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Moeyaert, Mariola – Behavioral Disorders, 2019
Multilevel meta-analysis is an innovative synthesis technique used for the quantitative integration of effect size estimates across participants and across studies. The quantitative summary allows for objective, evidence-based, and informed decisions in research, practice, and policy. Based on previous methodological work, the technique results in…
Descriptors: Meta Analysis, Evidence, Correlation, Predictor Variables
Ottley, Jennifer Riggie; Ferron, John M.; Hanline, Mary Frances – Grantee Submission, 2016
The purpose of this study was to explain the variability in data collected from a single-case design study and to identify predictors of communicative outcomes for children with developmental delays or disabilities (n = 4). Using SAS® University Edition, we fit multilevel models with time nested within children. Children's level of baseline…
Descriptors: Communication (Thought Transfer), Hierarchical Linear Modeling, Research Design, Predictor Variables
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Gage, Nicholas A.; Lewis, Timothy J. – Journal of Special Education, 2014
The identification of evidence-based practices continues to provoke issues of disagreement across multiple fields. One area of contention is the role of single-subject design (SSD) research in providing scientific evidence. The debate about SSD's utility centers on three issues: sample size, effect size, and serial dependence. One potential…
Descriptors: Hierarchical Linear Modeling, Meta Analysis, Research Design, Sample Size
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Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
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Davis, Dawn H.; Gagne, Phill; Fredrick, Laura D.; Alberto, Paul A.; Waugh, Rebecca E.; Haardorfer, Regine – Behavior Modification, 2013
The purpose of this article is to demonstrate how hierarchical linear modeling (HLM) can be used to enhance visual analysis of single-case research (SCR) designs. First, the authors demonstrated the use of growth modeling via HLM to augment visual analysis of a sophisticated single-case study. Data were used from a delayed multiple baseline…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Research Design, Case Studies
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Wang, Shin-Yi; Parrila, Rauno; Cui, Ying – Journal of Autism and Developmental Disorders, 2013
This meta-analysis used hierarchical linear modeling to examine 115 single-case studies with 343 participants that examined the effectiveness of social skills interventions for individuals with autism spectrum disorder (ASD). The average effect size of the included studies was 1.40 (SD = 0.43, 95% CL = 1.32-1.48, N = 115). In the further, several…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Case Studies, Pervasive Developmental Disorders