Emily Griffith - Academic Self Concept

``` Load Packages

library(arm); library(lmerTest); library(psych)

## Loading required package: MASS
## Loading required package: Matrix
## Loading required package: lme4
## Loading required package: Rcpp
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## arm (Version 1.7-03, built: 2014-4-27)
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## Working directory is /private/var/folders/9c/88_yf73x0ys02zvn5hkvvq6r0000gn/T/RtmpmxWTU5
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## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
## 
## Attaching package: 'lmerTest'
## 
## The following object is masked from 'package:lme4':
## 
##     lmer
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## The following object is masked from 'package:stats':
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##     step
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## Attaching package: 'psych'
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## The following objects are masked from 'package:arm':
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##     logit, rescale, sim

Loading Data

setwd("/Volumes/TOSHIBA EXT/Dropbox/Schools Study Data/Emily Griffith Data and R Scripts")

data <- read.csv("EmilyGriffith_all.csv")

Creating School ID as ID

data$ID <- data$Q1

#Create scale scores
data$meanASDQII <- apply(data[, c("ASDQII_1",  "ASDQII_2",  "ASDQII_3",  "ASDQII_4",  "ASDQII_5", "ASDQII_6",  "ASDQII_7",  "ASDQII_8",  "ASDQII_9", "ASDQII_10", "ASDQII_11", "ASDQII_12", "ASDQII_13", "ASDQII_14", "ASDQII_15", "ASDQII_16", "ASDQII_17", "ASDQII_18", "ASDQII_19", "ASDQII_20")], 1, mean, na.rm = TRUE)
#Means or plotting
data$baseline <- ifelse(data$Time<4,0,1) 
pdata <- tapply(data[,"meanASDQII"], data[,3], mean, na.rm=TRUE)
plot(pdata, type="l")

M0 <- lmer(meanASDQII ~ 1 + (1|ID), data=data)
fixef(M0)

## (Intercept) 
##       4.221

confint(M0)

## Computing profile confidence intervals ...

##              2.5 % 97.5 %
## .sig01      0.4741 0.8347
## .sigma      0.4724 0.6679
## (Intercept) 4.0146 4.4265

M1 <- update(M0, .~. + Time, REML=FALSE)
fixef(M1)

## (Intercept)        Time 
##     4.03955     0.08376

confint(M1)

## Computing profile confidence intervals ...

##                2.5 % 97.5 %
## .sig01       0.46360 0.8219
## .sigma       0.47068 0.6657
## (Intercept)  3.70961 4.3691
## Time        -0.03605 0.2061

M2 <- update(M1, .~. + baseline)
fixef(M2)

## (Intercept)        Time    baseline 
##     4.09337     0.05347     0.12849

confint(M2)

## Computing profile confidence intervals ...

##               2.5 % 97.5 %
## .sig01       0.4664 0.8252
## .sigma       0.4687 0.6632
## (Intercept)  3.7083 4.4763
## Time        -0.1089 0.2196
## baseline    -0.3485 0.6001

M3 <- update(M2, .~. + I(Time^2))
fixef(M3)

## (Intercept)        Time    baseline   I(Time^2) 
##      4.6869     -0.6264     -0.4493      0.1693

confint(M3)

## Computing profile confidence intervals ...

##                2.5 % 97.5 %
## .sig01       0.47651 0.8332
## .sigma       0.45952 0.6506
## (Intercept)  3.79193 5.5681
## Time        -1.55562 0.3162
## baseline    -1.36252 0.4664
## I(Time^2)   -0.06182 0.3981

Pdata <- tapply(data[,"meanASDQII"], data[,3], mean, na.rm=TRUE)
# Add random noise to time to better see the points of interest
data$TimeJIT <- data$Time+runif(126, min=-.1, max=.1)
with(data, plot(TimeJIT, meanASDQII, col="grey", pch="*"))
lines(pdata, col="red", lwd=2)

boxplot(data$meanASDQII~Time, data=data, notch=F, col=(c("red","blue", "green", "gold")), main="Academic Self Concept", xlab="Academic Self Concept", xlim = c(), ylim = c(2, 8 ), yaxs = "i")