Item Analysis for Criterion-referenced Test

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Cut-score Indices

Single administration of a test

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

If you need to create 1-0 data first, use Binary (1-0) Data Converter .

Your data needs to have the header (variable names) in the first row.


Option:


                
                

Basic statistics and item statistics


                

Drop if: Cronbach alpha when the item is removed
r dropped: item-total correlation without the item
IF: item facility or item mean (proportion of those who answered the item correctly)
ID: item discrimination (upper 1/3 - lower 1/3)
rpbi: point-biserial correlation


Histogram

Box plot with individual data points


Total score and percentage (descending order)


                

Cut-score Indices


                

Total IF: item facility or item mean of all test-takers
Pass IF: item facility of masters (over the cut-score)
Fail IF: item facility of non-masters (under the cut-score)
B: B-index (Pass IF — Fail IF)
A: Agreement statistic (Brown & Hudson, 2002, p. 125)
Φ: Item phi (Brown & Hudson, 2002, p. 126)


Comparison of masters and non-masters



R session info

              

Difference Index

Two administrations of the test

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

If you need to create 1-0 data first, use Binary (1-0) Data Converter .

Your data needs to have the header (variable names) in the first row.


Option:

Pretest


                
                

Posttest


                
                

Basic statistics and item statistics


                

Drop if: Cronbach alpha when the item is removed
r dropped: item-total correlation without the item
IF: item facility or item mean (proportion of those who answered the item correctly)
ID: item discrimination (upper 1/3 - lower 1/3)
rpbi: point-biserial correlation


Difference index (DI)


                

IF = Item facility (proportion of those who answered the item correctly)
DI = Difference index (Posttest IF - Pretest IF)
IF DI is > .10, the item is appropriate for criterion-referenced test.


Change (gain) score


                

Gain: Post — Pre
Exp.Post: Expected posttest score = M.post + r * (SD.post/SD.pre) * (X - M.pre)
±RTM: Post — Exp.Post (If the value is positive, the gain is over regression to the mean (RTM) effect.)
Adj.Post: Asjusted (corrected) posttest score (Bonate, 2000)


Gain score reliability


                

If these values are low, you should not use the gain score as an indicator for subsequent analysis.




Overlayed histograms


Box plots with individual data points


Changes of the individual data


Scatterplot


Paired t-test


                

Effect size indices


                

(Borenstein et al., 2009)



R session info

              
Note

This web application is developed with Shiny.


List of Packages Used
library(shiny)
library(shinyAce)
library(psych)
library(CTT)
library(lattice)
library(latticeExtra)
library(beeswarm)

Code

Source code for this application is based on "The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012) and MacR.

The code for this web application is available at GitHub.

If you want to run this code on your computer (in a local R session), run the code below:
library(shiny)
runGitHub("criterion","mizumot")


Acknowledgment

I thank Dr. Takaaki Kumazawa for his support and feedback to create this web application.


Citation in Publications

Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp


Article

Mizumoto, A., & Plonsky, L. (2015). R as a lingua franca: Advantages of using R for quantitative research in applied linguistics. Applied Linguistics, Advance online publication. doi:10.1093/applin/amv025


Recommended

To learn more about R, I suggest this excellent and free e-book (pdf), A Guide to Doing Statistics in Second Language Research Using R, written by Dr. Jenifer Larson-Hall.

Also, if you are a cool Mac user and want to use R with GUI, MacR is defenitely the way to go!


Author

Atsushi MIZUMOTO, Ph.D.
Associate Professor of Applied Linguistics
Faculty of Foreign Language Studies /
Graduate School of Foreign Language Education and Research,
Kansai University, Osaka, Japan



Code for "Item Analysis for Criterion-referenced Test"
by Atsushi Mizumoto

show with app
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(),
            'Associate 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())

            )
        )
    )

))
Code license: GPL-3