library(shiny)
library(shinyAce)
library(psych)
library(CTT)
library(lattice)
library(latticeExtra)
library(beeswarm)
shinyServer(function(input, output, clientData, session) {
observe({
########################################
# Difference Index (pre-post test)
########################################
# Basic stats ==========================================================
prepost.ds <- reactive({
if (input$rowname == 1) {
x <- read.csv(text=input$pretest, sep="\t")
x <- x[,-1]
y <- read.csv(text=input$posttest, sep="\t")
y <- y[,-1]
} else {
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
}
brownRpbi <- function(data,missing) {
m <- mean(rowSums(data))
sd <- sd(rowSums(data))
totalDat <- cbind(data,rowSums(data))
sortDat <- totalDat[order(-totalDat[,length(totalDat)]),]
pbi <- c()
r <- c()
itemD <- c()
rownames(sortDat) <- c(1:nrow(sortDat))
highDat <- head(sortDat,nrow(sortDat) %/% 3)
lowDat <- tail(sortDat,nrow(sortDat) %/% 3)
for (i in 1:length(data)) {
if (is.element(colnames(data)[i], missing) == F ) {
mhigh <- mean(subset(totalDat[,length(totalDat)],(data[,i] == 1)))
mlow <- mean(subset(totalDat[,length(totalDat)],(data[,i] == 0)))
imean <- mean(data[,i])
itemD <- c(itemD,round((mean(highDat[,i]) - mean(lowDat[,i])),3))
if (imean == 1 || imean == 0) {
pbi <- c(pbi,0)
} else {
pbi <- c(pbi,round(((mhigh - mlow) / sd) * sqrt(imean * (1 - imean)),3))
}
}
}
pbiDF <- data.frame(itemD,pbi)
colnames(pbiDF) <- c("ID","rpbi")
return(pbiDF)
}
myAlpha <- function(data) {
alphaRes <- reliability(data, itemal = T)
if (length(alphaRes$N_person) == 0) {
n <- sprintf("%d",alphaRes$nPerson)
items <- sprintf("%d",alphaRes$nItem)
mean <- sprintf("%.2f",round(alphaRes$scaleMean,2))
sd <- sprintf("%.2f",round(alphaRes$scaleSD,2))
alpha <- substring(sprintf("%.3f",round(alphaRes$alpha,3)),2,5)
sem <- round(alphaRes$scaleSD * sqrt(1-alphaRes$alpha),3)
} else {
n <- sprintf("%d",alphaRes$N_person)
items <- sprintf("%d",alphaRes$N_item)
mean <- sprintf("%.2f",round(alphaRes$scale.mean,2))
sd <- sprintf("%.2f",round(alphaRes$scale.sd,2))
alpha <- substring(sprintf("%.3f",round(alphaRes$alpha,3)),2,5)
sem <- round(alphaRes$scale.sd * sqrt(1-alphaRes$alpha),3)
}
sumStats <- data.frame(Total=c(n,items,mean,sd,alpha,sem))
rownames(sumStats) <- c("N","Number of items","Mean","SD","Cronbach's alpha","SEM")
if (length(alphaRes$N_person) == 0) {
dropif <- round(ifelse(is.na(alphaRes$alphaIfDeleted),0,alphaRes$alphaIfDeleted),3)
r.drop <- round(ifelse(is.na(alphaRes$pBis), 0, alphaRes$pBis),3)
item.mean <- round(alphaRes$itemMean,3)
itemStats <- data.frame(dropif,r.drop,item.mean)
rownames(itemStats) <- colnames(data)
} else {
dropif <- round(ifelse(is.na(alphaRes$alpha.if.deleted),0,alphaRes$alpha.if.deleted),3)
r.drop <- round(ifelse(is.na(alphaRes$pbis), 0, alphaRes$pbis),3)
item.mean <- round(alphaRes$item.mean,3)
itemStats <- data.frame(dropif,r.drop,item.mean)
rownames(itemStats) <- attr(alphaRes$item.mean,"names")
}
colnames(itemStats) <- c("Drop if","r dropped","IF")
itemStats <- cbind(itemStats,brownRpbi(data,c()))
return(list(sumStats,itemStats))
}
if (ncol(x) == ncol(y) ){
alphaResult.x <- myAlpha(x)
alphaResult.y <- myAlpha(y)
list(Pretest = alphaResult.x, Posttest = alphaResult.y)
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$prepost.ds.out <- renderPrint({
prepost.ds()
})
# Difference Index ==========================================================
DI <- reactive({
if (input$rowname == 1) {
x <- read.csv(text=input$pretest, sep="\t")
x <- x[,-1]
y <- read.csv(text=input$posttest, sep="\t")
y <- y[,-1]
} else {
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
}
if (ncol(x) == ncol(y) ){
preIF <- colMeans(x, na.rm=T)
postIF <- colMeans(y, na.rm=T)
prepost <- data.frame(preIF, postIF)
prepost$DI <- prepost$postIF - prepost$preIF
colnames(prepost) <- c(" Pretest IF", " Posttest IF", " DI")
round(prepost, 3)
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$DI.out <- renderPrint({
DI()
})
# Change (gain) score ==========================================================
gain <- reactive({
pre <- read.csv(text=input$pretest, sep="\t")
post <- read.csv(text=input$posttest, sep="\t")
if (ncol(pre) == ncol(post)){
if (input$rowname == 1) {
pre$total <- rowSums(pre[,-1], na.rm=T)
x <- data.frame(pre[,1], pre$total)
post$total <- rowSums(post[,-1], na.rm=T)
y <- data.frame(post[,1], post$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1], all=T) # all=T includes "NA"
z$change <- z[,3] - z[,2]
# Merged without NA
zz <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
