#R-Code based on data collected and analysed for MA Thesis by Nina Selina Mueller, January 2020, University of Fribourg (CH)
#abbreviated code for the article
#Code organized by:
###Import and organize data
###Initial differences
###Improvement by treatment and Yes/No test
###Linear mixed effect regression model
library("tidyverse")
## -- Attaching packages ----------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.1
## v tidyr 1.1.1 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts -------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library("ggplot2")
library("lme4")
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
library("lmerTest")
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
###################### IMPORT AND ORGANIZE DATA ########################
data <- read.csv("dataMueller2020.csv") #complete data
summary(data)
## ID Class Group wnw_istd
## Length:260 Length:260 Length:260 Min. :0.0000
## Class :character Class :character Class :character 1st Qu.:0.2575
## Mode :character Mode :character Mode :character Median :0.4300
## Mean :0.4158
## 3rd Qu.:0.5700
## Max. :0.9300
##
## tasks_n evaluation T1cogtsz T1cogpf T1cogdth
## Min. :0.000 Min. :0.250 Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.750 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000
## Median :3.000 Median :2.250 Median :2.000 Median :3.00 Median :4.000
## Mean :3.125 Mean :2.133 Mean :2.446 Mean :2.55 Mean :3.454
## 3rd Qu.:4.000 3rd Qu.:2.500 3rd Qu.:3.000 3rd Qu.:3.00 3rd Qu.:5.000
## Max. :7.000 Max. :3.000 Max. :6.000 Max. :4.00 Max. :7.000
## NA's :140 NA's :140
## T1cogck T1cogkch T1noncog T1total
## Min. :0.000 Min. :0.000 Min. :0.000 Min. : 3.00
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:11.00
## Median :3.000 Median :1.000 Median :2.000 Median :15.50
## Mean :3.035 Mean :1.196 Mean :2.523 Mean :15.20
## 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:19.25
## Max. :5.000 Max. :2.000 Max. :6.000 Max. :30.00
##
## T1cog T2cogtsz T2cogpf T2cogdth T2cogck
## Min. : 2.00 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.: 9.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.00
## Median :13.00 Median :4.000 Median :3.000 Median :3.000 Median :4.00
## Mean :12.68 Mean :3.469 Mean :2.681 Mean :3.454 Mean :3.45
## 3rd Qu.:16.00 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :24.00 Max. :6.000 Max. :4.000 Max. :7.000 Max. :5.00
##
## T2cogkch T2noncog T2total T2cog
## Min. :0.000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:13.00 1st Qu.:11.00
## Median :2.000 Median :3.000 Median :18.00 Median :15.00
## Mean :1.365 Mean :2.538 Mean :16.96 Mean :14.42
## 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:22.00 3rd Qu.:19.00
## Max. :2.000 Max. :6.000 Max. :29.00 Max. :24.00
##
## T3cogtsz T3cogpf T3cogdth T3cogck
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :3.000 Median :3.000
## Mean :2.973 Mean :2.131 Mean :2.942 Mean :2.685
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :4.000 Max. :7.000 Max. :5.000
##
## T3cogkch T3noncog T3total T3cog
## Min. :0.0000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:10.00 1st Qu.: 8.00
## Median :1.0000 Median :2.000 Median :14.00 Median :12.00
## Mean :0.9923 Mean :2.362 Mean :14.08 Mean :11.72
## 3rd Qu.:2.0000 3rd Qu.:3.000 3rd Qu.:18.00 3rd Qu.:15.00
## Max. :2.0000 Max. :6.000 Max. :29.00 Max. :24.00
##
#wnw_istd = Index of Signal Detection, Yes/No vocabulary test 1 Level 1 (Meara 1992)
#tasks_n / evaluation = feedback of intervention group on number of tasks completed and pleasantness/usefulness/novelty
#T1/T2/T3 = pre-/post-/delayed post-test vocabulary learning activity, cog = cognates
#Improvement from T1 to T2 and T3 and overall score on vocabulary learning activities
data$T2T1cog <- data$T2cog - data$T1cog #improvement on cognates from T1 to T2
data$T3T1cog <- data$T3cog - data$T1cog #improvement on cognates from T1 to T3
data$overall <- data$T1total + data$T2total + data$T3total #overall performance across three tests on all items
data$cognates <- data$T1cog + data$T2cog + data$T3cog #overall performance across three tests on cognates
#Improvement from T1 to T2 on cognates by correspondence
data$T2T1tsz <- (data$T2cogtsz - data$T1cogtsz)/6
data$T2T1pf <- (data$T2cogpf - data$T1cogpf)/4
data$T2T1thdt <- (data$T2cogdth - data$T1cogdth)/7
data$T2T1ck <- (data$T2cogck - data$T1cogck)/5
data$T2T1kch <- (data$T2cogkch - data$T1cogkch)/2
#Improvement from T1 to T3 on cognates by correspondence
