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Methods for Modeling Autocorrelation and Handling Missing Data in Mediation Analysis in Single Case Experimental
Emma Somer1, Christian Gische2, Milica Miočević1
1Department of Psychology, 5620McGill University, Montreal, QC, Canada.
Evaluation & the Health Professions
|February 28, 2022
Summary
This study enhances mediation analysis for single-case experimental designs (SCEDs). Feasible generalized least squares (FGLS) and autoregressive (AR(1)) models effectively handle autocorrelation and missing data in SCEDs.
Area of Science:
- Behavioral Science
- Psychology
- Research Methodology
Background:
- Single-case experimental designs (SCEDs) are gaining prominence as alternatives to group designs.
- Mediation analysis in SCEDs helps elucidate intervention mechanisms but faces challenges like autocorrelation and missing data.
- Recent advancements in SCED mediation analysis methods are emerging.
Purpose of the Study:
- To extend indirect effect estimation in piecewise regression for SCEDs.
- To evaluate methods for modeling autocorrelation (Newey-West, FGLS, AR(1)) and handling missing data (multiple imputation).
Main Methods:
- Piecewise regression analysis within SCEDs.
- Evaluation of Newey-West (NW), feasible generalized least squares (FGLS), and autoregressive order one (AR(1)) for autocorrelation.
- Assessment of multiple imputation for data missing completely at random.
Main Results:
- FGLS and AR(1) demonstrated superior efficiency, Type I error rates, and coverage compared to NW and OLS.
- OLS showed higher power in larger samples.
- Method performance remained consistent with 0% and 20% missing data, but 50% missing data resulted in poor power and biased estimates.
Conclusions:
- FGLS and AR(1) are recommended for mediation analysis in SCEDs with autocorrelation and missing data.
- Researchers should be cautious with high percentages of missing data (≥50%) due to potential bias and reduced power.
- Findings offer practical guidance for applied researchers using SCEDs.
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