# Pre-calc for the following two indices
PreSD <- sd(zz[,2])
PostSD <- sd(zz[,3])
ColPrePost <- cor(zz[,2], zz[,3]) # TestRetest reliability
MeanPre <- mean(zz[,2])
MeanPost <- mean(zz[,3])
# Expected posttest score (In'nami, 2012)
ExpPost <- c()
j <- 1
for (i in 1:nrow(z)) {
if (!is.na(z[i, 4])) {
ExpPost[j] <- MeanPost + ColPrePost * (PostSD/PreSD) * (z[,2][j] - MeanPre)
j <- j + 1
} else {
ExpPost[j] <- NA
j <- j + 1
}
}
z$ExpPost <- round(ExpPost, 2)
z$diff <- z[,3] - z$ExpPost
# Corrected Posttest Scores (Bonate, 2000)
# http://bit.ly/1m3n37a
AdjPost <- c()
j <- 1
for (i in 1:nrow(z)) {
if (!is.na(z[i, 4])) {
AdjPost[j] <- z[,3][j] - (PostSD/PreSD) * (ColPrePost - 1) * (z[,2][j] - MeanPre)
j <- j + 1
} else {
AdjPost[j] <- NA
j <- j + 1
}
}
z$AdjPost <- round(AdjPost, 2)
colnames(z) <- c("ID", "Pre", "Post", "Gain", "Exp.Post", "±RTM", "Adj.Post")
z
} else { # without 1st rowname
if (nrow(pre) == nrow(post)){
x <- rowSums(pre, na.rm=T)
y <- rowSums(post, na.rm=T)
z <- data.frame(x, y)
z$change <- z[,2] - z[,1]
# Pre-calc for the following two indices
PreSD <- sd(z[,1])
PostSD <- sd(z[,2])
ColPrePost <- cor(z[,1], z[,2]) # TestRetest reliability
MeanPre <- mean(z[,1])
MeanPost <- mean(z[,2])
# Expected posttest score (In'nami, 2012)
ExpPost <- c()
j <- 1
for (i in 1:nrow(z)) {
if (!is.na(z[i, 3])) {
ExpPost[j] <- MeanPost + ColPrePost * (PostSD/PreSD) * (z[,1][j] - MeanPre)
j <- j + 1
} else {
ExpPost[j] <- NA
j <- j + 1
}
}
z$ExpPost <- round(ExpPost, 2)
z$diff <- z[,2] - z$ExpPost
# Corrected Posttest Scores (Bonate, 2000)
# http://bit.ly/1m3n37a
AdjPost <- c()
j <- 1
for (i in 1:nrow(z)) {
if (!is.na(z[i, 3])) {
AdjPost[j] <- z[,3][j] - (PostSD/PreSD) * (ColPrePost - 1) * (z[,1][j] - MeanPre)
j <- j + 1
} else {
AdjPost[j] <- NA
j <- j + 1
}
}
z$AdjPost <- round(AdjPost, 2)
colnames(z) <- c("Pre", "Post", "Gain", "Exp.Post", "±RTM", "Adj.Post")
z
} else {
cat("Your datasets need to have the same number of cases (rows).")
}
}
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$gain.out <- renderPrint({
gain()
})
# Gain score reliability ==========================================================
gain.rel <- reactive({
pre <- read.csv(text=input$pretest, sep="\t")
post <- read.csv(text=input$posttest, sep="\t")
if (ncol(pre) == ncol(post)){
if (input$rowname == 1) {
pre.alpha <- alpha(pre[,-1], check.keys=F, na.rm=T)
post.alpha <- alpha(post[,-1], check.keys=F, na.rm=T)
pre$total <- rowSums(pre[,-1], na.rm=T)
x <- data.frame(pre[,1], pre$total)
post$total <- rowSums(post[,-1], na.rm=T)
y <- data.frame(post[,1], post$total)
# Merged without NA
zz <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
# Pre-calc for the following two indices
RelA <- pre.alpha[[1]][,1] # Preの信頼性係数
RelB <- post.alpha[[1]][,1] # Postの信頼性係数
ColAB <- cor(zz[,2], zz[,3]) # PrePostの相関係数
SdA <- sd(zz[,2]) # PretestのSD
SdB <- sd(zz[,3]) # PosttestのSD
VarA <- SdA^2 # Preの分散（標準偏差の2乗）
VarB <- SdB^2 # Postの分散（標準偏差の2乗）
# Lord1963
# http://lilt.ilstu.edu/staylor/csdcb/articles/Volume9/Chiou%20et%20al%201996.pdf
numerator1 <- VarA*RelA + VarB*RelB - 2*ColAB*SdA*SdB
denominator1 <- VarA + VarB - 2*ColAB*SdA*SdB
GainRelLord <- round((numerator1/denominator1), 3)
# Thorndike (Educational Measurement, 1971)
numerator2 <- ((RelA + RelB)/2) - ColAB
denominator2 <- 1 - ColAB
GainRelThorndike <- round((numerator2/denominator2), 3)
cat(" Lord's formula (1963):", GainRelLord, "\n",
"Thorndike's formula (1971):", GainRelThorndike, "\n")
} else { # without 1st rowname
if (nrow(pre) == nrow(post)){
pre.alpha <- alpha(pre, check.keys=F, na.rm=T)
post.alpha <- alpha(post, check.keys=F, na.rm=T)
x <- rowSums(pre, na.rm=T)
y <- rowSums(post, na.rm=T)
z <- data.frame(x, y)
# Pre-calc for the following two indices
RelA <- pre.alpha[[1]][,1] # Preの信頼性係数
RelB <- post.alpha[[1]][,1] # Postの信頼性係数
ColAB <- cor(z[,1], z[,2]) # PrePostの相関係数
SdA <- sd(z[,1]) # PretestのSD
SdB <- sd(z[,2]) # PosttestのSD
VarA <- SdA^2 # Preの分散（標準偏差の2乗）
VarB <- SdB^2 # Postの分散（標準偏差の2乗）
# Lord1963
# http://lilt.ilstu.edu/staylor/csdcb/articles/Volume9/Chiou%20et%20al%201996.pdf
numerator1 <- VarA*RelA + VarB*RelB - 2*ColAB*SdA*SdB
denominator1 <- VarA + VarB - 2*ColAB*SdA*SdB
GainRelLord <- round((numerator1/denominator1), 3)
# Thorndike (Educational Measurement, 1971)
numerator2 <- ((RelA + RelB)/2) - ColAB
denominator2 <- 1 - ColAB
GainRelThorndike <- round((numerator2/denominator2), 3)
cat(" Lord's formula (1963):", GainRelLord, "\n",
"Thorndike's formula (1971):", GainRelThorndike, "\n")
} else {
cat("Your datasets need to have the same number of cases (rows).")
