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Synthesis of single-case design mediation effects using two-stage multilevel modeling
Mariola Moeyaert1, Milica Miočević2, Yaosheng Lou3
1Department of Educational and Counseling Psychology, University at Albany, the State University of New York, 1400 Washington Ave, Albany, NY, 12222, USA. mmoeyaert@albany.edu.
Behavior Research Methods
|May 29, 2026
Summary
This study introduces a two-stage multilevel modeling approach for mediation analysis in multiple-baseline designs (MBDs). The method accurately synthesizes indirect effects across participants, especially with more than three individuals.
Area of Science:
- Behavioral Science
- Psychology
- Research Methodology
Background:
- Mediation analysis in single-case experimental designs (SCEDs) is crucial for understanding intervention mechanisms.
- Existing methods are validated for AB designs but not for synthesizing indirect effects in multiple-baseline designs (MBDs).
Purpose of the Study:
- To investigate the performance of a two-stage multilevel modeling approach for synthesizing indirect effects in MBDs.
- To provide an empirical demonstration and interpretation of the modeling approach.
Main Methods:
- A large-scale Monte Carlo simulation study was employed to evaluate the two-stage multilevel modeling approach.
- The simulation examined various conditions, including the number of participants and mediator-outcome relations.
Main Results:
- Unbiased estimation of indirect effects was achieved with more than three participants.
- For statistical inference, at least 20 participants and a non-zero mediator-outcome relation are recommended for .95 coverage and controlled Type I error rates.
- Sufficient power to detect indirect effects requires at least eight participants and a mediator-outcome relation of 0.39 or higher.
Conclusions:
- The two-stage multilevel modeling approach is a promising method for mediation analysis in MBDs.
- The approach provides accurate and reliable results for synthesizing indirect effects across participants, with specific recommendations for statistical inference and power.
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