Sample Dataset: Serial Moderated Mediation with Two Categorical Moderators
Source:R/dat_4.R
data_med_mod_serial_cat.RdA serial 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"
- m1
Mediator 1. Numeric.
- m2
Mediator 2. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
data(data_med_mod_serial_cat)
dat <- data_med_mod_serial_cat
summary(lm_m1 <- lm(m1 ~ x*w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = m1 ~ x * w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.48162 -0.63917 0.03616 0.62807 2.88106
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.13552 0.66475 -0.204 0.838596
#> x 0.97270 0.10335 9.411 < 2e-16 ***
#> w1group2 -0.23379 0.90635 -0.258 0.796628
#> w1group3 2.53055 0.90458 2.797 0.005493 **
#> c1 0.06033 0.06185 0.975 0.330139
#> c2 0.04888 0.05965 0.819 0.413195
#> x:w1group2 -0.03211 0.14433 -0.222 0.824096
#> x:w1group3 -0.51225 0.14585 -3.512 0.000515 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.047 on 292 degrees of freedom
#> Multiple R-squared: 0.4478, Adjusted R-squared: 0.4346
#> F-statistic: 33.83 on 7 and 292 DF, p-value: < 2.2e-16
#>
summary(lm_m2 <- lm(m2 ~ m1 + x + w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = m2 ~ m1 + x + w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.03503 -0.65833 0.02794 0.64110 2.99045
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.314339 0.443355 7.476 8.95e-13 ***
#> m1 0.363210 0.055052 6.598 1.95e-10 ***
#> x 0.005616 0.072660 0.077 0.93844
#> w1group2 -0.415468 0.148938 -2.790 0.00562 **
#> w1group3 -0.075908 0.145738 -0.521 0.60286
#> c1 -0.034240 0.059456 -0.576 0.56513
#> c2 0.010818 0.057533 0.188 0.85098
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.01 on 293 degrees of freedom
#> Multiple R-squared: 0.2334, Adjusted R-squared: 0.2177
#> F-statistic: 14.87 on 6 and 293 DF, p-value: 7.661e-15
#>
summary(lm_y <- lm(y ~ m2*w2 + m1 + x + w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = y ~ m2 * w2 + m1 + x + w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.30328 -0.69259 0.03058 0.67952 2.89379
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.81161 0.56116 3.228 0.00139 **
#> m2 0.39472 0.07703 5.124 5.46e-07 ***
#> w2team2 0.48363 0.56320 0.859 0.39120
#> m1 0.11568 0.05984 1.933 0.05418 .
#> x -0.06053 0.07369 -0.821 0.41210
#> w1group2 -0.02577 0.15383 -0.168 0.86706
#> w1group3 -0.14960 0.14819 -1.010 0.31358
#> c1 -0.08765 0.06035 -1.452 0.14750
#> c2 0.08223 0.05853 1.405 0.16112
#> m2:w2team2 -0.07549 0.10503 -0.719 0.47288
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.024 on 290 degrees of freedom
#> Multiple R-squared: 0.2074, Adjusted R-squared: 0.1828
#> F-statistic: 8.43 on 9 and 290 DF, p-value: 3.442e-11
#>