}
}
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$gain.rel.out <- renderPrint({
gain.rel()
})
# Overlayed histograms ==========================================================
makedistPlot <- function(){
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
} else {
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
}
simple.bincount <- function(x, breaks) {
nx <- length(x)
nbreaks <- length(breaks)
counts <- integer(nbreaks - 1)
for (i in 1:nx) {
lo <- 1
hi <- nbreaks
if (breaks[lo] <= x[i] && x[i] <= breaks[hi]) {
while (hi - lo >= 2) {
new <- (hi + lo) %/% 2
if(x[i] > breaks[new])
lo <- new
else
hi <- new
}
counts[lo] <- counts[lo] + 1
}
}
return(counts)
}
nclass.x <- nclass.FD(x)
breaks.x <- pretty(x, nclass.x)
counts.x <- simple.bincount(x, breaks.x)
counts.max.x <- max(counts.x)
nclass.y <- nclass.FD(y)
breaks.y <- pretty(y, nclass.y)
counts.y <- simple.bincount(y, breaks.y)
counts.max.y <- max(counts.y)
counts.max <- max(c(counts.max.x, counts.max.y))
xy.min <- min(c(x,y))
xy.min <- xy.min - xy.min*0.1
xy.max <- max(c(x,y))
xy.max <- xy.max + xy.max*0.1
p1 <- hist(x, xlim = c(xy.min, xy.max), ylim = c(0, counts.max*1.3))
p2 <- hist(y, xlim = c(xy.min, xy.max), ylim = c(0, counts.max*1.3))
plot(p1, las=1, xlab = "Pretest is expressed in blue; Posttest in red. Vertical lines show the mean.",
main = "", col = rgb(0,0,1,1/4), xlim = c(xy.min,xy.max), ylim = c(0, counts.max*1.3))
plot(p2, las=1, xlab = "", main = "", col = rgb(1,0,0,1/4), xlim = c(xy.min,xy.max), ylim = c(0, counts.max*1.3), add = TRUE)
abline(v = mean(x), col = "blue", lwd = 2)
abline(v = mean(y), col = "red", lwd = 2)
} else {
NULL
}
}
output$distPlot <- renderPlot({
print(makedistPlot())
})
# Boxplots ==========================================================
makeboxPlot <- function(){
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
} else {
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
}
score <- c(x, y)
group <- factor(c(rep(1, length(x)), rep(2, length(y))))
group <- factor(group, levels=c(1,2), labels=c("Pretest", "Posttest"))
boxplot(score ~ group, las=1, xlab= "Means and +/-1 SDs are displayed in red.")
beeswarm(score ~ group, col = 4, pch = 16, vert = TRUE, add = TRUE)
points(1.2, mean(x), pch = 18, col = "red", cex = 2)
arrows(1.2, mean(x), 1.2, mean(x) + sd(x), length = 0.1, angle = 45, col = "red")
arrows(1.2, mean(x), 1.2, mean(x) - sd(x), length = 0.1, angle = 45, col = "red")
points(2.2, mean(y), pch = 18, col = "red", cex = 2)
arrows(2.2, mean(y), 2.2, mean(y) + sd(y), length = 0.1, angle = 45, col = "red")
arrows(2.2, mean(y), 2.2, mean(y) - sd(y), length = 0.1, angle = 45, col = "red")
} else {
NULL
}
}
output$boxPlot <- renderPlot({
print(makeboxPlot())
})
# Spaghetti Plot ==========================================================
makeindvPlot <- function(){
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
} else {
if (nrow(x) == nrow(y)){
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
z <- data.frame(x, y)
x <- z[,1]
y <- z[,2]
} else {
NULL
}
}
xy <- c(x, y)
data.point <- factor(c(rep(1, length(x)), rep(2, length(y))))
data.point <- factor(data.point, levels=c(1,2), labels=c("Pretest", "Posttest"))
dat <- data.frame(xy, data.point)
dat$indiv <- factor(c(rep(1:length(x)), rep(1:length(y))))
each <- xyplot(xy ~ data.point, group = indiv,
type = c("l"), data = dat, xlab ="", ylab="")
a <- mean(x)
b <- mean(y)
value <- c(a, b)
data.point2 <- factor(c(rep(1, 1), rep(2, 1)))
data.point2 <- factor(data.point2, levels=c(1,2), labels=c("Pretest", "Posttest"))
dat2 <- data.frame(value, data.point2)
all <- xyplot(value ~ data.point2, col = "black",
lwd = 5, type = c("l"), data = dat, xlab = "", ylab = "")
indv <- each + as.layer(all, axes = NULL)
# print(indv)
} else {
NULL
}
}
output$indvPlot <- renderPlot({
print(makeindvPlot())
})
# Scatterplot ==========================================================
makecorrelPlot <- function(){
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
} else {
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
}
xy.min <- min(c(x,y))
xy.min <- xy.min - xy.min*0.1
xy.max <- max(c(x,y))
xy.max <- xy.max + xy.max*0.1
plot(x, y, las=1, pch = 16, xlab = "Pretest", ylab = "Posttest", main = "",
xlim = c(xy.min,xy.max), ylim = c(xy.min,xy.max))
lines(par()$usr[1:2], par()$usr[3:4], lty = 3)
} else {
NULL
}
}
output$correlPlot <- renderPlot({
print(makecorrelPlot())
})
# t-test ==========================================================
t <- reactive({
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
t.test(x, y, paired=TRUE)
} else {
if (nrow(x) == nrow(y)){
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
z <- data.frame(x, y)
x <- z[,1]
y <- z[,2]
t.test(x, y, paired=TRUE)
} else {
cat("Your datasets need to have the same number of cases (rows).")
