caa a22 4500 606241906 CHVBK 20210128101315.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11336-014-9420-2 doi (NATIONALLICENCE)springer-10.1007/s11336-014-9420-2 A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis [Elektronische Daten] [Yan Huo, Jimmy de la Torre, Eun-Young Mun, Su-Young Kim, Anne Ray, Yang Jiao, Helene White] The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research. The Psychometric Society, 2014 IDA nationallicence IRT nationallicence MCMC nationallicence multi-unidimensional nationallicence multiple groups nationallicence Huo Yan Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut de la Torre Jimmy Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut Mun Eun-Young Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut Kim Su-Young Department of Psychology, Ewha Womans University, Seoul, Republic of Korea aut Ray Anne Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut Jiao Yang Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut White Helene Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 834-855 0033-3123 80:3<834 2015 80 11336 https://doi.org/10.1007/s11336-014-9420-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-014-9420-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huo Yan Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut NATIONALLICENCE 700 1- de la Torre Jimmy Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut NATIONALLICENCE 700 1- Mun Eun-Young Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA aut NATIONALLICENCE 700 1- Kim Su-Young Department of Psychology, Ewha Womans University, Seoul, Republic of Korea aut NATIONALLICENCE 700 1- Ray Anne Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut NATIONALLICENCE 700 1- Jiao Yang Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut NATIONALLICENCE 700 1- White Helene Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 834-855 0033-3123 80:3<834 2015 80 11336