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1.	Mulder, J., et al. (author) Bayesian model selection of informative hypotheses for repeated measurements 2009 In: Journal of mathematical psychology (Print). - : Elsevier. - 0022-2496 .- 1096-0880. ; 53:6, s. 530-546 Journal article (peer-reviewed)abstract When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the prior. To avoid subjective prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior priors. A simulation study and an empirical example from developmental psychology show that this prior results in Bayes factors with desirable properties.

Mulder, J., et al. (author)
Bayesian model selection of informative hypotheses for repeated measurements
2009
In: Journal of mathematical psychology (Print). - : Elsevier. - 0022-2496 .- 1096-0880. ; 53:6, s. 530-546
Journal article (peer-reviewed)abstract
- When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the prior. To avoid subjective prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior priors. A simulation study and an empirical example from developmental psychology show that this prior results in Bayes factors with desirable properties.

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