A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-incoefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.The purpose of this article is to compare statistical methods used to test a model in which an independent variable (X) causes an intervening variable (I), which in turn causes the dependent variable (Y). Many different disciplines use such models, with the terminology, assumptions, and statistical tests only partially overlapping among them. In psychology, the X → I → Y relation is often termed mediation (Baron & Kenny, 1986), sociology originally popularized the term indirect effect (Alwin & Hauser, 1975), and in epidemiology, it is termed the surrogate or intermediate endpoint effect (Freedman & Schatzkin, 1992). This article focuses on the statistical performance of each of the available tests of the effect of an intervening variable. Consideration of conceptual issues related to the definition of intervening variable effects is deferred to the final section of the Discussion.Hypotheses articulating measurable processes that intervene between the independent and dependent variables have long been proposed in psychology (e.g., MacCorquodale & Meehl, 1948;Woodworth, 1928). Such hypotheses are fundamental to theory in many areas of basic and applied psychology (Baron & Kenny, 1986;James & Brett, 1984). Reflecting this importance, a search of the Social Science Citation Index turned up more than 2,000 citations of the Baron and Kenny article that presented an important statistical approach to the investigation of these processes. Examples of hypotheses and models that involve intervening variables abound. In basic social psychology, intentions are thought to mediate the relation between attitude and behavior (Ajzen & Fish-bein, 1980). In cognitive psychology, attentional processes are thought to intervene between stimulus and behavior (Stacy, Leigh, & Weingardt, 1994). In industrial psychology, work environment leads to changes in the intervening variable of job perception, which in turn affects behavioral outcomes (James & Brett, 1984 NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript work on preventive health interventions, programs are designed to change proximal variables, which in turn are expected to have beneficial effects on the distal health outcomes of interest (Hansen, 1992; MacKinnon, 1994;West & Aiken, 1997).A search of psychological abstracts from 1996 to 1999 yi...

The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.

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Mediation analysis is a newer statistical tool that is becoming more prominent in nutritional research. Its use provides insight into the relationship among variables in a potential causal chain. For intervention studies, it can define the impact of different programmatic components, and in doing so allow investigators to identify and refine a program’s critical aspects. We present an overview of mediation analysis, compare mediators with other variables (confounders, moderators and covariates) and illustrate how mediation analysis permits interpretation of the change process. A framework is outlined for the critical appraisal of articles purporting to use mediation analysis. The framework’s utility is demonstrated by searching the nutrition literature and identifying articles citing mediation cross referenced with nutrition, diet, food and obesity. Seventy-two articles were identified that involved human subjects and behavioral outcomes, and almost half mentioned mediation without tests to define its presence. Tabulation of the 40 articles appropriately assessing mediation demonstrates an increase in these techniques’ appearance and the breadth of nutrition topics addressed. Mediation analysis is an important new statistical tool. Familiarity with its methodology and a framework for assessing articles will allow readers to critically appraise the literature and make informed independent evaluations of works using these techniques.