Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 7 |
Since 2006 (last 20 years) | 9 |
Descriptor
Author
Moeyaert, Mariola | 9 |
Van den Noortgate, Wim | 5 |
Beretvas, S. Natasha | 3 |
Ugille, Maaike | 3 |
Yang, Panpan | 3 |
Ferron, John | 2 |
Ferron, John M. | 2 |
Xu, Xinyun | 2 |
Akhmedjanova, Diana | 1 |
Beretvas, Tasha | 1 |
Declercq, Lies | 1 |
More ▼ |
Publication Type
Reports - Research | 9 |
Journal Articles | 5 |
Information Analyses | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Moeyaert, Mariola; Yang, Panpan; Xu, Xinyun; Kim, Esther – Grantee Submission, 2021
Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant…
Descriptors: Meta Analysis, Case Studies, Research Design, Hierarchical Linear Modeling
Moeyaert, Mariola; Yang, Panpan; Xu, Xinyun – Grantee Submission, 2021
This study investigated the power of two-level hierarchical linear modeling (HLM) to explain variability in intervention effectiveness between participants in context of single-case experimental design (SCED) research. HLM is a flexible technique that allows the inclusion of participant characteristics (e.g., age, gender, and disability types) as…
Descriptors: Hierarchical Linear Modeling, Intervention, Research Design, Participant Characteristics
Moeyaert, Mariola; Yang, Panpan – Grantee Submission, 2021
This study introduces an innovative meta-analytic approach, two-stage multilevel meta-analysis that considers the hierarchical structure of single-case experimental design (SCED) data. This approach is unique as it is suitable to include moderators at the intervention level, participant level, and study level, and is therefore especially…
Descriptors: Hierarchical Linear Modeling, Meta Analysis, Research Design, Case Studies
Moeyaert, Mariola; Akhmedjanova, Diana; Ferron, John; Beretvas, S. Natasha; Van den Noortgate, Wim – Grantee Submission, 2020
The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for…
Descriptors: Effect Size, Research Methodology, Research Design, Hierarchical Linear Modeling
Jamshidi, Laleh; Declercq, Lies; Fernández-Castilla, Belén; Ferron, John M.; Moeyaert, Mariola; Beretvas, S. Natasha; Van den Noortgate, Wim – Grantee Submission, 2020
The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Meta Analysis, Regression (Statistics)
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
Heyvaert, Mieke; Moeyaert, Mariola; Verkempynck, Paul; Van den Noortgate, Wim; Vervloet, Marlies; Ugille, Maaike; Onghena, Patrick – Journal of Experimental Education, 2017
This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test "p" values (RTcombiP). Four factors were manipulated: mean intervention effect,…
Descriptors: Monte Carlo Methods, Simulation, Intervention, Replication (Evaluation)
Van den Noortgate, Wim; Moeyaert, Mariola; Ugille, Maaike; Beretvas, Tasha; Ferron, John – Society for Research on Educational Effectiveness, 2014
Due to an increasing interest in the use of single-subject experimental designs (SSEDs), appropriate techniques are needed to analyze this type of data. The purpose of this paper proposal is to present four studies (Beretvas, Hembry, Van den Noortgate, & Ferron, 2013; Bunuan, Hembry & Beretvas, 2013; Moeyaert, Ugille, Ferron, Beretvas,…
Descriptors: Research Methodology, Simulation, Bias, Statistical Inference
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2014
One approach for combining single-case data involves use of multilevel modeling. In this article, the authors use a Monte Carlo simulation study to inform applied researchers under which realistic conditions the three-level model is appropriate. The authors vary the value of the immediate treatment effect and the treatment's effect on the time…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Case Studies, Research Design