Evaluating manifest monotonicity using Bayes factors

J. Tijmstra, H. Hoijtink, K. Sijtsma

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
112 Downloads (Pure)


The assumption of latent monotonicity in item response theory models for dichotomous data cannot be evaluated directly, but observable consequences such as manifest monotonicity facilitate the assessment of latent monotonicity in real data. Standard methods for evaluating manifest monotonicity typically produce a test statistic that is geared toward falsification, which can only provide indirect support in favor of manifest monotonicity. We propose the use of Bayes factors to quantify the degree of support available in the data in favor of manifest monotonicity or against manifest monotonicity. Through the use of informative hypotheses, this procedure can also be used to determine the support for manifest monotonicity over substantively or statistically relevant alternatives to manifest monotonicity, rendering the procedure highly flexible. The performance of the procedure is evaluated using a simulation study, and the application of the procedure is illustrated using empirical data.
Keywords: Bayes factor, essential monotonicity, item response theory, latent monotonicity, manifest monotonicity
Original languageEnglish
Pages (from-to)880-896
Issue number4
Publication statusPublished - 2015


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