Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Multilevel meta-analysis of single-case experimental designs using robust variance estimation. |
| Συγγραφείς: |
Chen M; Department of Educational Psychology, University of Wisconsin-Madison., Pustejovsky JE; Department of Educational Psychology, University of Wisconsin-Madison. |
| Πηγή: |
Psychological methods [Psychol Methods] 2024 Jun; Vol. 29 (3), pp. 537-560. Date of Electronic Publication: 2022 Jul 04. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: American Psychological Association Country of Publication: United States NLM ID: 9606928 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1463 (Electronic) Linking ISSN: 1082989X NLM ISO Abbreviation: Psychol Methods Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Washington, DC : American Psychological Association, c1996- |
| Ιατρικοί όροι (MeSH): |
Multilevel Analysis*/methods , Meta-Analysis as Topic* , Research Design* , Models, Statistical*, Psychology/methods ; Single-Case Studies as Topic/methods ; Humans ; Data Interpretation, Statistical ; Monte Carlo Method |
| Περίληψη: |
Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences. (PsycInfo Database Record (c) 2024 APA, all rights reserved). |
| Grant Information: |
US Department of Education; Institute of Education Sciences |
| Entry Date(s): |
Date Created: 20220705 Date Completed: 20240923 Latest Revision: 20241014 |
| Update Code: |
20260130 |
| DOI: |
10.1037/met0000510 |
| PMID: |
35786985 |
| Βάση Δεδομένων: |
MEDLINE |