Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies

Frontiers in Psychology

article

Eun-Young Mun¹  Yan Huo¹  Helene R. White²  Sumihiro Suzuki¹  Jimmy de la Torre³ 

[1] University of North Texas Health Science Center, United States;Rutgers University Center of Alcohol Studies, United States;The University of Hong Kong

关键词: higher-order IRT;    multivariate IRT;    multi-group IRT;    Bayesian estimation;    individual participant data;    meta-analysis;   

DOI  :  10.3389/fpsyg.2019.01328

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.

【 授权许可】

CC BY