Methods for Modeling Autocorrelation and Handling Missing Data in Mediation Analysis in Single Case Experimental Designs (SCEDs). Issue 1 (March 2022)

• Record Type: Journal Article
• Authors: Somer, Emma; Gische, Christian; Miočević, Milica
• Other Names: Miočević Milica guest-editor.; Moeyaert Mariola guest-editor.; Montoya Amanda guest-editor.; Mayer Axel guest-editor.
• Is Part Of: Evaluation & the health professions. Volume 45:Issue 1(2022)
• Page Start: 36
• Page End: 53
• Publication Date: 2022-03
• Subjects: mediation analysis -- single-case experiment designs -- autocorrelation -- missing data -- small sample sizes; Medical care -- Evaluation -- Periodicals; Medicine -- Ability testing -- Periodicals; 362.105
• DOI: 10.1177/01632787211071136
• Languages: English
• ISSNs: 0163-2787

Abstract

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.