Impact Factor 2.089
2017 JCR, Clarivate Analytics 2018

Frontiers journals are at the top of citation and impact metrics

Methods ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychol. | doi: 10.3389/fpsyg.2019.01675

Bayesian Covariance Structure Modelling of Responses and Process Data

  • 1University of Twente, Netherlands

A novel Bayesian modelling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modelling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modelled. The local dependences are modelled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modelled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden’s hierarchical model (LHM; (van der Linden, 2007)). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed.

Keywords: Process data, Educational Measurement, bayesian modelling, covariance structure, Marginal modelling, Cross-classification, response times, Latent variable modelling

Received: 15 Sep 2018; Accepted: 02 Jul 2019.

Edited by:

Hong Jiao, University of Maryland, College Park, United States

Reviewed by:

Paul De Boeck, The Ohio State University, United States
Maria Bolsinova, University of Amsterdam, Netherlands  

Copyright: © 2019 Klotzke and Fox. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Konrad Klotzke, University of Twente, Enschede, Netherlands, k.klotzke@utwente.nl