Details

Autor(en) / Beteiligte

• Lachowicz, Mark J
• Preacher, Kristopher J
• Kelley, Ken
• Harlow, Lisa L

Titel

A Novel Measure of Effect Size for Mediation Analysis

Ist Teil von

• Psychological methods, 2018-06, Vol.23 (2), p.244-261

Ort / Verlag

United States: American Psychological Association

Erscheinungsjahr

2018

Quelle

MEDLINE

Beschreibungen/Notizen

• Abstract Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirable properties. We also derive an expression for the bias of the sample estimator for the proposed effect size measure and propose an adjusted version of the estimator. We present a Monte Carlo simulation study conducted to examine the finite sampling properties of the adjusted and unadjusted estimators, which shows that the adjusted estimator is effective at recovering the true value it estimates. Finally, we demonstrate the use of the effect size measure with an empirical example. We provide freely available software so that researchers can immediately implement the methods we discuss. Our developments here extend the existing literature on effect sizes and mediation by developing a potentially useful method of communicating the magnitude of mediation. Translational Abstract An effect size is often used to translate a result obtained from a specific study into a metric that is independent of arbitrary characteristics of the study design (e.g., variable scales), making it easier for researchers to communicate the importance of their results and compare them with those obtained from other studies. The purpose of this research is to propose such an effect size for mediation analysis. Mediation analysis is used to examine the processes through which a predictor has an effect on an outcome through intervening variables called mediators. The component of an effect transmitted via a mediator is known as an indirect effect. Although indirect effects are commonly reported, effect size measures for them have yet to be firmly established. We show that our proposed measure is an attractive option for several reasons, but most importantly that it (a) has an intuitive interpretation, (b) quantifies the indirect effect independent of arbitrary design choices, and (c) can be used to draw valid inferences for sample sizes and effect magnitudes common in applied research. We then demonstrate the application and interpretation of the effect size using real data, and provide freely available software so researchers may immediately use the measure in their research.