LET- Life Engagement Test (Scheier et al., 2006)

Data Preperation on April, 2nd 2014 - these are preliminary results with on 22 people in the intervention group reporting.

setwd("/Users/levibrackman/Desktop/Adult_study")
data_pre <- read.csv("pre.csv")
data_post <- read.csv("post.csv")

table(data_pre$GROUP, dnn = "group")

## group
##  0  1 
## 35 32

data <- merge(data_pre, data_post, by = "ID", all = TRUE)
library(psych)
library(lattice)
# Time 1 LET
items <- grep("LET[0-6]*.x", names(data), value = TRUE)
scaleKey <- c(-1, 1, -1, 1, -1, 1)
data$meanT1LET <- scoreItems(scaleKey, items = data[, items], delete = FALSE)$score
# T2 LET
items <- grep("LET[0-6]*.y", names(data), value = TRUE)
scaleKey <- c(-1, 1, -1, 1, -1, 1)
data$meanT2LET <- scoreItems(scaleKey, items = data[, items], delete = FALSE)$score


# All LET questions
data$T1LET <- apply(data[, c("LET1.x", "LET2.x", "LET3.x", "LET4.x", "LET5.x", 
    "LET6.x", "meanT1LET")], 1, mean, na.rm = TRUE)
data$T2LET <- apply(data[, c("LET1.y", "LET2.y", "LET3.y", "LET4.y", "LET5.y", 
    "LET6.y", "meanT2LET")], 1, mean, na.rm = TRUE)

plot(data$T1LET, data$T2LET, ylab = "Pre", xlab = "Post", main = "Life Staisfaction")

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# pre test plots
bwplot(GROUP.x ~ T1LET, ylab = "GROUP", xlab = "LET", main = "Pre test", data = data)

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# post test plots
bwplot(GROUP.x ~ T2LET, ylab = "Group", xlab = "LET", main = "Post test", data = data)

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# Pre test
t.test(T1LET ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T1LET by GROUP.x
## t = 0.9578, df = 64.98, p-value = 0.3417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.07754  0.22044
## sample estimates:
## mean in group 0 mean in group 1 
##           3.301           3.230

t.test(T2LET ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T2LET by GROUP.x
## t = -1.078, df = 64.25, p-value = 0.2851
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4610  0.1378
## sample estimates:
## mean in group 0 mean in group 1 
##           3.525           3.687

# Ancova, Model for LET
LET_ANCOVA <- lm(T2LET ~ as.factor(GROUP.x) + T1LET, data = data)
# check assumptions visually
plot(LET_ANCOVA)

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# see results
summary(LET_ANCOVA)

## 
## Call:
## lm(formula = T2LET ~ as.factor(GROUP.x) + T1LET, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -0.766 -0.435 -0.233  0.705  1.121 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            2.143      0.814    2.63    0.011 *
## as.factor(GROUP.x)1    0.191      0.149    1.29    0.202  
## T1LET                  0.419      0.245    1.71    0.092 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.604 on 64 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.0606, Adjusted R-squared:  0.0312 
## F-statistic: 2.06 on 2 and 64 DF,  p-value: 0.135

Rerun with different factors

When I conducted a factor analysis I found that there were two factors in the LET, questions 1,3,5 (negative questions) being one factor and 2 ,4, 6 being another. I will re-run these same tests with different factors. frst 2,4,6.


# Time 1 LET
items <- grep("LET[0-6]*.x", names(data), value = TRUE)
scaleKey <- c(-1, 1, -1, 1, -1, 1)
data$meanT1LET <- scoreItems(scaleKey, items = data[, items], delete = FALSE)$score
# T2 LET
items <- grep("LET[0-6]*.y", names(data), value = TRUE)
scaleKey <- c(-1, 1, -1, 1, -1, 1)
data$meanT2LET <- scoreItems(scaleKey, items = data[, items], delete = FALSE)$score


# Only questions 2, 4, 6 of LET
data$T1LET_Factor2 <- apply(data[, c("LET2.x", "LET4.x", "LET6.x", "meanT1LET")], 
    1, mean, na.rm = TRUE)
data$T2LET_Factor2 <- apply(data[, c("LET2.y", "LET4.y", "LET6.y", "meanT2LET")], 
    1, mean, na.rm = TRUE)

plot(data$T1LET_Factor2, data$T2LET_Factor2, ylab = "Pre", xlab = "Post", main = "LET")

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# pre test plots
bwplot(GROUP.x ~ T1LET_Factor2, ylab = "GROUP", xlab = "LET", main = "Pre test", 
    data = data)

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# post test plots
bwplot(GROUP.x ~ T2LET_Factor2, ylab = "Group", xlab = "LET", main = "Post test", 
    data = data)

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# Pre test
t.test(T1LET_Factor2 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T1LET_Factor2 by GROUP.x
## t = 1.712, df = 62.45, p-value = 0.09183
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04487  0.58110
## sample estimates:
## mean in group 0 mean in group 1 
##           4.085           3.816

t.test(T2LET_Factor2 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T2LET_Factor2 by GROUP.x
## t = -0.7959, df = 64.82, p-value = 0.429
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.3457  0.1487
## sample estimates:
## mean in group 0 mean in group 1 
##           4.287           4.385

