Bayesian covariance structure modelling of responses and process data

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Abstract

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.

Original languageEnglish
Article number1675
JournalFrontiers in psychology
Volume10
DOIs
Publication statusPublished - 5 Aug 2019

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Keywords

  • Bayesian modelling
  • Covariance structure
  • Cross-classification
  • Educational Measurement
  • Latent variable modelling
  • Marginal modelling
  • Process data
  • Response times

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title = "Bayesian covariance structure modelling of responses and process data",
abstract = "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.",
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Bayesian covariance structure modelling of responses and process data. / Klotzke, Konrad; Fox, Jean Paul.

In: Frontiers in psychology, Vol. 10, 1675, 05.08.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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