data$T3T1tsz <- (data$T3cogtsz - data$T1cogtsz)/6
data$T3T1pf <- (data$T3cogpf - data$T1cogpf)/4
data$T3T1thdt <- (data$T3cogdth - data$T1cogdth)/7
data$T3T1ck <- (data$T3cogck - data$T1cogck)/5
data$T3T1kch <- (data$T3cogkch - data$T1cogkch)/2
#sum-code treatment groups and center Yes/No test
data$n.Group <- ifelse(data$Group == "intervention", 1, 0)
data$c.wnw_istd <- data$wnw_istd - mean(data$wnw_istd, na.rm = TRUE)
#Create subset intervention group only
dataint <- subset(data,n.Group==1)
######################### INITIAL DIFFERENCES ###############################
#Boxplots T1 by treatment (vocabulary learning activity and yes-no test)
yesnotreatment <- ggplot(data = data,
aes(x = Group,
y = (wnw_istd), color = Group)) +
scale_color_grey(start=0.6, end=0.1) +
theme_classic() +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Treatment group") +
ylab("Performance") +
theme_bw() +
ggtitle("Yes/No test",
sub = "initial vocabulary knowledge")
T1cogtreatment <- ggplot(data = data,
aes(x = Group,
y = (T1cog), color = Group)) +
scale_color_grey(start=0.6, end=0.1) +
theme_classic() +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Treatment group") +
ylab("Performance") +
theme_bw() +
ggtitle("Performance on cognates",
sub ="Vocabulary learning activity T1")
# Group initial differences between treatment groups
t.test(data$T1total~data$Group)
##
## Welch Two Sample t-test
##
## data: data$T1total by data$Group
## t = 1.7762, df = 257.99, p-value = 0.07687
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1319172 2.5604886
## sample estimates:
## mean in group control mean in group intervention
## 15.76429 14.55000
t.test(data$T1cog~data$Group)
##
## Welch Two Sample t-test
##
## data: data$T1cog by data$Group
## t = 1.6756, df = 256.23, p-value = 0.09503
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1618784 2.0094975
## sample estimates:
## mean in group control mean in group intervention
## 13.10714 12.18333
t.test(data$wnw_istd~data$Group)
##
## Welch Two Sample t-test
##
## data: data$wnw_istd by data$Group
## t = 0.25019, df = 257.21, p-value = 0.8026
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.04858794 0.06273079
## sample estimates:
## mean in group control mean in group intervention
## 0.4190714 0.4120000
################## IMPROVEMENT BY TREATMENT AND YES/NO TEST #######################
# Boxplots improvement on cognates by treatment
impbytreat1 <- ggplot(data = data,
aes(x = Group,
y = T2T1cog)) +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Treatment group") +
ylab("Improvement T2-T1") +
theme_bw() +
ggtitle("Improvement on cognates T2-T1",
sub = "by treatment group")
impbytreat2 <- ggplot(data = data,
aes(x = Group,
y = (T3T1cog))) +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Treatment group") +
ylab("Improvement T3-T1") +
theme_bw() +
ggtitle("Improvement on cognates T3-T1",
sub = "by treatment group")
ggsave(file = "impbytreat1.eps", plot=impbytreat1, width = 3.10, height= 4.14)
ggsave(file = "impbytreat2.eps", plot=impbytreat2, width = 3.10, height= 4.14)
#Boxplots Improvement by class grey scales
T2T1byclass <- ggplot(data = data,
aes(x = fct_reorder(Class,(T2T1cog)),
y = (T2T1cog), color = Group),
outlier.shape = NA) +
scale_color_grey(start=0.6, end=0.1) +
theme_classic() +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Class") +
ylab("Improvement T2-T1") +
theme_bw() +
ggtitle("Improvement on cognates from T1 to T2",
sub = "by class")
T3T1byclass <- ggplot(data = data,
aes(x = fct_reorder(Class,(T3T1cog)),
y = (T3T1cog), color = Group)) +
scale_color_grey(start=0.6, end=0.1) +
theme_classic() +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.4,
height = 0.0),
shape = 1) +
xlab("Class") +
ylab("Improvement T3-T1") +
theme_bw() +
ggtitle("Improvement on cognates from T1 to T3",
sub = "by class")
ggsave(file = "T2T1byclass.eps", plot=T2T1byclass, width = 6.21, height= 3.55)
ggsave(file = "T3T1byclass.eps", plot=T3T1byclass, width = 6.21, height= 3.55)
#Linear Regression Model
summary(lmer(T2T1cog~n.Group*c.wnw_istd+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1cog ~ n.Group * c.wnw_istd + (1 | Class)
## Data: data
##
## REML criterion at convergence: 1393.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0124 -0.5442 -0.0443 0.6086 4.0713
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.289 0.5376
## Residual 12.625 3.5531
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.6811 0.3493 14.7805 1.950 0.070416 .
