Download Reporting the Results from a Simple Moderation Analysis

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
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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.