Father Parenting Competence

The data, from an exercise given by Meyers et al. (2006) relates to 60 fathers assessed on three subscales of a Perceived Parenting Competence Scale. The fathers were selected from three groups: (a) fathers of a child with no disabilities; (b) fathers with a physically disabled child; (c) fathers with a mentally disabled child.

Format

A data frame with 60 observations on the following 4 variables.

group

a factor with levels Normal Physical Disability Mental Disability

caring

caretaking responsibilities, a numeric vector

emotion

emotional support provided to the child, a numeric vector

play

recreational time spent with the child, a numeric vector

Source

Meyers, L. S., Gamst, G, & Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation, Thousand Oaks, CA: Sage Publications, https://study.sagepub.com/meyers3e, Exercises 10B.

Details

The scores on the response variables are discrete.

Examples

data(Parenting)
require(car)

# fit the MLM
parenting.mod <- lm(cbind(caring, emotion, play) ~ group, data=Parenting)
car::Anova(parenting.mod)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>       Df test stat approx F num Df den Df    Pr(>F)    
#> group  2   0.94836   16.833      6    112 8.994e-14 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Box's M test
boxM(parenting.mod)
#> 
#> 	Box's M-test for Homogeneity of Covariance Matrices
#> 
#> data:  Y
#> Chi-Sq (approx.) = 28.343, df = 12, p-value = 0.004927
#> 
plot(boxM(parenting.mod))


parenting.mod <- lm(cbind(caring, emotion, play) ~ group, data=Parenting)
car::Anova(parenting.mod)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>       Df test stat approx F num Df den Df    Pr(>F)    
#> group  2   0.94836   16.833      6    112 8.994e-14 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# test contrasts
print(linearHypothesis(parenting.mod, "group1"), SSP=FALSE)
#> 
#> Multivariate Tests: 
#>                  Df test stat approx F num Df den Df     Pr(>F)    
#> Pillai            1 0.5210364 19.94376      3     55 7.1051e-09 ***
#> Wilks             1 0.4789636 19.94376      3     55 7.1051e-09 ***
#> Hotelling-Lawley  1 1.0878413 19.94376      3     55 7.1051e-09 ***
#> Roy               1 1.0878413 19.94376      3     55 7.1051e-09 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(linearHypothesis(parenting.mod, "group2"), SSP=FALSE)
#> 
#> Multivariate Tests: 
#>                  Df test stat approx F num Df den Df     Pr(>F)    
#> Pillai            1 0.4293815 13.79555      3     55 8.0113e-07 ***
#> Wilks             1 0.5706185 13.79555      3     55 8.0113e-07 ***
#> Hotelling-Lawley  1 0.7524844 13.79555      3     55 8.0113e-07 ***
#> Roy               1 0.7524844 13.79555      3     55 8.0113e-07 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


heplot(parenting.mod)

# display tests of contrasts
hyp <- list("N:MP" = "group1", "M:P" = "group2")
heplot(parenting.mod, hypotheses=hyp)


# make a prettier plot
heplot(parenting.mod, hypotheses=hyp, asp=1,
       fill=TRUE, fill.alpha=c(0.3,0.1), 
       col=c("red", "blue"), 
       lty=c(0,0,1,1), label.pos=c(1,1,3,2),
       cex=1.4, cex.lab=1.4, lwd=3)


pairs(parenting.mod, fill=TRUE, fill.alpha=c(0.3, 0.1))


if (FALSE) { # \dontrun{
heplot3d(parenting.mod, wire=FALSE)
} # }