A General Model for Testing Mediation and Moderation Effects

Department of Psychology, University of South Carolina, Barnwell College, 1512 Pendleton St., Columbia, SC 29208, USA.
Prevention Science (Impact Factor: 2.63). 12/2008; 10(2):87-99. DOI: 10.1007/s11121-008-0109-6
Source: PubMed


This paper describes methods for testing mediation and moderation effects in a dataset, both together and separately. Investigations of this kind are especially valuable in prevention research to obtain information on the process by which a program achieves its effects and whether the program is effective for subgroups of individuals. A general model that simultaneously estimates mediation and moderation effects is presented, and the utility of combining the effects into a single model is described. Possible effects of interest in the model are explained, as are statistical methods to assess these effects. The methods are further illustrated in a hypothetical prevention program example.

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    • "In other to improve the efficiency of MMR technique, Anderson et al (1996) recommended the use of small sample size Cortina (1993) recommended the use of square terms as covariates. Fairchild and MacKinnon (2009) wrote on a general model that has the capability of estimating both mediation and moderation effects simultaneously. "
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    ABSTRACT: The paper introduces the concept of reverse moderation in order to investigate the uniqueness of the coefficients of independent variables and non-commutative nature of interactions in moderated multiple regression (MMR) in hierarchical order. The moderation effect is 0.01and the data used was masked to maintain the integrity of an ongoing research. The research concludes that moderation and its reverse yield different results indicating the uniqueness of the coefficients of the independent variables and the interactions are not commutative. Interactions are one-way. Each case is different as shown by the results of the 20 models used.
    Mediterranean Journal of Social Sciences 09/2015; 6(4):408-417. DOI:10.5901/mjss.2015.v6n4s3p408
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    • "All three situations described in the objectives are required for moderation to exist (Baron and Kenny, 1986). A significant interaction between integrative complexity and value-laden basic beliefs suggests that there is a moderating effect of integrative complexity on the value–attitude relationship (Baron and Kenny, 1986; Fairchild and MacKinnon, 2009; Vaske, 2008). That is, the strength of the relationship between an individual's values and their attitude toward prescribed fire, in the context of MPB infestation, depends on their integrative complexity regarding that issue. "
    Forest Policy and Economics 07/2015; DOI:10.1016/j.forpol.2015.07.003 · 1.86 Impact Factor
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    • "The product of coefficients method for testing statistical mediation was applied using MPlus Version 5.2 (Muthén & Muthén, Los Angeles, LA), with percentile bootstrapping implemented to adjust asymmetric confidence limits and address biased standard errors (Fairchild, Mackinnon, Taborga, & Taylor, 2009; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). This method provides a balance of power and Type I error and supports the use of mediation when there may not be strong predictoreoutcome associations , whereas the causal steps and difference in coefficients methods are less advisable for relatively smaller samples, and are more susceptible to Type II errors (Fairchild & MacKinnon, 2009; Fritz & MacKinnon, 2007; MacKinnon et al., 2002). The product of coefficients method involves regression of outcomes on the mediator and predictors, and regression of the mediator on the predictors , yielding two coefficients that link predictors to the mediator and the mediator to the outcome, with the product of these coefficients providing an estimate of the mediated/indirect effect (ab). "
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