## n.Group 2.3529 0.5190 13.0494 4.533 0.000557 ***
## c.wnw_istd 1.0010 1.2699 255.0962 0.788 0.431278
## n.Group:c.wnw_istd -1.2440 1.9835 255.9789 -0.627 0.531094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) n.Grop c.wnw_
## n.Group -0.673
## c.wnw_istd -0.001 0.001
## n.Grp:c.wn_ 0.001 0.012 -0.640
summary(lmer(T3T1cog~n.Group*c.wnw_istd+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1cog ~ n.Group * c.wnw_istd + (1 | Class)
## Data: data
##
## REML criterion at convergence: 1407.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2065 -0.5702 -0.0141 0.6526 2.2211
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 1.07 1.034
## Residual 12.93 3.595
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.0608 0.4538 15.4340 -4.541 0.000364 ***
## n.Group 2.4782 0.6846 13.9096 3.620 0.002812 **
## c.wnw_istd -0.9346 1.2964 251.5793 -0.721 0.471635
## n.Group:c.wnw_istd -1.4201 2.0307 253.4828 -0.699 0.484972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) n.Grop c.wnw_
## n.Group -0.663
## c.wnw_istd 0.009 -0.006
## n.Grp:c.wn_ -0.006 0.017 -0.638
################## IMPROVEMENT BY TYPE OF CORRESPONDENCE #######################
#Improvement by class per correspondence rule
imp_cog_rule <- data %>%
group_by(Class, Group, n.Group) %>%
summarise(
mean_T2T1_ck = mean(T2T1ck), mean_T3T1_ck = mean(T3T1ck),
mean_T2T1_pf = mean(T2T1pf), mean_T3T1_pf = mean(T3T1pf),
mean_T2T1_thdt = mean(T2T1thdt), mean_T3T1_thdt = mean(T3T1thdt),
mean_T2T1_tsz = mean(T2T1tsz), mean_T3T1_tsz = mean(T3T1tsz),
mean_T2T1_kch = mean(T2T1kch), mean_T3T1_kch = mean(T3T1kch),
.groups = "drop"
)
#rearrange data
imp_cog <- imp_cog_rule %>%
pivot_longer(cols = starts_with("mean"),
names_to = c("time", "rule"),
names_pattern = "mean_(.*)_(.*)",
values_to = "mean",
values_drop_na = TRUE)
imp_cog$rule <- factor(imp_cog$rule,
levels=c("ck", "pf", "thdt", "tsz", "kch"))
#boxplot
imp_correspondence <- ggplot(data = imp_cog,
aes(x = Group,
y = (mean))) +
ylim(c(-0.4, +0.4)) +
theme_classic() +
geom_boxplot() +
geom_point(position = position_jitter(width = 0.1,
height = 0.0),
shape = 1) +
facet_grid(time~rule) +
xlab("Treatment group") +
ylab("Progress") +
theme_bw() +
ggtitle("Improvement by type of correspondence",
sub = "by treatment (M per class) and test time")
#Linear Mixed-Effect Regression Models T2-T1
summary(lmer(T2T1ck~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1ck ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 52.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55632 -0.61851 -0.01167 0.62805 2.47739
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.002593 0.05092
## Residual 0.067223 0.25927
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.04323 0.02760 16.66389 1.566 0.1361
## n.Group 0.08342 0.04122 14.77796 2.024 0.0615 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.670
summary(lmer(T2T1pf~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1pf ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 116.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1387 -0.6612 0.1423 0.8217 2.7558
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.004715 0.06867
## Residual 0.085648 0.29266
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03923 0.03345 15.47588 -1.173 0.25855
## n.Group 0.16731 0.05017 13.79543 3.335 0.00499 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.667
summary(lmer(T2T1thdt~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1thdt ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: -16.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6327 -0.7248 -0.0572 0.6722 3.2101
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.002727 0.05222
## Residual 0.051199 0.22627
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03500 0.02567 16.58268 -1.363 0.1910
## n.Group 0.08515 0.03849 14.77952 2.212 0.0431 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.667
summary(lmer(T2T1tsz~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1tsz ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 31.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12462 -0.55625 0.08973 0.77913 2.75051
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.00116 0.03407
## Residual 0.06278 0.25055
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.12687 0.02402 13.97110 5.283 0.000116 ***
## n.Group 0.09554 0.03563 12.33509 2.682 0.019569 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.674
summary(lmer(T2T1kch~n.Group+(1|Class), data=data))
## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T2T1kch ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 267.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7798 -0.2717 -0.1612 0.9823 2.3469
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.000 0.0000
## Residual 0.159 0.3987
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.06429 0.03370 258.00000 1.908 0.0575 .
