Demographics
# Age
data %>%
distinct(Participant.ID, Age)%>%
summarise_at('Age',
list(~mean(., na.rm=T),
~sd(., na.rm=T),
~median(., na.rm=T),
~min(., na.rm=T),
~max(., na.rm=T),
~sum(!is.na(.))))%>%
dplyr::rename("n" = "sum")%>%
dplyr::select(n, mean, sd, median, min, max)%>%
mutate(total.n = sum(n))%>%
knitr::kable()
| 32 |
11.71875 |
1.727097 |
12 |
9 |
14 |
32 |
# Gender
data %>%
distinct(Participant.ID, Gender)%>%
group_by(Gender)%>%
summarise(n = n())%>%
mutate(total.Gender.n = sum(n))%>%
knitr::kable()
# Race
data %>%
distinct(Participant.ID, child.race)%>%
group_by(child.race)%>%
summarise(n = n())%>%
mutate(total.child.race.n = sum(n))%>%
knitr::kable()
|
2 |
32 |
| Black or African American |
4 |
32 |
| Multiple |
6 |
32 |
| n/a |
1 |
32 |
| Other |
2 |
32 |
| White |
17 |
32 |
# Ethnicity
data %>%
distinct(Participant.ID, child.ethnicity)%>%
group_by(child.ethnicity)%>%
summarise(n = n())%>%
mutate(total.child.ethnicity.n = sum(n))%>%
knitr::kable()
|
1 |
32 |
| Hispanic or Latino |
8 |
32 |
| Not Hispanic or Latino |
23 |
32 |
Descriptives
# CELF-4 standard scores
data %>%
distinct(Participant.ID, CELF.std)%>%
summarise_at('CELF.std',
list(~mean(., na.rm=T),
~sd(., na.rm=T),
~median(., na.rm=T),
~min(., na.rm=T),
~max(., na.rm=T),
~sum(!is.na(.))))%>%
dplyr::rename("n" = "sum")%>%
dplyr::select(n, mean, sd, median, min, max)%>%
mutate(total.n = sum(n))%>%
knitr::kable()
| 32 |
109.9375 |
12.99116 |
113 |
79 |
130 |
32 |
# CELF-4 WC standard scores
data %>%
distinct(Participant.ID, CELF.WC)%>%
summarise_at('CELF.WC',
list(~mean(., na.rm=T),
~sd(., na.rm=T),
~median(., na.rm=T),
~min(., na.rm=T),
~max(., na.rm=T),
~sum(!is.na(.))))%>%
dplyr::rename("n" = "sum")%>%
dplyr::select(n, mean, sd, median, min, max)%>%
mutate(total.n = sum(n))%>%
knitr::kable()
| 32 |
12.5625 |
3.311149 |
13 |
5 |
18 |
32 |
# WISC standard scores
data %>%
distinct(Participant.ID, WISC.nonverbal.index)%>%
summarise_at('WISC.nonverbal.index',
list(~mean(., na.rm=T),
~sd(., na.rm=T),
~median(., na.rm=T),
~min(., na.rm=T),
~max(., na.rm=T),
~sum(!is.na(.))))%>%
dplyr::rename("n" = "sum")%>%
dplyr::select(n, mean, sd, median, min, max)%>%
mutate(total.n = sum(n))%>%
knitr::kable()
| 32 |
108 |
14.19223 |
105.5 |
86 |
139 |
32 |
# Income-to-Needs Ratio
data %>%
distinct(Participant.ID, Needs.Income.ratio)%>%
summarise_at('Needs.Income.ratio',
list(~mean(., na.rm=T),
~sd(., na.rm=T),
~median(., na.rm=T),
~min(., na.rm=T),
~max(., na.rm=T),
~sum(!is.na(.))))%>%
dplyr::rename("n" = "sum")%>%
dplyr::select(n, mean, sd, median, min, max)%>%
mutate(total.n = sum(n))%>%
knitr::kable()
| 32 |
3.691064 |
1.766435 |
3.630218 |
0.1758087 |
7.510288 |
32 |
# Maternal Education
data %>%
distinct(Participant.ID, maternal.ed)%>%
group_by(maternal.ed)%>%
summarise(n = n())%>%
mutate(total.maternal.ed.n = sum(n))%>%
knitr::kable()
| college graduate |
10 |
32 |
| graduate degree |
7 |
32 |
| high school graduate |
6 |
32 |
| partial college/associate’s degree |
9 |
32 |
Visualizations
agg_p <- data %>%
group_by(maternal.ed, Needs.Income.ratio) %>%
#summarise(mean = mean(CELF.WC)) %>%
ungroup()
plotlabels <- c("High School\nGraduate","Partial College/\nAssociate's Degree","College Degree","Graduate Degree")