}
}
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$t.out <- renderPrint({
t()
})
# Effect size ==========================================================
es <- reactive({
x <- read.csv(text=input$pretest, sep="\t")
y <- read.csv(text=input$posttest, sep="\t")
if (ncol(x) == ncol(y)){
if (input$rowname == 1) {
x$total <- rowSums(x[,-1], na.rm=T)
x <- data.frame(x[,1], x$total)
y$total <- rowSums(y[,-1], na.rm=T)
y <- data.frame(y[,1], y$total)
z <- merge(x=x, y=y, by.x=names(x)[1], by.y=names(y)[1]) # all=T includes "NA"
x <- z[,2]
y <- z[,3]
m1 <- mean(x)
sd1 <- sd(x)
n1 <- length(x)
m2 <- mean(y)
sd2 <- sd(y)
n2 <- length(y)
diff <- x - y
d <- abs(mean(diff)/(sd(diff)/sqrt(2*(1-cor(x, y)))))
var.d <- ((1/n1)+((d^2)/(2*n1)))*(2*(1-cor(x, y)))
df <- n1 - 1
j <- 1 - (3/(4 * df - 1))
g <- j * d
var.g <- j^2 * var.d
alpha <- 0.05
crit <- qt(alpha/2, df, lower.tail = FALSE)
lower.d <- d - crit * sqrt(var.d)
upper.d <- d + crit * sqrt(var.d)
lower.g <- g - crit * sqrt(var.g)
upper.g <- g + crit * sqrt(var.g)
cat("=======================================================", "\n")
cat(" Mean difference / within-groups SD:", "\n",
"(Based on Borenstein et al., 2009) ", "\n",
"\n",
" d [95% CI] =", d, "[", lower.d, ",", upper.d, "]", "\n",
" var(d) =", var.d, "\n",
"\n",
" g [95% CI] =", g, "[", lower.g, ",", upper.g, "]", "\n",
" var(g) =", var.g, "\n",
"\n"
)
delta <- abs(mean(diff)/sd(x))
cat("=======================================================", "\n")
cat(" Mean difference / SD of Data 1 (e.g., pretest):", "\n",
"\n",
" Delta =", delta, "\n",
"\n"
)
result.dependent<-t.test(x,y,paired=TRUE)
paired.t<-result.dependent$statistic
r2 <- sqrt(paired.t^2/(paired.t^2+df))
r2 <- r2[[1]]
cat("=======================================================", "\n")
cat(" r --- sqrt(paired.t^2/(paired.t^2+df)):", "\n",
"\n",
" r =", r2, "\n",
"\n"
)
} else {
if (nrow(x) == nrow(y)){
x <- rowSums(x, na.rm=T)
y <- rowSums(y, na.rm=T)
z <- data.frame(x, y)
x <- z[,1]
y <- z[,2]
m1 <- mean(x)
sd1 <- sd(x)
n1 <- length(x)
m2 <- mean(y)
sd2 <- sd(y)
n2 <- length(y)
diff <- x - y
d <- abs(mean(diff)/(sd(diff)/sqrt(2*(1-cor(x, y)))))
var.d <- ((1/n1)+((d^2)/(2*n1)))*(2*(1-cor(x, y)))
df <- n1 - 1
j <- 1 - (3/(4 * df - 1))
g <- j * d
var.g <- j^2 * var.d
alpha <- 0.05
crit <- qt(alpha/2, df, lower.tail = FALSE)
lower.d <- d - crit * sqrt(var.d)
upper.d <- d + crit * sqrt(var.d)
lower.g <- g - crit * sqrt(var.g)
upper.g <- g + crit * sqrt(var.g)
cat("=======================================================", "\n")
cat(" Mean difference / within-groups SD:", "\n",
"(Based on Borenstein et al., 2009) ", "\n",
"\n",
" d [95% CI] =", d, "[", lower.d, ",", upper.d, "]", "\n",
" var(d) =", var.d, "\n",
"\n",
" g [95% CI] =", g, "[", lower.g, ",", upper.g, "]", "\n",
" var(g) =", var.g, "\n",
"\n"
)
delta <- abs(mean(diff)/sd(x))
cat("=======================================================", "\n")
cat(" Mean difference / SD of Data 1 (e.g., pretest):", "\n",
"\n",
" Delta =", delta, "\n",
"\n"
)
result.dependent<-t.test(x,y,paired=TRUE)
paired.t<-result.dependent$statistic
r2 <- sqrt(paired.t^2/(paired.t^2+df))
r2 <- r2[[1]]
cat("=======================================================", "\n")
cat(" r --- sqrt(paired.t^2/(paired.t^2+df)):", "\n",
"\n",
" r =", r2, "\n",
"\n"
)
} else {
cat("Your datasets need to have the same number of cases (rows).")
}
}
} else {
cat("Your datasets need to have the same number of items (columns).")
}
})
output$es.out <- renderPrint({
es()
})
########################################
# Cut-score Indices
########################################
# Basic stats ==========================================================
cutscore.ds <- reactive({
if (input$rownameCS == 1) {
dat <- read.csv(text=input$cutscore, sep="\t")
dat <- dat[,-1]
} else {
dat <- read.csv(text=input$cutscore, sep="\t")
}
brownRpbi <- function(data,missing) {
m <- mean(rowSums(data))
sd <- sd(rowSums(data))
totalDat <- cbind(data,rowSums(data))
sortDat <- totalDat[order(-totalDat[,length(totalDat)]),]
pbi <- c()
r <- c()
itemD <- c()
rownames(sortDat) <- c(1:nrow(sortDat))
highDat <- head(sortDat,nrow(sortDat) %/% 3)
lowDat <- tail(sortDat,nrow(sortDat) %/% 3)
for (i in 1:length(data)) {
if (is.element(colnames(data)[i], missing) == F ) {
mhigh <- mean(subset(totalDat[,length(totalDat)],(data[,i] == 1)))
mlow <- mean(subset(totalDat[,length(totalDat)],(data[,i] == 0)))
imean <- mean(data[,i])
itemD <- c(itemD,round((mean(highDat[,i]) - mean(lowDat[,i])),3))
if (imean == 1 || imean == 0) {
pbi <- c(pbi,0)
} else {
pbi <- c(pbi,round(((mhigh - mlow) / sd) * sqrt(imean * (1 - imean)),3))
}
}
}
pbiDF <- data.frame(itemD,pbi)
colnames(pbiDF) <- c("ID","rpbi")
return(pbiDF)
}
myAlpha <- function(data) {
alphaRes <- reliability(data, itemal = T)
if (length(alphaRes$N_person) == 0) {
n <- sprintf("%d",alphaRes$nPerson)
items <- sprintf("%d",alphaRes$nItem)
