Related Experiment Video
Updated: Jun 7, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
Estimating Mediation Effects in ABAB Reversal Designs
Matthew J Valente1, Jinyong Pang1, Judith J M Rijnhart1
1University of South Florida, USA.
Abstract:
Single-Case Experimental Designs (SCEDs), or N-of-1 trials, are commonly used to estimate intervention effects in many disciplines including in the treatment of youth mental health problems. SCEDs consist of repeated measurements of an outcome over time for a single case (e.g., student or patient) throughout one or more baseline phases and throughout one or more intervention phases. The manipulation of the baseline and intervention phase make the SCED a type of interrupted time series design, which is considered one of the most effective experimental designs for causal inference. An important step towards understanding why interventions are effective at producing a change in the outcome is through the investigation of mediating mechanisms. Hypotheses of mediating mechanisms involve an intervention variable which is hypothesized to affect an outcome through its effect on a mediating variable. Little work has attempted to combine mediation analysis and ABAB reversal designs. Therefore, the goals of this paper are to define, estimate, and interpret mediation effects for ABAB reversal designs. An empirical example is used to demonstrate how to estimate and interpret the mediation effects. R code is provided for researchers interested in estimating mediation effects in single-case reversal designs.
More Related Videos
13:00Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
08:05A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
Published on: January 5, 2018
Related Concept Videos
Group Design
Crossover Experiments
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Factorial Design
One-Way ANOVA