A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research.
Single case experimental design (SCED) is an indispensable methodology when evaluating intervention efficacy. Despite long-standing success with using visual analyses to evaluate SCED data, this method has limited utility for conducting meta-analyses. This is critical because meta-analyses should dr...
| Publicado en: | Behavioral Disorders Vol. 46; no. 4; pp. 226 - 238 |
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| Autores principales: | , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151330950&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151330950 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01987429 BHD jtl: Behavioral Disorders issn: 01987429 maglogo: Y pubinfo: dt: Aug2021 vid: 46 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 151330950 146284329 151330950 151330950 10.1177/0198742920930704 151330950 ppf: 226 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research. aug: au: Natesan Batley, Prathiba Shukla Mehta, Smita Hitchcock, John H. affil: Brunel University London, UK sug: subj: Study Design Odds Ratio Effect Size Meta Analysis Mental Disorders Data Analysis, Statistical Interrupted Time Series Analysis ab: Single case experimental design (SCED) is an indispensable methodology when evaluating intervention efficacy. Despite long-standing success with using visual analyses to evaluate SCED data, this method has limited utility for conducting meta-analyses. This is critical because meta-analyses should drive practice and policy in behavioral disorders more than evidence derived from individual SCEDs. Even when analyzing data from individual studies, there is merit to using multiple analytic methods since statistical analyses in SCED can be challenging given small sample sizes and autocorrelated data. These complexities are exacerbated when using count data, which are common in SCEDs. Bayesian methods can be used to develop new statistical procedures that may address these challenges. The purpose of the present study was to formulate a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data that would obviate the need for small sample corrections. This effect size is the first step toward building a between-subject rate ratio that can be used for meta-analyses. We illustrate this within-subject effect size using real data for an ABAB design and provide codes for practitioners who may want to compute BRR. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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