mean <- sprintf("%.2f",round(alphaRes$scaleMean,2))
sd <- sprintf("%.2f",round(alphaRes$scaleSD,2))
alpha <- substring(sprintf("%.3f",round(alphaRes$alpha,3)),2,5)
sem <- round(alphaRes$scaleSD * sqrt(1-alphaRes$alpha),3)
} else {
n <- sprintf("%d",alphaRes$N_person)
items <- sprintf("%d",alphaRes$N_item)
mean <- sprintf("%.2f",round(alphaRes$scale.mean,2))
sd <- sprintf("%.2f",round(alphaRes$scale.sd,2))
alpha <- substring(sprintf("%.3f",round(alphaRes$alpha,3)),2,5)
sem <- round(alphaRes$scale.sd * sqrt(1-alphaRes$alpha),3)
}
sumStats <- data.frame(Total=c(n,items,mean,sd,alpha,sem))
rownames(sumStats) <- c("N","Number of items","Mean","SD","Cronbach's alpha","SEM")
if (length(alphaRes$N_person) == 0) {
dropif <- round(ifelse(is.na(alphaRes$alphaIfDeleted),0,alphaRes$alphaIfDeleted),3)
r.drop <- round(ifelse(is.na(alphaRes$pBis), 0, alphaRes$pBis),3)
item.mean <- round(alphaRes$itemMean,3)
itemStats <- data.frame(dropif,r.drop,item.mean)
rownames(itemStats) <- colnames(data)
} else {
dropif <- round(ifelse(is.na(alphaRes$alpha.if.deleted),0,alphaRes$alpha.if.deleted),3)
r.drop <- round(ifelse(is.na(alphaRes$pbis), 0, alphaRes$pbis),3)
item.mean <- round(alphaRes$item.mean,3)
itemStats <- data.frame(dropif,r.drop,item.mean)
rownames(itemStats) <- attr(alphaRes$item.mean,"names")
}
colnames(itemStats) <- c("Drop if","r dropped","IF")
itemStats <- cbind(itemStats,brownRpbi(data,c()))
return(list(sumStats,itemStats))
}
myAlpha(dat)
})
output$cutscore.ds.out <- renderPrint({
cutscore.ds()
})
# Total score and % ==========================================================
totalper <- reactive({
dat <- read.csv(text=input$cutscore, sep="\t")
if (input$rownameCS == 1) {
x <- dat
x$total <- rowSums(dat[,-1], na.rm=T)
x$percent <- round(x$total/length(dat[,-1]),3)
sortDat <- x[order(-x[,length(x)]),]
res <- data.frame(sortDat[,1], sortDat$total, sortDat$percent*100)
colnames(res) <- c("ID", "Total score", "%")
res
} else {
x <- dat
x$total <- rowSums(dat, na.rm=T)
x$percent <- round(x$total/length(dat),3)
sortDat <- x[order(-x[,length(x)]),]
res <- data.frame(sortDat$total, sortDat$percent*100)
colnames(res) <- c("Total", "%")
res
}
})
output$totalper.out <- renderPrint({
totalper()
})
# Cut-score indiecs ==========================================================
cutscoreInd <- reactive({
dat <- read.csv(text=input$cutscore, sep="\t")
if (input$rownameCS == 1) {
x <- dat
x$total <- rowSums(dat[,-1], na.rm=T)
x$percent <- round(x$total/length(dat[,-1]),3)
sortDat <- x[order(-x[,length(x)]),]
TotalIF <- round(colMeans(dat[,-1], na.rm=T), 3)
# From the input of cut-score
CutScore <- input$CutPoint
cs <- CutScore * 0.01
# Dependability Indices
x.alpha <- alpha(dat[,-1], check.keys=F, na.rm=T)
KR20 <- x.alpha[[1]][,1] # KR20=Cronbach
n <- nrow(dat)
varp = function(x) { var(x) * (length(x)-1) / length(x) } #varではなくvarpを求める関数
s2 <- varp(x$percent)
M <- mean(x$percent)
k <- ncol(dat)-1
DependOrg <- ((n*s2)/(n-1))*KR20 / ((((n*s2)/(n-1))*KR20) + ((M*(1-M)-s2)/(k-1)))
Depend <- round(DependOrg, 3)
CIcrtOrg <- sqrt((M*(1-M)-s2)/(k-1))
CIcrt <- round((CIcrtOrg*100),3)
phicutOrg <- 1 - ((1/(k-1)) * ((M*(1-M) - s2)/((M-cs)^2+s2)))
phicut <- round(phicutOrg, 3)
# データフレームで項目ごとの指標算出
pass <- subset(x, percent >= cs) # cut-score以上の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
pass <- pass[setdiff(colnames(pass), delete)]
PassIF <- round(colMeans(pass[,-1], na.rm=T), 3) # 合格者のIF
fail <- subset(x, percent < cs) # cut-score未満の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
fail <- fail[setdiff(colnames(fail), delete)]
FailIF <- round(colMeans(fail[,-1], na.rm=T), 3) # 不合格のIF
# B-index
B <- PassIF - FailIF
# 一致指数（Agreement statistic）
# Pit = 正答者の中での正解者数/全受験者数
Pit <- colSums(pass[,-1])/nrow(dat)
# 誤答率Qi
Qi <- 1 - TotalIF
# Pt 合格者の割合
Pt <- nrow(pass)/nrow(dat)
# 一致指数（Agreement statistic）
A <- round((2 * Pit + Qi - Pt), 3)
# Φ (Phi)
# 正解者数率Pi（全体のIFと同じ）
Pi <- round(colMeans(dat[,-1], na.rm=T), 3) # 全体のIF
# 不正解者数率Qi
Qi <- 1 - Pi
# Pt 合格者の割合
Pt <- nrow(pass)/nrow(dat)
# 不合格者数率Qt
Qt <- 1 - Pt
# Pit = 正答者の中での正解者数/全受験者数
Pit <- colSums(pass[,-1])/nrow(dat)
# Φ
Φ <- round((Pit - Pi*Pt) / (sqrt(Pi*Qi*Pt*Qt)),3)
res <- data.frame(TotalIF, PassIF, FailIF, B, A, Φ)
cat(" Dependability (Φ) =", Depend, "\n",
"CRT CI (in %) =", CIcrt, "\n",
"Φ(λ) =", phicut, "\n",
"\n"
)
cat("=======================================================", "\n", "\n")
res
} else {
x <- dat
x$total <- rowSums(dat, na.rm=T)
x$percent <- round(x$total/length(dat),3)
sortDat <- x[order(-x[,length(x)]),]
TotalIF <- round(colMeans(dat, na.rm=T), 3)
# From the input of cut-score
CutScore <- input$CutPoint
cs <- CutScore * 0.01
# Dependability Indices
x.alpha <- alpha(dat[,-1], check.keys=F, na.rm=T)
KR20 <- x.alpha[[1]][,1] # KR20=Cronbach
n <- nrow(dat)
varp = function(x) { var(x) * (length(x)-1) / length(x) } #varではなくvarpを求める関数
s2 <- varp(x$percent)
M <- mean(x$percent)
k <- ncol(dat)-1
