Academic Journal
A Gradual Effects Model for Single-Case Designs.
| Τίτλος: | A Gradual Effects Model for Single-Case Designs. |
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| Συγγραφείς: | Swan DM; a The University of Texas at Austin., Pustejovsky JE; a The University of Texas at Austin. |
| Πηγή: | Multivariate behavioral research [Multivariate Behav Res] 2018 Jul-Aug; Vol. 53 (4), pp. 574-593. Date of Electronic Publication: 2018 May 14. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Taylor & Francis Group Country of Publication: United States NLM ID: 0046052 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-7906 (Electronic) Linking ISSN: 00273171 NLM ISO Abbreviation: Multivariate Behav Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2009- > : Philadelphia, PA : Taylor & Francis Group Original Publication: Fort Worth, Tex. : Society of Multivariate Experimental Psychology |
| Ιατρικοί όροι (MeSH): | Models, Statistical*, Computer Simulation ; Data Interpretation, Statistical ; Humans ; Meta-Analysis as Topic ; Monte Carlo Method ; Nonlinear Dynamics |
| Περίληψη: | Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data. |
| Contributed Indexing: | Keywords: Effect size; generalized linear model; intervention analysis; log response ratio; meta-analysis; single-case research |
| Entry Date(s): | Date Created: 20180515 Date Completed: 20181217 Latest Revision: 20181217 |
| Update Code: | 20260130 |
| DOI: | 10.1080/00273171.2018.1466681 |
| PMID: | 29757002 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1532-7906 |
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| DOI: | 10.1080/00273171.2018.1466681 |