## n.Group 0.04405 0.04960 258.00000 0.888 0.3753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.679
## convergence code: 0
## boundary (singular) fit: see ?isSingular
#Linear Mixed-Effect Regression Models T3-T1
summary(lmer(T3T1ck~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1ck ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 96.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3353 -0.7768 -0.0743 0.5978 3.7003
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.004499 0.06708
## Residual 0.079179 0.28139
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13151 0.03239 17.02798 -4.061 0.000811 ***
## n.Group 0.12598 0.04859 15.19965 2.593 0.020236 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.666
summary(lmer(T3T1pf~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1pf ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 108
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6113 -0.5759 -0.1241 0.6094 2.6216
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.005911 0.07688
## Residual 0.082169 0.28665
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.18877 0.03489 14.16051 -5.410 8.83e-05 ***
## n.Group 0.18559 0.05252 12.69758 3.534 0.00379 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.664
summary(lmer(T3T1thdt~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1thdt ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: -28.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4746 -0.6189 0.0357 0.6578 3.2892
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.005836 0.07639
## Residual 0.047625 0.21823
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08927 0.03086 15.65601 -2.892 0.0108 *
## n.Group 0.04601 0.04682 14.29743 0.983 0.3421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.659
summary(lmer(T3T1tsz~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1tsz ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 22.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5628 -0.5718 0.1340 0.7291 2.3325
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.005279 0.07266
## Residual 0.058498 0.24186
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03516 0.03126 15.59927 1.125 0.2776
## n.Group 0.12054 0.04720 14.09217 2.554 0.0229 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.662
summary(lmer(T3T1kch~n.Group+(1|Class), data=data))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T3T1kch ~ n.Group + (1 | Class)
## Data: data
##
## REML criterion at convergence: 301.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1770 -0.8667 0.1905 0.3214 2.7177
##
## Random effects:
## Groups Name Variance Std.Dev.
## Class (Intercept) 0.006008 0.07751
## Residual 0.177095 0.42083
## Number of obs: 260, groups: Class, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10969 0.04382 13.48376 -2.503 0.0259 *
## n.Group 0.02603 0.06534 11.92433 0.398 0.6974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## n.Group -0.671
###################### ATTITUDE #############################
cor.test(dataint$evaluation, dataint$T2T1cog) #attitude towards intervention with progress
##
## Pearson's product-moment correlation
##
## data: dataint$evaluation and dataint$T2T1cog
## t = -0.32121, df = 118, p-value = 0.7486
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2076975 0.1504820
## sample estimates:
## cor
## -0.02955654
cor.test(dataint$evaluation, dataint$T3T1cog)
##
## Pearson's product-moment correlation
##
## data: dataint$evaluation and dataint$T3T1cog
## t = 0.60729, df = 118, p-value = 0.5448
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1246704 0.2327311
## sample estimates:
## cor
## 0.05581825
cor.test(dataint$tasks_n, dataint$T2T1cog) #number of tasks completed with progress
##
## Pearson's product-moment correlation
##
## data: dataint$tasks_n and dataint$T2T1cog
## t = -0.4975, df = 118, p-value = 0.6198
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2231619 0.1345945
## sample estimates:
## cor
## -0.04575058
cor.test(dataint$tasks_n, dataint$T3T1cog)
##
## Pearson's product-moment correlation
##
## data: dataint$tasks_n and dataint$T3T1cog
## t = -0.23777, df = 118, p-value = 0.8125
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2003388 0.1579777
## sample estimates:
## cor
## -0.02188329