DependOrg <- ((n*s2)/(n-1))*KR20 / ((((n*s2)/(n-1))*KR20) + ((M*(1-M)-s2)/(k-1)))
Depend <- round(DependOrg, 3)
CIcrtOrg <- sqrt((M*(1-M)-s2)/(k-1))
CIcrt <- round((CIcrtOrg*100),3)
phicutOrg <- 1 - ((1/(k-1)) * ((M*(1-M) - s2)/((M-cs)^2+s2)))
phicut <- round(phicutOrg, 3)
# データフレームで項目ごとの指標算出
pass <- subset(x, percent >= cs) # cut-score以上の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
pass <- pass[setdiff(colnames(pass), delete)]
PassIF <- round(colMeans(pass, na.rm=T), 3) # 合格者のIF
fail <- subset(x, percent < cs) # cut-score未満の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
fail <- fail[setdiff(colnames(fail), delete)]
FailIF <- round(colMeans(fail, na.rm=T), 3) # 不合格のIF
# B-index
B <- PassIF - FailIF
# 一致指数（Agreement statistic）
# Pit = 正答者の中での正解者数/全受験者数
Pit <- colSums(pass)/nrow(dat)
# 誤答率Qi
Qi <- 1 - TotalIF
# Pt 合格者の割合
Pt <- nrow(pass)/nrow(dat)
# 一致指数（Agreement statistic）
A <- round((2 * Pit + Qi - Pt), 3)
# Φ (Phi)
# 正解者数率Pi（全体のIFと同じ）
Pi <- round(colMeans(dat, na.rm=T), 3) # 全体のIF
# 不正解者数率Qi
Qi <- 1 - Pi
# Pt 合格者の割合
Pt <- nrow(pass)/nrow(dat)
# 不合格者数率Qt
Qt <- 1 - Pt
# Pit = 正答者の中での正解者数/全受験者数
Pit <- colSums(pass)/nrow(dat)
# Φ
Φ <- round((Pit - Pi*Pt) / (sqrt(Pi*Qi*Pt*Qt)),3)
res <- data.frame(TotalIF, PassIF, FailIF, B, A, Φ)
cat(" Dependability (Φ) =", Depend, "\n",
"CRT CI (in %) =", CIcrt, "\n",
"Φ(λ) =", phicut, "\n",
"\n"
)
cat("=======================================================", "\n", "\n")
res
}
})
output$cutscoreInd.out <- renderPrint({
cutscoreInd()
})
# Histogram ==========================================================
makedistPlot2 <- function(){
dat <- read.csv(text=input$cutscore, sep="\t")
if (input$rowname == 1) {
x <- rowSums(dat[,-1], na.rm=T)
} else {
x <- rowSums(dat, na.rm=T)
}
simple.bincount <- function(x, breaks) {
nx <- length(x)
nbreaks <- length(breaks)
counts <- integer(nbreaks - 1)
for (i in 1:nx) {
lo <- 1
hi <- nbreaks
if (breaks[lo] <= x[i] && x[i] <= breaks[hi]) {
while (hi - lo >= 2) {
new <- (hi + lo) %/% 2
if(x[i] > breaks[new])
lo <- new
else
hi <- new
}
counts[lo] <- counts[lo] + 1
}
}
return(counts)
}
nclass <- nclass.FD(x)
breaks <- pretty(x, nclass)
counts <- simple.bincount(x, breaks)
counts.max <- max(counts)
h <- hist(x, las=1, breaks="FD", xlab= "Red vertical line shows the mean.",
ylim=c(0, counts.max*1.2), main="", col = "cyan")
rug(x)
abline(v = mean(x, na.rm=T), col = "red", lwd = 2)
xfit <- seq(min(x, na.rm=T), max(x, na.rm=T))
yfit <- dnorm(xfit, mean = mean(x, na.rm=T), sd = sd(x, na.rm=T))
yfit <- yfit * diff(h$mids[1:2]) * length(x)
lines(xfit, yfit, col = "blue", lwd = 2)
}
output$distPlot2 <- renderPlot({
print(makedistPlot2())
})
# Boxplot ==========================================================
makeboxPlot2 <- function(){
dat <- read.csv(text=input$cutscore, sep="\t")
if (input$rowname == 1) {
x <- rowSums(dat[,-1], na.rm=T)
} else {
x <- rowSums(dat, na.rm=T)
}
boxplot(x, horizontal=TRUE, xlab= "Mean and +/-1 SD are displayed in red.")
beeswarm(x, horizontal=TRUE, col = 4, pch = 16, add = TRUE)
points(mean(x, na.rm=T), 0.9, pch = 18, col = "red", cex = 2)
arrows(mean(x, na.rm=T), 0.9, mean(x, na.rm=T) + sd(x, na.rm=T), length = 0.1, angle = 45, col = "red")
arrows(mean(x, na.rm=T), 0.9, mean(x, na.rm=T) - sd(x, na.rm=T), length = 0.1, angle = 45, col = "red")
}
output$boxPlot2 <- renderPlot({
print(makeboxPlot2()) # 上の function を参照する指定
})
# Comparison of masters and non-masters ==========================================================
masternonmaster <- function(){
dat <- read.csv(text=input$cutscore, sep="\t")
if (input$rownameCS == 1) {
x <- dat
x$total <- rowSums(dat[,-1], na.rm=T)
x$percent <- round(x$total/length(dat[,-1]),3)
# From the input of cut-score
CutScore <- input$CutPoint
cs <- CutScore * 0.01
pass <- subset(x, percent >= cs) # cut-score以上の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
pass <- pass[setdiff(colnames(pass), delete)]
passTotal <- rowSums(pass[,-1], na.rm=T) # 合格者の合計点
fail <- subset(x, percent < cs) # cut-score未満の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
fail <- fail[setdiff(colnames(fail), delete)]
failTotal <- rowSums(fail[,-1], na.rm=T) # 不合格の合格点
} else {
x <- dat
x$total <- rowSums(dat, na.rm=T)
x$percent <- round(x$total/length(dat),3)
# From the input of cut-score
CutScore <- input$CutPoint
cs <- CutScore * 0.01
pass <- subset(x, percent >= cs) # cut-score以上の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
pass <- pass[setdiff(colnames(pass), delete)]
passTotal <- rowSums(pass, na.rm=T) # 合格者の合計点
fail <- subset(x, percent < cs) # cut-score未満の学習者のデータフレーム
delete <- c("total", "percent") # 削除する列の指定
fail <- fail[setdiff(colnames(fail), delete)]
failTotal <- rowSums(fail, na.rm=T) # 不合格の合格点
}
x <- passTotal
y <- failTotal
score <- c(x, y)
group <- factor(c(rep("Masters", length(x)), rep("Non-masters", length(y))))
boxplot(score ~ group, las=1, xlab= "Means and +/-1 SDs are displayed in red.")
