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("LET2", "LET4", "LET6")
scaleKey <- c(1, 1, 1)
data.test4$meanLET  <- scoreItems(scaleKey, items=data.test4[,items], delete=FALSE)$score
library(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':
## 
##     logit
data <- 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"] <- 1

For 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 4.02 0.70   4.00    4.08 0.49   2   5     3 -0.73
## meanLET     2 59 4.12 0.76   4.33    4.21 0.49   2   5     3 -0.92
##          kurtosis   se
## BASELINE     0.38 0.08
## meanLET      0.18 0.10
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad  min max range  skew
## BASELINE    1 88 3.79 0.77   4.00    3.84 0.99 2.00   5  3.00 -0.55
## meanLET     2 54 4.27 0.64   4.33    4.34 0.49 2.67   5  2.33 -0.90
##          kurtosis   se
## BASELINE    -0.35 0.08
## meanLET      0.15 0.09

Create 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)$residual

Plot the residuals to see that they are random

plot(density(residual))# A density plot

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qqnorm(residual) # A quantile normal plot to checking normality
qqline(residual)

plot of chunk unnamed-chunk-10 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 chunk unnamed-chunk-11 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).

plot of chunk unnamed-chunk-12 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))

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with(data2, boxplot(residual ~ WAVE + GROUP))

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Linear Mixed-Effects Model

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")
Results

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
##   174.7 193.6 -80.36
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.3649   0.3843
## 
## Fixed effects: meanLET ~ GROUP * WAVE + BASELINE 
##              Value Std.Error DF t-value p-value
## (Intercept) 1.4176    0.3655 66   3.879  0.0002
## GROUP1      0.2221    0.2566 66   0.866  0.3897
## WAVE        0.0094    0.1120 38   0.084  0.9334
## BASELINE    0.6597    0.0819 66   8.055  0.0000
## GROUP1:WAVE 0.0453    0.1637 38   0.276  0.7837
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.353                     
## WAVE        -0.364  0.597              
## BASELINE    -0.880  0.034 -0.065       
## GROUP1:WAVE  0.247 -0.882 -0.684  0.047
## 
## Standardized Within-Group Residuals:
##      Min       Q1      Med       Q3      Max 
## -3.78174 -0.35659  0.05225  0.39335  2.31472 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

Value Std.Error DF t-value p-value
(Intercept) 1.4176 0.3655 66.0000 3.8789 0.0002
GROUP1 0.2221 0.2566 66.0000 0.8658 0.3897
WAVE 0.0094 0.1120 38.0000 0.0841 0.9334
BASELINE 0.6597 0.0819 66.0000 8.0552 0.0000
GROUP1:WAVE 0.0453 0.1637 38.0000 0.2764 0.7837

``` Table with confidence intervals

est. lower upper
(Intercept) 1.4176 0.7049 2.1304
GROUP1 0.2221 -0.2782 0.7225
WAVE 0.0094 -0.2121 0.2309
BASELINE 0.6597 0.4999 0.8194
GROUP1:WAVE 0.0453 -0.2785 0.3691