A gentle introduction to Bayesian posterior predictive checking for single-case researchers

A gentle introduction to Bayesian posterior predictive checking for single-case researchers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A gentle introduction to Bayesian posterior predictive checking for single-case researchers Paulina Grekov, James E. Pustejovsky This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7032595/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Journal of Behavioral Education → Version 1 posted 11 You are reading this latest preprint version Abstract Although researchers typically rely on visual analysis to draw conclusions about functional relations in single-case designs (SCDs), a growing collection of statistical methods have been proposed to augment visual assessment. With the introduction of increasingly complex statistical models, single-case researchers need techniques for evaluating their plausibility and utility. One potentially useful method for such model assessment is Bayesian posterior predictive checking (PPC), which involves simulating artificial data based on an estimated model and comparing the features of the simulated data to features of actual data. We provide a non-technical introduction to the use of PPCs for assessing the plausibility of statistical models for SCD data. We propose that PPCs should focus on data features that are of central interest in visual analysis. We demonstrate how PPCs can be represented in graphical form. We illustrate these techniques by re-analyzing data from two previously conducted studies: an across-participant multiple-baseline design assessing an oral reading fluency intervention and a reversal design investigating the effects of a group contingency intervention on inappropriate verbalizations. single-case design multilevel modeling posterior predictive checks model diagnostics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Journal of Behavioral Education → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviews received at journal 01 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 07 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 02 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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