Using SPSS software, all the responses of a thesis survey has been extracted and statistical values has been calculated. A multiple regression analysis and median analysis including mean, standard dev

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Reporting Mediation and Moderation

Dr . Jeffr ey Kahn, Illinois State University

Updated Mar ch 24, 2014

Complex regression procedures like mediation and moderation are best explained with a combination of

plain language and a figure. For mediation, a path diagram that illustrates the mediational relationship and

indicates beta weights is most useful. The statistical significance of the indirect ef fect should be tested using

bootstrapping (see Hayes [2013], Introduction to mediation, moderation, and conditional pr ocess analysis ).

For moderation, a figure showing conditional/simple slopes at different levels of the moderator (typically 1

SD above, 1 SD below , and the M of the moderator variable for continious moderators) is most useful.

A brief, simulated example of how to r eport simple mediation:

The relationship between math ability and interest in becoming a math major was mediated by

math self-efficacy. As Figure 1 illustrates, the standardized regression coef ficient between math

ability and math self-efficacy was statistically significant, as was the standardized regression

coefficient between math self-ef ficacy and interest in the math major. The standardized indirect

effect was (.47)(.36) = .17. W e tested the significance of this indirect effect using bootstrapping

procedures. Unstandardized indirect ef fects were computed for each of 10,000 bootstrapped

samples, and the 95% confidence interval was computed by determining the indirect ef fects at

the 2.5th and 97.5th percentiles. The bootstrapped unstandardized indirect ef fect was .84, and

the 95% confidence interval ranged from .21, 1.28. Thus, the indirect ef fect was statistically

significant.

A brief, simulated example of how to report moderation:

Negative affect was examined as a moderator of the relation between social support and job

burnout. Social support and negative af fect were entered in the first step of the regression

analysis. In the second step of the regression analysis, the interaction term between negative

affect and social support was entered, and it explained a significant increase in variance in job

burnout, Δ R2 = .03, F(1, 335) = 14.61, p < .001. Thus, n egative affect was a significant

moderator of the relationship between social support and job burnout . The unstandardized

simple slope for employees 1 SD below the mean of negative af fect was .56, the unstandardized

simple slope for employees with a mean level of negative affect was -.08, and the

unstandardized simple slope for employees 1 SD above the mean of negative af fect was -.72 (see

Figure 2). 4/9/2020 Reporting Mediation and Moderation

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