# Ancova, Model for LET
LET_Factor2_ANCOVA <- lm(T2LET_Factor2 ~ as.factor(GROUP.x) + T1LET_Factor2, 
    data = data)
# check assumptions visually
plot(LET_Factor2_ANCOVA)

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# see results
summary(LET_Factor2_ANCOVA)

## 
## Call:
## lm(formula = T2LET_Factor2 ~ as.factor(GROUP.x) + T1LET_Factor2, 
##     data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0432 -0.2417  0.0315  0.2488  0.8962 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.3995     0.3406    7.04  1.6e-09 ***
## as.factor(GROUP.x)1   0.2224     0.1048    2.12    0.038 *  
## T1LET_Factor2         0.4621     0.0816    5.66  3.8e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.419 on 64 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.34,   Adjusted R-squared:  0.32 
## F-statistic: 16.5 on 2 and 64 DF,  p-value: 1.65e-06

Now I will run it with 1,3,5 (negative questions) .

# Only questions 1, 3, 5 of LET
data$T1LET_Factor1 <- apply(data[, c("LET1.x", "LET3.x", "LET5.x", "meanT1LET")], 
    1, mean, na.rm = TRUE)
data$T2LET_Factor1 <- apply(data[, c("LET1.y", "LET3.y", "LET5.y", "meanT2LET")], 
    1, mean, na.rm = TRUE)

plot(data$T1LET_Factor1, data$T2LET_Factor1, ylab = "Pre", xlab = "Post", main = "LET")

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# pre test plots
bwplot(GROUP.x ~ T1LET_Factor1, ylab = "GROUP", xlab = "LET", main = "Pre test", 
    data = data)

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# post test plots
bwplot(GROUP.x ~ T2LET_Factor1, ylab = "Group", xlab = "LET", main = "Post test", 
    data = data)

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# Pre test
t.test(T1LET_Factor1 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T1LET_Factor1 by GROUP.x
## t = -0.6192, df = 63.83, p-value = 0.538
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.3565  0.1878
## sample estimates:
## mean in group 0 mean in group 1 
##           2.677           2.762

t.test(T2LET_Factor1 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T2LET_Factor1 by GROUP.x
## t = -0.7742, df = 63.23, p-value = 0.4417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.7156  0.3159
## sample estimates:
## mean in group 0 mean in group 1 
##           2.951           3.151

# Ancova, Model for LET
LET_Factor1_ANCOVA <- lm(T2LET_Factor1 ~ as.factor(GROUP.x) + T1LET_Factor1, 
    data = data)
# check assumptions visually
plot(LET_Factor1_ANCOVA)

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# see results
summary(LET_Factor1_ANCOVA)

## 
## Call:
## lm(formula = T2LET_Factor1 ~ as.factor(GROUP.x) + T1LET_Factor1, 
##     data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.354 -0.641 -0.337  0.728  2.233 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.543      0.579    0.94     0.35    
## as.factor(GROUP.x)1    0.124      0.229    0.54     0.59    
## T1LET_Factor1          0.900      0.208    4.32  5.5e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.932 on 64 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.233,  Adjusted R-squared:  0.209 
## F-statistic: 9.73 on 2 and 64 DF,  p-value: 0.000205

Now I will run it with just question 1 (negative question) “There is not enough purpose in my life.”

# Only questions 1 of LET
data$T1LETQ1 <- apply(data[("LET1.x")], 1, mean, na.rm = TRUE)
data$T2LETQ1 <- apply(data[("LET1.y")], 1, mean, na.rm = TRUE)

plot(data$T1LETQ1, data$T2LETQ1, ylab = "Pre", xlab = "Post", main = "LET")

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# pre test plots
bwplot(GROUP.x ~ T1LETQ1, ylab = "GROUP", xlab = "LET", main = "Pre test", data = data)

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# post test plots
bwplot(GROUP.x ~ T2LETQ1, ylab = "Group", xlab = "LET", main = "Post test", 
    data = data)

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# Pre test
t.test(T1LETQ1 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T1LETQ1 by GROUP.x
## t = -0.7225, df = 64.31, p-value = 0.4726
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.9008  0.4223
## sample estimates:
## mean in group 0 mean in group 1 
##           2.886           3.125

t.test(T2LETQ1 ~ GROUP.x, data = data)

## 
##  Welch Two Sample t-test
## 
## data:  T2LETQ1 by GROUP.x
## t = 0.616, df = 43.94, p-value = 0.5411
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.5285  0.9937
## sample estimates:
## mean in group 0 mean in group 1 
##           2.423           2.190

# Ancova, Model for LET
LETQ1ANCOVA <- lm(T2LETQ1 ~ as.factor(GROUP.x) + T1LETQ1, data = data)
# check assumptions visually
plot(LETQ1ANCOVA)

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# see results
summary(LETQ1ANCOVA)

## 
## Call:
## lm(formula = T2LETQ1 ~ as.factor(GROUP.x) + T1LETQ1, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2529 -0.4167  0.0937  0.5833  2.0955 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.556      0.331    1.68     0.10 .  
## as.factor(GROUP.x)1   -0.324      0.270   -1.20     0.24    
## T1LETQ1                0.674      0.100    6.74  2.8e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.92 on 44 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.512,  Adjusted R-squared:  0.49 
## F-statistic: 23.1 on 2 and 44 DF,  p-value: 1.41e-07