program:||Statistical and Research Methodology in Education–Early Career [Program Details]|
|Award Period:||1½ years (7/1/15–12/31/16)||Award Amount:||$199,924|
|Type:||Methodological Innovation||Award Number:||R305D150052|
The purpose of this project is to develop a better way to deal with measurement error in predictors and to address the impact of non-normality in latent variable distributions. Predictors that contain measurement error yield attenuated correlation or regression coefficient estimates, which results in bias in treatment effect estimates. This problem is exacerbated in multilevel models when level-1 values are simply aggregated to an upper level as group means. By introducing a multilevel and multidimensional measurement model, the measurement error in the predictor can be more properly handled.
The research team will first develop software in R that can run the necessary models. Using simulation studies, the researchers will then investigate the extent to which various model facets lead to bias in the parameter estimates. A second set of simulation studies will compare the new model against current approaches in terms of parameter recovery under different conditions, including non-normal distributions of the trait being measured. The researchers will also use real data to demonstrate the utility of the new model. By the end of the project, the team expects to have released a user-friendly version of the software and to be working on disseminating the results of the research at conferences and in journals.
Journal article, monograph, or newsletter
Chen, Y., Li, X., Liu, J., Xu, G., and Ying, Z. (2017). Exploratory Item Classification Via Spectral Graph Clustering. Applied Psychological Measurement, 41(8), 579–599.
Wang, C., and Weiss, D.J. (2018). Multivariate Hypothesis Testing Methods for Evaluating Significant Individual Change. Applied Psychological Measurement, 42(3): 221–239.
Wang, C., Xu, G., Shang, Z., and Kuncel, N. (2018). Detecting Aberrant Behavior and Item Preknowledge: A Comparison of Mixture Modeling Method and Residual Method. Journal of Educational and Behavioral Statistics.
Xu, G., Chiou, S.H., Huang, C.Y., Wang, M.C., and Yan, J. (2017). Joint Scale-Change Models for Recurrent Events and Failure Time. Journal of the American Statistical Association, 112(518), 794–805.
Xu, G., and Shang, Z. (2017). Identifying Latent Structures in Restricted Latent Class Models. Journal of the American Statistical Association
Zhang, X., Wang, C., and Tao, J. (2018). Assessing Item-Level Fit for Higher Order Item Response Theory Models. Applied Psychological Measurement.
Gu, Y., and Xu, G. (2018). Partial Identifiability of Restricted Latent Class Models. arXiv preprint arXiv:1803.04353.