Sample Dataset: Serial-Parallel Moderated Mediation with Two Categorical Moderators
Source:R/dat_4.R
data_med_mod_serial_parallel_cat.RdA serial-parallel mediation model with two categorical moderators.
Format
A data frame with 300 rows and 8 variables:
- x
Predictor. Numeric.
- w1
Moderator. String. Values: "group1", "group2", "group3"
- w2
Moderator. String. Values: "team1", "team2"
- m11
Mediator 1 in Path 1. Numeric.
- m12
Mediator 2 in Path 1. Numeric.
- m2
Mediator in Path 2. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
data(data_med_mod_serial_parallel_cat)
dat <- data_med_mod_serial_parallel_cat
summary(lm_m11 <- lm(m11 ~ x*w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = m11 ~ x * w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.0619 -0.6060 -0.0216 0.6404 3.4200
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.3209962 0.6435180 0.499 0.61829
#> x 0.9510556 0.0940888 10.108 < 2e-16 ***
#> w1group2 0.9280457 0.8350532 1.111 0.26733
#> w1group3 1.9216220 0.8739049 2.199 0.02867 *
#> c1 -0.0009806 0.0545802 -0.018 0.98568
#> c2 -0.0064057 0.0580329 -0.110 0.91218
#> x:w1group2 -0.1964450 0.1338652 -1.467 0.14332
#> x:w1group3 -0.4204409 0.1276517 -3.294 0.00111 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.955 on 292 degrees of freedom
#> Multiple R-squared: 0.4167, Adjusted R-squared: 0.4027
#> F-statistic: 29.8 on 7 and 292 DF, p-value: < 2.2e-16
#>
summary(lm_m12 <- lm(m12 ~ m11 + x + w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = m12 ~ m11 + x + w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.83583 -0.56576 0.02488 0.59982 2.07412
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.239944 0.480360 4.663 4.74e-06 ***
#> m11 0.042372 0.058007 0.730 0.466
#> x 0.430485 0.068153 6.316 9.91e-10 ***
#> w1group2 0.112602 0.140247 0.803 0.423
#> w1group3 0.051596 0.166553 0.310 0.757
#> c1 0.054272 0.054916 0.988 0.324
#> c2 -0.006007 0.058548 -0.103 0.918
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.9642 on 293 degrees of freedom
#> Multiple R-squared: 0.2655, Adjusted R-squared: 0.2505
#> F-statistic: 17.66 on 6 and 293 DF, p-value: < 2.2e-16
#>
summary(lm_m2 <- lm(m2 ~ x + w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = m2 ~ x + w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.50745 -0.72556 0.01446 0.74006 2.64019
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.87424 0.50746 5.664 3.52e-08 ***
#> x 0.44820 0.05732 7.820 9.54e-14 ***
#> w1group2 -0.12412 0.15005 -0.827 0.409
#> w1group3 -0.05783 0.16970 -0.341 0.733
#> c1 -0.02093 0.05907 -0.354 0.723
#> c2 -0.01997 0.06298 -0.317 0.751
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.037 on 294 degrees of freedom
#> Multiple R-squared: 0.2303, Adjusted R-squared: 0.2172
#> F-statistic: 17.59 on 5 and 294 DF, p-value: 3.033e-15
#>
summary(lm_y <- lm(y ~ m12 + m2*w2 + m12 + x + c1 + c2, dat))
#>
#> Call:
#> lm(formula = y ~ m12 + m2 * w2 + m12 + x + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.0979 -0.7856 -0.0818 0.7568 3.6255
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.462047 0.754233 0.613 0.5406
#> m12 0.815192 0.074408 10.956 < 2e-16 ***
#> m2 0.448334 0.099012 4.528 8.68e-06 ***
#> w2team2 1.308023 0.704869 1.856 0.0645 .
#> x 0.002084 0.073283 0.028 0.9773
#> c1 -0.020047 0.069981 -0.286 0.7747
#> c2 -0.022310 0.075188 -0.297 0.7669
#> m2:w2team2 -0.228928 0.122836 -1.864 0.0634 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.226 on 292 degrees of freedom
#> Multiple R-squared: 0.4392, Adjusted R-squared: 0.4258
#> F-statistic: 32.67 on 7 and 292 DF, p-value: < 2.2e-16
#>