ggplot(agg_p, aes(x=factor(maternal.ed, level=c('high school graduate',"partial college/associate's degree",'college graduate','graduate degree')), y=CELF.WC))+#, color = maternal.ed))+#, color =free.or.reduced.lunch)) +
geom_point(color='#BA0412') +
geom_smooth(method="lm",se=FALSE, fullrange=FALSE) +
labs(x="Maternal Education Level", y = "CELF-4 Word Classes Total Standard Score")+
scale_x_discrete(labels= plotlabels)+
theme(axis.text.x=element_text(color = "black", size=11, angle=0, vjust=.5, hjust=0.5),axis.title.x = element_text(face= "bold",margin = margin(t = 10, r = 0, b = 0, l = 0)),axis.title.y=element_text(face= "bold"))
## `geom_smooth()` using formula = 'y ~ x'
ggplot(agg_p, aes(x=Needs.Income.ratio, y=CELF.WC))+#, color = maternal.ed))+#, color =free.or.reduced.lunch)) +
geom_point() +
geom_smooth(method="lm",se=FALSE, fullrange=FALSE, color = '#12567D') +
labs(x="Income-to-Needs Ratio", y = "CELF-4 Word Classes Total Standard Score")+ theme(axis.title.x = element_text(face= "bold"),axis.title.y=element_text(face= "bold"))
## `geom_smooth()` using formula = 'y ~ x'
agg_p$maternal.ed <- factor(agg_p$maternal.ed, levels = c('high school graduate',"partial college/associate's degree",'college graduate','graduate degree'))
ggplot(agg_p, aes(x=Needs.Income.ratio, y=CELF.WC, color = maternal.ed))+#, color =free.or.reduced.lunch)) +
geom_point() +
geom_smooth(method="lm",se=FALSE, fullrange=FALSE) +
labs(x="Income-to-Needs Ratio", y = "CELF-4 Word Classes Total Standard Score", col="Maternal Education Level")+ scale_fill_discrete(limits=c('high school graduate',"partial college/associate's degree",'college graduate','graduate degree')) + theme(axis.title.x = element_text(face= "bold"),axis.title.y=element_text(face= "bold"),legend.position="none") + scale_color_hue(labels = c("High School Graduate","Partial College/Associate's Degree","College Degree","Graduate Degree"))
## `geom_smooth()` using formula = 'y ~ x'
allvarsplot <- ggplot(agg_p, aes(x=Needs.Income.ratio, y=CELF.WC, color = maternal.ed))+#, color =free.or.reduced.lunch)) +
geom_point() +
geom_smooth(method="lm",se=FALSE, fullrange=FALSE) +
labs(x="Income-to-Needs Ratio", y = "CELF-4 Word Classes Total Standard Score", col="Maternal Education Level")+ scale_fill_discrete(limits=c('high school graduate',"partial college/associate's degree",'college graduate','graduate degree')) + theme(axis.title.x = element_text(face= "bold"),axis.title.y=element_text(face= "bold"),legend.text =element_text( color = "black")) + scale_color_hue(labels = c("High School Graduate","Partial College/Associate's Degree","College Degree","Graduate Degree"))
legend <- cowplot::get_legend(allvarsplot)
## `geom_smooth()` using formula = 'y ~ x'
grid.newpage()
grid.draw(legend)
Analyses
data$maternal.ed <- relevel(data$maternal.ed, ref = "high school graduate")
#HcSum <- rbind(
#cH1=c("out of category"=0.333333, "target meaning"=0, "within category"=-0.333333),
#cH2=c("out of category"=-0.333333, "target meaning"=0.666667, "within category"=-0.333333)
#)
#contrasts(data$language.status)<-contr.helmert(3)/3
#contrasts(proportion_data$response)
#colnames(contrasts(proportion_data$response)) <- c("_target meaning", "_within category")
data.max <- lm (CELF.WC ~ gender.c + age.c + wisc.c + free.red.lunch.c + maternal.ed*nir, data=data) #converges and all that jazz
summary (data.max)
