![]() We examined articles in two psychological journals, the Journal of Consulting and Clinical Psychology and the Journal of Applied Psychology. Given the large number of methods to test for mediation, we conducted a literature survey to examine which methods were most often used by psychologists. If a large number of samples are taken from the original sample with replacement, the parameter of interest, in this case the indirect effect αβ, can be calculated for each new sample, forming a bootstrap distribution of that parameter, and confidence intervals can be formed to test for mediation. A variation on the product-of-coefficients tests uses resampling. ![]() Much as the difference-in-coefficients tests vary in the formulas used to calculate the standard error of the difference, the main difference among the various product-of-coefficients tests is the formula used to calculate the standard error of the product. Despite conceptual differences between the product-of-coefficients tests and the difference-in-coefficients tests, MacKinnon, Warsi, and Dwyer (1995) showed that τ ̂ − τ ̂′ is equal to α ̂ β ̂ for ordinary least squares regression, although this relationship does not hold for logistic regression models. This test statistic is then compared against a normal distribution to test for significance. ![]() In the product-of-coefficients tests, the product of the coefficient from the independent variable to the mediator, α ̂, and the coefficient from the mediator to the dependent variable adjusted for the independent variable, β ̂, is divided by the standard error of the product to create a test statistic. The main difference between the various difference-in-coefficients tests is that they use different formulas for calculating the standard error of the difference. This value is then compared against a t distribution to test for significance. A model in which Step 4 is relaxed so that the requirement is only | τ ̂′| < | τ ̂|, rather than that τ ̂′ be nonsignificant, is called a partially mediated model ( Baron & Kenny, 1986).ĭifference-in-coefficients tests are conducted by taking the difference between the overall effect of X on Y and the direct effect of X on Y adjusted for M, τ ̂ − τ ̂′, and dividing by the standard error of the difference. ![]() Models in which all four steps are satisfied are called fully mediated models. The direct effect of X on Y adjusted for M ( τ ̂′) must be non-significant. The purpose of this article is to offer guidelines for researchers in determining the sample size necessary to conduct mediational studies with. However, for researchers planning studies, it would be more useful to know the sample size required for. (2002) investigated power empirically for common sample sizes for many of these tests. A more recent search of the Social Science Citation Index that we conducted found almost 8,000 citations, though a number of these publications examined moderation rather than mediation.Īlthough there are a number of methods to test for mediation, including structural equation modeling (SEM Cole & Maxwell, 2003 Holmbeck, 1997 Kenny, Kashy, & Bolger, 1998) and bootstrapping ( MacKinnon, Lockwood, & Williams, 2004 Shrout & Bolger, 2002), many researchers prefer to use regression-based tests. Using the Social Science Citation Index, MacKinnon, Lockwood, Hoffman, West, and Sheets (2002) found more than 2,000 citations of Baron and Kenny’s article. ![]() Since the publication of Baron and Kenny’s (1986) article describing a method to evaluate mediation, the use of mediation models in the social sciences has increased dramatically. ![]()
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