beeswarm(score ~ group, col = 4, pch = 16, vert = TRUE, add = TRUE)
points(1.2, mean(x), pch = 18, col = "red", cex = 2)
arrows(1.2, mean(x), 1.2, mean(x) + sd(x), length = 0.1, angle = 45, col = "red")
arrows(1.2, mean(x), 1.2, mean(x) - sd(x), length = 0.1, angle = 45, col = "red")
points(2.2, mean(y), pch = 18, col = "red", cex = 2)
arrows(2.2, mean(y), 2.2, mean(y) + sd(y), length = 0.1, angle = 45, col = "red")
arrows(2.2, mean(y), 2.2, mean(y) - sd(y), length = 0.1, angle = 45, col = "red")
}
output$masternonmaster <- renderPlot({
print(masternonmaster())
})
# R Session Info (tabPanel "Difference Index") ==========================================================
info <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")# バージョン情報
info2 <- paste("It was executed on ", date(), ".", sep = "")# 実行日時
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$info.out <- renderPrint({
info()
})
# R Session Info (tabPanel "Cut-score Indices") ==========================================================
info22 <- reactive({
info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")# バージョン情報
info2 <- paste("It was executed on ", date(), ".", sep = "")# 実行日時
cat(sprintf(info1), "\n")
cat(sprintf(info2), "\n")
})
output$info22.out <- renderPrint({
info22()
})
})
})
library(shiny)
library(shinyAce)
shinyUI(bootstrapPage(
headerPanel("Item Analysis for Criterion-referenced Test"),
########## Adding loading message #########
tags$head(tags$style(type="text/css", "
#loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 10px 0px 10px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),
########## Added up untill here ##########
mainPanel(
tabsetPanel(
tabPanel("Cut-score Indices",
h2("Cut-score Indices"),
h4("Single administration of a test"),
p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),
p('If you need to create 1-0 data first, use',
a('Binary (1-0) Data Converter', href='https://langtest.shinyapps.io/biconv/', target="_blank"), '.'),
p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),
br(),
strong('Option:'),
checkboxInput("rownameCS", label = strong("The first columns of datasets contain case names."), value = T),
br(),
aceEditor("cutscore", 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0\t1\t1\t1\t0\t1\t0\t0\t0\t0\t1\t1\t1\t1\t0\t0\t1\t0\t1\nStudent115\t0\t1\t0\t1\t1\t0\t1\t1\t1\t1\t1\t1\t1\t1\t1\t1\t0\t1\t1\t1\t1\t0\t1\t0\t0\nStudent116\t1\t1\t0\t0\t0\t1\t0\t1\t1\t1\t0\t1\t0\t0\t1\t1\t0\t1\t1\t1\t0\t0\t0\t1\t0\nStudent117\t1\t0\t0\t1\t1\t0\t1\t0\t1\t1\t1\t1\t1\t0\t1\t1\t1\t1\t1\t0\t0\t1\t1\t1\t0\nStudent118\t0\t1\t0\t1\t1\t0\t0\t1\t0\t1\t0\t0\t1\t0\t1\t1\t0\t1\t0\t1\t0\t0\t1\t1\t0\nStudent119\t1\t0\t0\t1\t0\t0\t1\t1\t0\t0\t0\t1\t0\t0\t0\t1\t0\t1\t0\t1\t0\t0\t0\t1\t1\nStudent120\t0\t1\t0\t1\t0\t1\t0\t1\t1\t0\t0\t1\t1\t1\t1\t1\t1\t1\t1\t1\t0\t0\t1\t0\t0\nStudent121\t0\t1\t1\t0\t1\t1\t1\t0\t0\t0\t1\t1\t1\t1\t0\t1\t1\t1\t1\t1\t1\t0\t1\t0\t0\nStudent122\t0\t1\t0\t1\t1\t1\t1\t1\t0\t0\t1\t1\t1\t0\t0\t0\t0\t1\t0\t0\t0\t0\t1\t0\t0\nStudent123\t0\t1\t0\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\t0\t0\t0\t0\t1\t1\t0\t0\t0\t1\t1\t1\nStudent124\t1\t1\t1\t1\t1\t0\t1\t1\t1\t0\t1\t1\t0\t1\t1\t1\t1\t1\t1\t1\t0\t1\t0\t1\t1\nStudent125\t1\t1\t1\t0\t1\t0\t1\t0\t1\t1\t1\t0\t0\t0\t0\t1\t1\t1\t1\t1\t1\t0\t1\t1\t0\nStudent126\t1\t1\t0\t0\t0\t1\t0\t1\t1\t0\t1\t1\t1\t0\t0\t0\t0\t1\t1\t1\t0\t0\t0\t1\t0\nStudent127\t1\t1\t1\t0\t0\t1\t1\t1\t0\t1\t0\t1\t1\t1\t0\t1\t0\t1\t0\t0\t0\t0\t1\t0\t0\nStudent128\t1\t1\t0\t0\t1\t1\t1\t1\t1\t1\t0\t1\t0\t0\t0\t1\t0\t1\t1\t1\t0\t0\t0\t1\t0\nStudent129\t0\t0\t0\t1\t0\t0\t0\t0\t0\t0\t0\t1\t1\t1\t1\t1\t0\t1\t0\t0\t0\t0\t0\t0\t0\nStudent130\t0\t1\t0\t0\t1\t1\t0\t1\t1\t0\t1\t0\t0\t1\t1\t1\t0\t1\t1\t1\t0\t0\t1\t0\t1\nStudent131\t0\t0\t0\t1\t1\t0\t1\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t1", mode="r", theme="cobalt"),
br(),
br(),
h3("Basic statistics and item statistics"),
verbatimTextOutput("cutscore.ds.out"),
p('Drop if: Cronbach alpha when the item is removed', br(),
'r dropped: item-total correlation without the item', br(),
'IF: item facility or item mean (proportion of those who answered the item correctly)', br(),
'ID: item discrimination (upper 1/3 - lower 1/3)', br(),
'rpbi: point-biserial correlation', br()
),
br(),
h3("Histogram"),
plotOutput("distPlot2"),
h3("Box plot with individual data points"),
plotOutput("boxPlot2"),
br(),
h3("Total score and percentage (descending order)"),
verbatimTextOutput("totalper.out"),
br(),
h3("Cut-score Indices"),
numericInput("CutPoint", "Set the cut-score (in %):", 60),
verbatimTextOutput("cutscoreInd.out"),
p('Total IF: item facility or item mean of all test-takers', br(),
'Pass IF: item facility of masters (over the cut-score)', br(),
'Fail IF: item facility of non-masters (under the cut-score)', br(),
'B: B-index (Pass IF — Fail IF)', br(),
'A: Agreement statistic', a('(Brown & Hudson, 2002, p. 125)', href='http://bit.ly/1u76Txf', target="_blank"), br(),
'Φ: Item phi', a('(Brown & Hudson, 2002, p. 126)', href='http://bit.ly/1u76Txf', target="_blank"), br()
),
br(),
h3("Comparison of masters and non-masters"),
plotOutput("masternonmaster", height = "550px"),
br(),
br(),
strong('R session info'),
verbatimTextOutput("info22.out")
),
tabPanel("Difference Index",
h2("Difference Index"),
h4("Two administrations of the test"),
p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),
p('If you need to create 1-0 data first, use',
a('Binary (1-0) Data Converter', href='https://langtest.shinyapps.io/biconv/', target="_blank"), '.'),
p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),
