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Reporting the Results from a Simple Moderation Analysis Table 1 below is an example of a table for reporting the zero-order correlations. If you have missing data on any of the variables in the table, and you took the pairwise option when computing the correlations, the sample sizes will differ across the cells in the table, which is undesirable. To avoid this, you should, when computing the correlations, include only those variables that will be included in the moderation analysis (Y, X, Moderators, and any covariate) and use the NMISS option with Proc Corr. If using SPSS, select “Exclude cases listwise.” Your multiple regression will include an interaction term. If it is not significant, report that in text, something like this: The interaction between messages and resources fell short of statistical significance, F(1, 203) = 0.73, p = .39, R2 = .002. Then you should drop the interaction term from the model, rerun the multiple regression, and report the results of that reduced model. If your interaction term has only one degree of freedom (which is typical), then R2 is sr2. Table 2 below illustrates an efficient way to report the results of such a multiple regression. If desired, additional columns can be added to report the unstandardized partial slope, sr2, and so on. If any of your predictor variables, including covariates, are categorical and significant, then you should provide for them the adjusted means (LSMEANS). For example, if Sex (female = 0, male = 1) were significant, report the lsmeans on Y for each sex. In SAS these are obtained by using the LSMEANS command with PROC GLM Do remember to identify any categorical variables in the CLASS statement. In SPSS, use GLM, Univariate. Identify Y as the “Dependent Variable,” any categorical predictors as “Fixed Factor(s),” and any continuous predictors as “Covariates.” Under Options, ask for the means the categorical variable(s). If you have two or more categorical variables, SPSS will, by default, include in the model interactions between/among those categorical. If you wish to have a main effects only model (common when the categorical variables are control variables). Click “Model,” “Custom,” and build the model you desire. If your interaction term is significant, you should probe the interaction. Process Hayes makes this easy to do. See Table 3 below, and the text that follows the table, as an example of how to present the results of probing an interaction between two continuous predictors. Table 1 Descriptive Statistics and Intercorrelations Variable IAM SWLS WAQ Work SA AEI AL Age Incom e Tenure Bracke t Hours Worke d SWLS (.89) WAQ -.33** (.92) IAM Work .43** -.44** (.85) SA -.42** .34** -.80** (.92) AEI -.19** .33** -.74** .26** (.80) AL .42** -.34** .70** -.54** -.26** (.78) Age .10 -.07 .17** -.10 -.16* .13* _ Tenure .11* -.03 .17** -.05 -.19** .13* .57** _ Income Bracket Hours Worked .17* .23** -.02 -.02 .02 -.05 .34** .18** -.03 .45** -.07 .01 .05 -.12* .05 .06 .47** _ 70.9 1 17.6 1 64.1 0 10.2 1 8.61 15.5 8 24.2 8 11.57 3.95 47.41 5.15 5.38 3.03 47.4 4 11.9 4 9.45 1.57 10.06 M 25.56 SD 6.38 _ Note. N = 358. Entries on the main diagonal are Cronbach’s alpha. LSS = Life Satisfaction Scale; WAQ = Workaholism Analysis Questionnaire; IAM Work = Individual Authenticity Measure at Work; SA = Self-Alienation subscale; AEI = Accepting External Influence subscale; AL = Authentic Living subscale *p < .05, **p < .001. Table 2 Predicting Subjective Well Being Predictor r SJAS-Hard Driving Competitive -.035 .131* Rosenberg Self Esteem .561* .596* Contingent Self Esteem .092* -.161* Perceived Social Support .172* .426* Network Diversity -.089 .134* Number of Persons in Network .107* .221* *p .05 Table 3 Support for Animal Rights Predicted from Idealism and Misanthropy Predictor p Idealism .067 .39 -.086, .221 Misanthropy* .303 < .001 .149, .456 -.146 .048 Idealism x Misanthropy* 95% CI -.290, -.001 *p .05 As shown in Table 3, misanthropy was significantly related to support for animal rights and idealism significantly moderated that relationship. This interaction is illustrated in Figure 1. The interaction was probed by testing the conditional effects of misanthropy at three levels of idealism, one standard deviation below the mean, at the mean, and one standard deviation above the mean. As shown in Table 4, misanthropy was significantly related to support for animal rights when idealism was one standard deviation below the mean and when at the mean (p < .001), but not when idealism was one standard deviation above the mean (p = .14). The Johnson-Neyman technique showed that the relationship between misanthropy and support for animal rights was significant when idealism was less than .78 standard deviations above the mean but not significant with higher values of idealism. Table 4 Conditional Effects of Misanthropy on Support for Animal Rights Idealism p One SD below mean .448 < .001 At the mean .303 < .001 One SD above mean .157 .141 95% CI .236, .661 .149, .456 -.053, .367 *p .05 Karl Wuensch's Statistics Lessons Page Continuous Moderator Variables. Complete Example (SAS syntax, output, and presentation of results) Karl L. Wuensch, Krampusnacht, 2016. PS – I am keeping this document in Word format so that others can copy and paste tables from it to use as templates for their own tables.