Item Analysis for Criterion-referenced Test

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:

The first columns of datasets contain case names.

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

Set the cut-score (in %):

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:

The first columns of datasets contain case names.

Pretest

Posttest

Basic statistics and item statistics

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

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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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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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()) ) ) ) ))