Bayesian Hypothesis Testing for Single-Subject Designsde Vries, R. M. & Morey, R. D., Jun-2013, In : Psychological Methods. 18, 2, p. 165-185 21 p.
Research output: Contribution to journal › Article › Academic › peer-review
Researchers using single-subject designs are typically interested in score differences between intervention phases, such as differences in means or trends. If intervention effects are suspected in data, it is desirable to determine how much evidence the data show for an intervention effect. In Bayesian statistics, Bayes factors quantify the evidence in the data for competing hypotheses. We introduce new Bayes factor tests for single-subject data with 2 phases, taking serial dependency into account: a time-series extension of Rouder, Speckman, Sun, Morey, and Iverson's (2009) Jeffreys-Zellner-Siow Bayes factor for mean differences, and a time-series Bayes factor for testing differences in intercepts and slopes. The models we describe are closely related to interrupted time-series models (McDowall, McCleary, Meidinger, & Hay, 1980).
|Number of pages||21|
|Publication status||Published - Jun-2013|
- single-subject, Bayes factor, Bayesian estimation, intervention effect, trend difference, RANDOMIZATION TESTS, AUTO-CORRELATION, NULL HYPOTHESIS, EFFECT SIZES, R-PACKAGE, P-VALUES, INTERVENTION, PERCENTAGE, REGRESSION, CHAIN