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A randomization test wrapper for synthesizing single-case experiments using multilevel models: A Monte Carlo simulation study.

Tijdschriftbijdrage - Tijdschriftartikel

Multilevel models (MLMs) have been proposed in single-case research to synthesize data from a group of cases in a multiple-baseline design (MBD). A limitation of this approach is that MLMs require several statistical assumptions that are often violated in single-case research. In this article we propose a solution to this limitation by presenting a randomization test (RT) wrapper for MLMs which offers a nonparametric way to evaluate treatment effects, without making distributional assumptions or an assumption of random sampling. We present the rationale underlying the proposed technique and validate its performance (with respect to type I error rate and power) compared to parametric statistical inference in MLMs in the context of evaluating the average treatment effect across cases in an MBD. We performed a simulation study that manipulated the number of cases and observations per case in a dataset, the data variability between cases, the distributional characteristics of the data, the level of autocorrelation, and the size of the treatment effect in the data. The results showed that the power of the RT wrapper is superior to the power of parametric tests based on F-distributions for MBDs with fewer than five cases and that the Type I error rate of the RT wrapper is controlled for bimodal data whereas this is not the case for traditional MLMs.
Tijdschrift: Behavior Research Methods
ISSN: 1554-351X
Issue: 2
Volume: 52
Pagina's: 654 - 666
Jaar van publicatie:2020
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:3
CSS-citation score:1
Authors from:Higher Education
Toegankelijkheid:Open