LET- Life Engagement Test (Scheier et al., 2006)
Repeated Measures for Life Engagement, w/ only the positive questions
#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
# Load the psych package
library(psych)Creating a new variable that is the mean of all positive purpose meanLET questions
items <- c("LET1", "LET2", "LET3", "LET4", "LET5", "LET6")
scaleKey <- c(1, 1, 1,1,1,1)
data.test4$meanLET <- scoreItems(scaleKey, items=data.test4[,items], delete=FALSE)$score## Warning: NaNs producedlibrary(reshape2); library(car)## Warning: package 'car' was built under R version 3.1.2##
## Attaching package: 'car'
##
## The following object is masked from 'package:psych':
##
## logitdata <- data.test4[,c("ID", "GROUP", "wave", "meanLET")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanLET")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )Create new data set with ID Group baseline meanLET and wave so that we have Baseline, time 1 and 2 to compare to
data2 <- as.data.frame(mapply(c,data[,1:4], data[,c(1:3,5)]))
data2$wave <- rep(1:2, each=89)
names(data2) <- c("ID", "GROUP", "BASELINE", "meanLET", "WAVE")Drop the cases where participants did not complete the intervention completely
#data2 <- data2[-c(which(data2$GROUP ==2)),]Intention to treat model (ITT) where we keep the cases who dropped out and did not complete the study (http://en.wikipedia.org/wiki/Intention-to-treat_analysis).
data2[which(data2$GROUP ==2), "GROUP"] <- 1For lme to work GROUP and ID need to be seen as factors
data2$GROUP <-as.factor(data2$GROUP)
data2$ID <-as.factor(data2$ID)Describe the meanLET variable by the GROUP variable
describeBy(data2[,3:4], group = data2$GROUP)## group: 0
## vars n mean sd median trimmed mad min max range skew
## BASELINE 1 86 3.21 0.41 3.17 3.21 0.49 2.17 4.17 2.00 -0.07
## meanLET 2 59 3.07 0.30 3.00 3.04 0.25 2.67 4.00 1.33 1.08
## kurtosis se
## BASELINE -0.24 0.04
## meanLET 1.39 0.04
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## BASELINE 1 88 3.20 0.4 3.17 3.19 0.49 2.33 4 1.67 0.28 -0.41
## meanLET 2 54 3.07 0.3 3.00 3.04 0.25 2.50 4 1.50 1.06 1.24
## se
## BASELINE 0.04
## meanLET 0.04Create a plot that visualizes meanLET variable by the GROUP variable
library(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%Take a look at the residuals
residual <- lm(meanLET ~ BASELINE, data=data2)$residualPlot the residuals to see that they are random
plot(density(residual))# A density plot
qqnorm(residual) # A quantile normal plot to checking normality
qqline(residual)
Checking the different between intervention and control groups residuals. This allows us to control for individual unsystematic differences.
data2$residual <- NA
sel1 <- which(!is.na(data2$meanLET))
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, meanLET, data=data2, geom="boxplot")## Warning: Removed 65 rows containing non-finite values (stat_boxplot).
Plot of the difference between intervention and control groups.
qplot(GROUP, residual, data=data2, geom="boxplot")## Warning: Removed 69 rows containing non-finite values (stat_boxplot).
Two way repeated measures ======================================================== Graphing the Two-Way Interaction. Both meanLET and the Residuals
# Load the nlme package
library(nlme)
with(data2, boxplot(meanLET ~ WAVE + GROUP))
with(data2, boxplot(residual ~ WAVE + GROUP))
Comparing Basline to Wave 2 and 3 by Group.
fullModel <- lme(meanLET ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = data2, method = "ML", na.action = "na.omit")Explanation of significance:
We asses the significance of our models by comparing them from the baseline model using the anova() function.
(Intercept): Where everything is 0
GROUP1: Is there a difference between group. If it is significant than there is a difference and the treatment had an effect.
WAVE: Asseses whether the effects gets bigger beteen time 2 and 3 (does not have to be significant)
BASELINE: Should not be significant. If it is then it shows that there is a difference between groups before the treatment.
GROUP1:WAVE: If this is significant then it means that the effect was either fleeting or it happened after the treatment i.e. between time 2 and 3.
summary(fullModel)## Linear mixed-effects model fit by maximum likelihood
## Data: data2
## AIC BIC logLik
## 36.7 55.54 -11.35
##
## Random effects:
## Formula: ~1 | ID
## (Intercept) Residual
## StdDev: 0.06798 0.26
##
## Fixed effects: meanLET ~ GROUP * WAVE + BASELINE
## Value Std.Error DF t-value p-value
## (Intercept) 1.9569 0.24880 66 7.865 0.0000
## GROUP1 0.3495 0.15755 66 2.218 0.0300
## WAVE 0.1195 0.07121 38 1.678 0.1015
## BASELINE 0.3030 0.07250 66 4.179 0.0001
## GROUP1:WAVE -0.2497 0.10485 38 -2.381 0.0224
## Correlation:
## (Intr) GROUP1 WAVE BASELI
## GROUP1 -0.237
## WAVE -0.383 0.635
## BASELINE -0.904 -0.057 -0.020
## GROUP1:WAVE 0.237 -0.939 -0.680 0.040
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.8117 -0.5706 -0.1252 0.4203 2.6001
##
## Number of Observations: 109
## Number of Groups: 69Table with P-values
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| (Intercept) | 1.9569 | 0.2488 | 66.0000 | 7.8653 | 0.0000 |
| GROUP1 | 0.3495 | 0.1576 | 66.0000 | 2.2182 | 0.0300 |
| WAVE | 0.1195 | 0.0712 | 38.0000 | 1.6783 | 0.1015 |
| BASELINE | 0.3030 | 0.0725 | 66.0000 | 4.1795 | 0.0001 |
| GROUP1:WAVE | -0.2497 | 0.1048 | 38.0000 | -2.3815 | 0.0224 |
``` Table with confidence intervals
| est. | lower | upper | |
|---|---|---|---|
| (Intercept) | 1.9569 | 1.4716 | 2.4421 |
| GROUP1 | 0.3495 | 0.0422 | 0.6568 |
| WAVE | 0.1195 | -0.0213 | 0.2603 |
| BASELINE | 0.3030 | 0.1616 | 0.4444 |
| GROUP1:WAVE | -0.2497 | -0.4570 | -0.0424 |