Sample Dataset: Parallel Moderated Mediation with Two Categorical Moderators
Source:R/dat_4.R
data_med_mod_parallel_cat.RdA 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"
- 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_parallel_cat)
dat <- data_med_mod_parallel_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
#> -2.7472 -0.6656 0.0500 0.6887 3.4973
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.143612 0.692888 1.651 0.0999 .
#> x 0.902275 0.087667 10.292 <2e-16 ***
#> w1group2 1.150056 0.800215 1.437 0.1517
#> w1group3 0.708515 0.846560 0.837 0.4033
#> c1 -0.002886 0.062350 -0.046 0.9631
#> c2 -0.127222 0.059342 -2.144 0.0329 *
#> x:w1group2 -0.233639 0.117285 -1.992 0.0473 *
#> x:w1group3 -0.238876 0.123572 -1.933 0.0542 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.025 on 292 degrees of freedom
#> Multiple R-squared: 0.49, Adjusted R-squared: 0.4777
#> F-statistic: 40.07 on 7 and 292 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
#> -2.7490 -0.5931 -0.0192 0.6492 3.2015
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.224453 0.648430 4.973 1.13e-06 ***
#> x 0.438166 0.082042 5.341 1.86e-07 ***
#> w1group2 0.190078 0.748870 0.254 0.7998
#> w1group3 -0.150416 0.792242 -0.190 0.8496
#> c1 -0.129800 0.058349 -2.225 0.0269 *
#> c2 0.007616 0.055535 0.137 0.8910
#> x:w1group2 -0.053156 0.109759 -0.484 0.6285
#> x:w1group3 0.025153 0.115643 0.218 0.8280
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.9597 on 292 degrees of freedom
#> Multiple R-squared: 0.2529, Adjusted R-squared: 0.235
#> F-statistic: 14.12 on 7 and 292 DF, p-value: 8.862e-16
#>
summary(lm_y <- lm(y ~ m1*w2 + m2*w2 + m1 + x + w1 + c1 + c2, dat))
#>
#> Call:
#> lm(formula = y ~ m1 * w2 + m2 * w2 + m1 + x + w1 + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.9753 -0.9732 -0.0471 0.9780 4.0917
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.335755 0.823638 -0.408 0.683833
#> m1 0.897601 0.094224 9.526 < 2e-16 ***
#> w2team2 0.683583 0.919420 0.743 0.457787
#> m2 0.426387 0.115277 3.699 0.000259 ***
#> x 0.044777 0.088262 0.507 0.612315
#> w1group2 -0.194036 0.189154 -1.026 0.305839
#> w1group3 -0.031134 0.204115 -0.153 0.878874
#> c1 -0.125317 0.083119 -1.508 0.132728
#> c2 0.083940 0.078548 1.069 0.286119
#> m1:w2team2 0.006921 0.120168 0.058 0.954113
#> w2team2:m2 -0.080865 0.159015 -0.509 0.611463
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.338 on 289 degrees of freedom
#> Multiple R-squared: 0.5841, Adjusted R-squared: 0.5697
#> F-statistic: 40.59 on 10 and 289 DF, p-value: < 2.2e-16
#>