Quantitative Psychology Brownbag

October 24, 2022

12:30PM - 1:30PM

Psychology 35

Add to Calendar 2022-10-24 12:30:00 2022-10-24 13:30:00 Quantitative Psychology Brownbag Department of Psychology The Ohio State University Title: Should we be doing this? A deep dive into conditional indirect effects Abstract: Researchers interested in understanding causal relationships must not only test if X causes Y, but how and/or when X causes Y. Mediation analysis is a tool that allows researchers to identify the mechanism(s) by which one variable causes another, whereas moderation analysis allows researchers to detect when one variable’s effect is heterogenous across levels of another variable (or multiple variables). Although these analyses lead to a deeper understanding of an observed relationship, they are still often too simplistic in isolation to properly model real-world effects. Combining mediation and moderation into a single analysis allows one to study conditional indirect effects—that is, when an indirect effect of X on Y is variable across the levels of a moderator. Methodological researchers have paid much attention on how to test for conditional indirect effects. However, considerably less work has been devoted to evaluating the performance of these proposed methods. A review of prior simulation studies reveals that current methods have relatively poor performance except for the most optimistic combinations of effect and sample size. Despite this, many substantive researchers continue to use these methods and rely on them for dichotomous decisions about and interpretations of such effects. A simulation study was conducted to compare inferential tests for conditional indirect effects with more plausible combinations of sample and effect size and variable type (e.g., dichotomous vs. continuous independent variables). All methods had low Type I error and power in many of the study conditions. These limitations in statistical performance can be ameliorated by a more careful presentation of the model results. Clear guidelines are presented for substantive researchers on how to best specify, test, and interpret conditional indirect effects. Future directions for methodological researchers are also discussed. Discussant: Frank Leyva Castro Psychology 35 OSU ASC Drupal 8 ascwebservices@osu.edu America/New_York public

2022-10-24 12:30:00 2022-10-24 13:30:00 Quantitative Psychology Brownbag Department of Psychology The Ohio State University Title: Should we be doing this? A deep dive into conditional indirect effects Abstract:  Researchers interested in understanding causal relationships must not only test if X causes Y, but how and/or when X causes Y. Mediation analysis is a tool that allows researchers to identify the mechanism(s) by which one variable causes another, whereas moderation analysis allows researchers to detect when one variable’s effect is heterogenous across levels of another variable (or multiple variables). Although these analyses lead to a deeper understanding of an observed relationship, they are still often too simplistic in isolation to properly model real-world effects. Combining mediation and moderation into a single analysis allows one to study conditional indirect effects—that is, when an indirect effect of X on Y is variable across the levels of a moderator. Methodological researchers have paid much attention on how to test for conditional indirect effects. However, considerably less work has been devoted to evaluating the performance of these proposed methods. A review of prior simulation studies reveals that current methods have relatively poor performance except for the most optimistic combinations of effect and sample size. Despite this, many substantive researchers continue to use these methods and rely on them for dichotomous decisions about and interpretations of such effects. A simulation study was conducted to compare inferential tests for conditional indirect effects with more plausible combinations of sample and effect size and variable type (e.g., dichotomous vs. continuous independent variables). All methods had low Type I error and power in many of the study conditions. These limitations in statistical performance can be ameliorated by a more careful presentation of the model results. Clear guidelines are presented for substantive researchers on how to best specify, test, and interpret conditional indirect effects. Future directions for methodological researchers are also discussed. Discussant: Frank Leyva Castro Psychology 35 Department of Psychology ASC-psychmainoffice@osu.edu America/New_York public

Department of Psychology
The Ohio State University

Title: Should we be doing this? A deep dive into conditional indirect effects

Abstract: Researchers interested in understanding causal relationships must not only test if X causes Y, but how and/or when X causes Y. Mediation analysis is a tool that allows researchers to identify the mechanism(s) by which one variable causes another, whereas moderation analysis allows researchers to detect when one variable’s effect is heterogenous across levels of another variable (or multiple variables). Although these analyses lead to a deeper understanding of an observed relationship, they are still often too simplistic in isolation to properly model real-world effects. Combining mediation and moderation into a single analysis allows one to study conditional indirect effects—that is, when an indirect effect of X on Y is variable across the levels of a moderator.

Methodological researchers have paid much attention on how to test for conditional indirect effects. However, considerably less work has been devoted to evaluating the performance of these proposed methods. A review of prior simulation studies reveals that current methods have relatively poor performance except for the most optimistic combinations of effect and sample size. Despite this, many substantive researchers continue to use these methods and rely on them for dichotomous decisions about and interpretations of such effects.

A simulation study was conducted to compare inferential tests for conditional indirect effects with more plausible combinations of sample and effect size and variable type (e.g., dichotomous vs. continuous independent variables). All methods had low Type I error and power in many of the study conditions. These limitations in statistical performance can be ameliorated by a more careful presentation of the model results. Clear guidelines are presented for substantive researchers on how to best specify, test, and interpret conditional indirect effects. Future directions for methodological researchers are also discussed.

Discussant: Frank Leyva Castro