Paulina Grekov, James E. Pustejovsky, David A. Klingbeil
{"title":"Flexible distributional models for meta-analysis of reading fluency outcomes from single-case designs: An examination using Bayesian methods","authors":"Paulina Grekov, James E. Pustejovsky, David A. Klingbeil","doi":"10.1016/j.jsp.2025.101429","DOIUrl":null,"url":null,"abstract":"<div><div>There is growing interest in statistical modeling of data from single-case design (SCD) research. However, currently available methods such as hierarchical linear models and generalized linear mixed models have assumptions that may limit their utility for applied SCDs, such as those that use curriculum-based measures of academic performance as outcomes. In the present paper, we demonstrate use of a flexible class of distributional models, known as generalized additive models for location, scale, and shape (GAMLSS), to evaluate different distributional families and modeling specifications for reading curriculum-based measures of reading fluency data drawn from SCD studies of academic interventions. Using Bayesian methods and graphical posterior predictive checks, we evaluated GAMLSS based on normal (Gaussian), Poisson, and negative binomial distributional families. We also evaluated the extent to which the dispersion, or variability of outcomes, itself varied across studies and across participants within studies. We found that negative binomial models with heterogeneous dispersions fit better than other distributional families and closely reproduced features of the observed data. Findings highlight the need to consider a broader set of distributional families when developing meta-analytic models of SCD data as well as the need to consider how the degree of dispersion may vary from study to study. We discuss implications for future methodological research and for meta-analysis of SCDs.</div></div>","PeriodicalId":48232,"journal":{"name":"Journal of School Psychology","volume":"110 ","pages":"Article 101429"},"PeriodicalIF":3.8000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of School Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022440525000020","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, SOCIAL","Score":null,"Total":0}
引用次数: 0
Abstract
There is growing interest in statistical modeling of data from single-case design (SCD) research. However, currently available methods such as hierarchical linear models and generalized linear mixed models have assumptions that may limit their utility for applied SCDs, such as those that use curriculum-based measures of academic performance as outcomes. In the present paper, we demonstrate use of a flexible class of distributional models, known as generalized additive models for location, scale, and shape (GAMLSS), to evaluate different distributional families and modeling specifications for reading curriculum-based measures of reading fluency data drawn from SCD studies of academic interventions. Using Bayesian methods and graphical posterior predictive checks, we evaluated GAMLSS based on normal (Gaussian), Poisson, and negative binomial distributional families. We also evaluated the extent to which the dispersion, or variability of outcomes, itself varied across studies and across participants within studies. We found that negative binomial models with heterogeneous dispersions fit better than other distributional families and closely reproduced features of the observed data. Findings highlight the need to consider a broader set of distributional families when developing meta-analytic models of SCD data as well as the need to consider how the degree of dispersion may vary from study to study. We discuss implications for future methodological research and for meta-analysis of SCDs.
期刊介绍:
The Journal of School Psychology publishes original empirical articles and critical reviews of the literature on research and practices relevant to psychological and behavioral processes in school settings. JSP presents research on intervention mechanisms and approaches; schooling effects on the development of social, cognitive, mental-health, and achievement-related outcomes; assessment; and consultation. Submissions from a variety of disciplines are encouraged. All manuscripts are read by the Editor and one or more editorial consultants with the intent of providing appropriate and constructive written reviews.