##
## Call:
## lm(formula = CELF.WC ~ gender.c + age.c + wisc.c + free.red.lunch.c +
## maternal.ed * nir, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8521 -1.6084 0.4474 1.0936 2.7256
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 12.01327 1.00792 11.919
## gender.c -0.67656 0.91191 -0.742
## age.c -0.16533 0.31867 -0.519
## wisc.c -0.01031 0.06694 -0.154
## free.red.lunch.c -2.02760 1.16668 -1.738
## maternal.edcollege graduate -1.27383 1.26464 -1.007
## maternal.edgraduate degree 3.52521 2.80676 1.256
## maternal.edpartial college/associate's degree -0.10823 1.37327 -0.079
## nir 2.75604 0.96211 2.865
## maternal.edcollege graduate:nir -1.59371 0.97477 -1.635
## maternal.edgraduate degree:nir -3.14495 1.40453 -2.239
## maternal.edpartial college/associate's degree:nir -3.66958 1.27375 -2.881
## Pr(>|t|)
## (Intercept) 1.53e-10 ***
## gender.c 0.46676
## age.c 0.60958
## wisc.c 0.87909
## free.red.lunch.c 0.09760 .
## maternal.edcollege graduate 0.32584
## maternal.edgraduate degree 0.22360
## maternal.edpartial college/associate's degree 0.93797
## nir 0.00958 **
## maternal.edcollege graduate:nir 0.11770
## maternal.edgraduate degree:nir 0.03667 *
## maternal.edpartial college/associate's degree:nir 0.00924 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.149 on 20 degrees of freedom
## Multiple R-squared: 0.7283, Adjusted R-squared: 0.5789
## F-statistic: 4.874 on 11 and 20 DF, p-value: 0.001085
# Notes:
# For gender, female = -0.5 (after centering), male = 0.5 (after centering)
# For maternal education, college graduate = 1, graduate degree = 2, high school graduate = 3, partial college/associate's degree = 4
Exploring the interactions
emm_options(lm.df = "kenward-roger")
#emmeans(data.max, pairwise~ maternal.ed, adust = "bonferroni")
#emmeans(data.max, pairwise~ nir, adust = "bonferroni")
emmeans(data.max, pairwise~ maternal.ed | nir , adust = "bonferroni")
## $emmeans
## nir = -1.8e-16:
## maternal.ed emmean SE df lower.CL upper.CL
## high school graduate 12.0 1.008 20 9.91 14.1
## college graduate 10.7 0.914 20 8.83 12.6
## graduate degree 15.5 2.446 20 10.44 20.6
## partial college/associate's degree 11.9 1.105 20 9.60 14.2
##
## Results are averaged over the levels of: gender.c, free.red.lunch.c
## Confidence level used: 0.95
##
## $contrasts
## nir = -1.8e-16:
## contrast estimate SE df
## high school graduate - college graduate 1.274 1.26 20
## high school graduate - graduate degree -3.525 2.81 20
## high school graduate - (partial college/associate's degree) 0.108 1.37 20
## college graduate - graduate degree -4.799 2.80 20
## college graduate - (partial college/associate's degree) -1.166 1.20 20
## graduate degree - (partial college/associate's degree) 3.633 3.07 20
## t.ratio p.value
## 1.007 0.7470
## -1.256 0.6001
## 0.079 0.9998
## -1.717 0.3415
## -0.970 0.7676
## 1.184 0.6433
##
## Results are averaged over the levels of: gender.c, free.red.lunch.c
## P value adjustment: tukey method for comparing a family of 4 estimates
#emmeans(data.max, pairwise~ nir | maternal.ed, adust = "bonferroni")
High-level summary of these models:
There are no within-subject or within-item measures (only a single
summary data point from each participant for each measure), so a LMER is
not appropriate; a simple linear regression is. When controlling for the
other variables and high school graduate as the reference level for
maternal education, the effect of the income-to-needs ratio on on the
CELF-4 Word Classes total standard score is significantly different from
a maternal education of partial college/associate’s degree and graduate
degree, but not college graduate. Income-to-needs ratio strongly
predicts children’s semantic abilities for children with mothers with a
high school degree. Specifically, for every 1 unit increase in the
income-to-needs ratio, childrens’s CELF-4 Word Classes total standard
score increases by 2.76 units. The semantic abilities of children with
mothers with a college degree follow the same trajectory of higher
income-to-needs ratio predicting higher CELF-4 Word Classes total
standard scores as children with mothers with a high school degree.
However, there is a different relationship between income-to-needs ratio
and children’s semantic abilities for children with mothers with a
partial college/associate’s degree or a graduate degree. For children
with mothers with these education levels, a higher income-to-needs ratio
is predictive of a lower CELF-4 Word Classes total standard
score.
Not discussed in the paragraph above, but something we noted when
looking at the graph together is the variability/spread of the data for
each education level. Consider commenting on that too.