An introduction to Bayesian model selection for evaluating informative hypotheses

Rens van de Schoot*, Joris Mulder, Herbert Hoijtink, Marcel A. G. van Aken, Judith Semon Dubas, Bram Orobio de Castro, Wim Meeus, Jan-Willem Romeijn

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

16 Citaten (Scopus)
2 Downloads (Pure)


Most researchers have specific expectations concerning their research questions. These may be derived from theory, empirical evidence, or both. Yet despite these expectations, most investigators still use null hypothesis testing to evaluate their data, that is, when analysing their data they ignore the expectations they have. In the present article, Bayesian model selection is presented as a means to evaluate the expectations researchers have, that is, to evaluate so called informative hypotheses. Although the methodology to do this has been described in previous articles, these are rather technical and have mainly been published in statistical journals. The main objective of the present article is to provide a basic introduction to the evaluation of informative hypotheses using Bayesian model selection. Moreover, what is new in comparison to previous publications on this topic is that we provide guidelines on how to interpret the results. Bayesian evaluation of informative hypotheses is illustrated using an example concerning psychosocial functioning and the interplay between personality and support from family.

Originele taal-2English
Pagina's (van-tot)713-729
Aantal pagina's17
TijdschriftEuropean Journal of Developmental Psychology
Nummer van het tijdschrift6
StatusPublished - 2011

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