br(),
strong('Option:'),
checkboxInput("rowname", label = strong("The first columns of datasets contain case names."), value = T),
h4("Pretest"),
aceEditor("pretest", 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\t0\t0\t1\t1\nStudent153\t1\t1\t1\t1\t1\t1\t0\t1\t1\t1\t1\t0\t0\t0\t1\t1\t1\t1\t1\t1\t1\t0\t1\t1\t1\t0\t1\t1\t1\t0\t1\t0\t0\t1\t0\t1\t0\t1\t0\t1\nStudent154\t0\t0\t1\t1\t1\t0\t1\t1\t1\t1\t1\t1\t1\t0\t0\t1\t1\t1\t1\t1\t0\t0\t1\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\t0\t0\t1\t0\t0\nStudent155\t0\t1\t1\t1\t1\t1\t0\t1\t1\t1\t0\t1\t1\t0\t0\t0\t1\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t1\t1\t0\t0\t0\t0\t1\t1\t0\t0\t1\t0\t0\nStudent156\t1\t1\t1\t1\t1\t1\t0\t1\t1\t1\t1\t1\t0\t0\t0\t1\t1\t1\t1\t1\t1\t1\t1\t1\t1\t1\t1\t0\t0\t1\t1\t0\t1\t1\t1\t1\t0\t1\t1\t1\nStudent157\t1\t1\t1\t1\t1\t1\t1\t1\t0\t1\t1\t1\t0\t0\t0\t0\t1\t0\t1\t1\t0\t1\t0\t1\t1\t0\t1\t1\t1\t1\t0\t0\t1\t1\t1\t1\t1\t1\t0\t1", mode="r", theme="twilight"),
br(),
br(),
h3("Basic statistics and item statistics"),
verbatimTextOutput("prepost.ds.out"),
p('Drop if: Cronbach alpha when the item is removed', br(),
'r dropped: item-total correlation without the item', br(),
'IF: item facility or item mean (proportion of those who answered the item correctly)', br(),
'ID: item discrimination (upper 1/3 - lower 1/3)', br(),
'rpbi: point-biserial correlation', br()
),
br(),
h3("Difference index (DI)"),
verbatimTextOutput("DI.out"),
p('IF = Item facility (proportion of those who answered the item correctly)', br(),
'DI = Difference index (Posttest IF - Pretest IF)', br(),
'IF DI is > .10, the item is appropriate for criterion-referenced test.'
),
br(),
h3("Change (gain) score"),
verbatimTextOutput("gain.out"),
p('Gain: Post — Pre', br(),
'Exp.Post: Expected posttest score = M.post + r * (SD.post/SD.pre) * (X - M.pre)', br(),
'±RTM: Post — Exp.Post (If the value is positive, the gain is over regression to the mean (RTM) effect.)', br(),
'Adj.Post: Asjusted (corrected) posttest score', a('(Bonate, 2000)', href='http://bit.ly/1m3n37a', target="_blank"), br()
),
br(),
h3("Gain score reliability"),
verbatimTextOutput("gain.rel.out"),
p('If these values are low, you should not use the gain score as an indicator for subsequent analysis.'),
br(),
br(),
br(),
h3("Overlayed histograms"),
plotOutput("distPlot"),
br(),
h3("Box plots with individual data points"),
plotOutput("boxPlot", width="80%"),
br(),
h3("Changes of the individual data"),
plotOutput("indvPlot", width="70%"),
br(),
h3("Scatterplot"),
plotOutput("correlPlot", width="70%", height="500px"),
br(),
h3("Paired t-test"),
verbatimTextOutput("t.out"),
br(),
h3("Effect size indices"),
verbatimTextOutput("es.out"),
p(a('(Borenstein et al., 2009)', href='http://www.meta-analysis.com/downloads/Meta-analysis%20Effect%20sizes%20based%20on%20means.pdf', target="_blank"), br()),
br(),
br(),
strong('R session info'),
verbatimTextOutput("info.out")
),
tabPanel("About",
strong('Note'),
p('This web application is developed with',
a("Shiny.", href="http://www.rstudio.com/shiny/", target="_blank"),
''),
br(),
strong('List of Packages Used'), br(),
code('library(shiny)'),br(),
code('library(shinyAce)'),br(),
code('library(psych)'),br(),
code('library(CTT)'),br(),
code('library(lattice)'),br(),
code('library(latticeExtra)'),br(),
code('library(beeswarm)'),br(),
br(),
strong('Code'),
p('Source code for this application is based on',
a('"The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012)', href='http://mizumot.com/handbook/', target="_blank"), 'and',
a("MacR.", href="https://sites.google.com/site/casualmacr/", target="_blank")),
p('The code for this web application is available at',
a('GitHub.', href='https://github.com/mizumot/criterion', target="_blank")),
p('If you want to run this code on your computer (in a local R session), run the code below:',
br(),
code('library(shiny)'),br(),
code('runGitHub("criterion","mizumot")')
),
br(),
strong('Acknowledgment'),
p('I thank',
a("Dr. Takaaki Kumazawa", href="https://twitter.com/TakaakiKumazawa", target="_blank"),
'for his support and feedback to create this web application.'),
br(),
strong('Citation in Publications'),
p('Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp'),
br(),
strong('Article'),
p('Mizumoto, A., & Plonsky, L. (2015).', a("R as a lingua franca: Advantages of using R for quantitative research in applied linguistics.", href='http://applij.oxfordjournals.org/content/early/2015/06/24/applin.amv025.abstract', target="_blank"), em('Applied Linguistics,'), 'Advance online publication. doi:10.1093/applin/amv025'),
br(),
strong('Recommended'),
p('To learn more about R, I suggest this excellent and free e-book (pdf),',
a("A Guide to Doing Statistics in Second Language Research Using R,", href="http://cw.routledge.com/textbooks/9780805861853/guide-to-R.asp", target="_blank"),
'written by Dr. Jenifer Larson-Hall.'),
p('Also, if you are a cool Mac user and want to use R with GUI,',
a("MacR", href="https://sites.google.com/site/casualmacr/", target="_blank"),
'is defenitely the way to go!'),
br(),
strong('Author'),
p(a("Atsushi MIZUMOTO,", href="http://mizumot.com", target="_blank"),' Ph.D.',br(),
'Professor of Applied Linguistics',br(),
'Faculty of Foreign Language Studies /',br(),
'Graduate School of Foreign Language Education and Research,',br(),
'Kansai University, Osaka, Japan'),
br(),
a(img(src="http://i.creativecommons.org/p/mark/1.0/80x15.png"), target="_blank", href="http://creativecommons.org/publicdomain/mark/1.0/"),
p(br())
)
)
)
))
