Cronbach's Coefficient Alpha

Main
About

Option:

The input data includes variable names (header).

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

Basic statistics of the scale (test)

Cronbach's coefficient alpha

KR (Kuder–Richardson) 20 & 21

Calculate KR21 from k, M, and SD.

k = the number of items, M = mean, and SD = standard deviation

Histogram

Average

Total

Box plot with individual data points

Average

Total

Test of normality

Q-Q plot

R session info

Note

This web application is developed with Shiny.

List of Packages Used
library(shiny)
library(shinyAce)
library(psych)
library(beeswarm)

Code

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

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("rel","mizumot")

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

show with app

server.R
ui.R

library(shiny)
library(shinyAce)
library(psych)
library(beeswarm)



shinyServer(function(input, output) {


    options(warn=-1)
    
    
    bs <- reactive({
        if (input$colname == 0) {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            total <- rowSums(x, na.rm=T)
            result1 <- describe(total)[2:13]

            y <- rowMeans(x, na.rm=T)
            result2 <- describe(y)[2:13]
        
            row.names(result1) <- "Total   "
            row.names(result2) <- "Average "
            return(list(result2, result1))
        
        } else {
            
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            total <- rowSums(x, na.rm=T)
            result1 <- describe(total)[2:13]
            
            y <- rowMeans(x, na.rm=T)
            result2 <- describe(y)[2:13]
            
            row.names(result1) <- "Total   "
            row.names(result2) <- "Average "
            return(list(result2, result1))
        
        }
    })
    
    
    alpha.result <- reactive({
        if (input$colname == 0) {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            print(alpha(x, check.keys=F, na.rm=T),3)
        
        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            print(alpha(x, check.keys=F, na.rm=T),3)

        }
    })
    
    
    kr.result <- reactive({
        if (input$colname == 0) {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            bicheck <- apply(x,2,function(x) { all(na.omit(x) %in% 0:1) })
            k <- ncol(x)
            varp <- function(x) {
              v <- var(x) * (length(x)-1) / length(x)
              v
            }
            SX <- varp(rowSums(x))
            IM <- colMeans(x)
            KR20 <- ((k/(k - 1))*((SX - sum(IM*(1 - IM)))/SX))
            KR21 <- (k/(k-1))*((varp(rowSums(x)) - k*(sum(colMeans(x))/k) *
                          (1-(sum(colMeans(x))/k))))/varp(rowSums(x))
            
        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            bicheck <- apply(x,2,function(x) { all(na.omit(x) %in% 0:1) })
            k <- ncol(x)
            varp <- function(x) {
              v <- var(x) * (length(x)-1) / length(x)
              v
            }
            SX <- varp(rowSums(x))
            IM <- colMeans(x)
            KR20 <- ((k/(k - 1))*((SX - sum(IM*(1 - IM)))/SX))
            KR21 <- (k/(k-1))*((varp(rowSums(x)) - k*(sum(colMeans(x))/k) *
                          (1-(sum(colMeans(x))/k))))/varp(rowSums(x))
        }
        
        if (all(bicheck) == TRUE){
          cat(" KR20 =", round(KR20, 3), "\n", "KR21 =", round(KR21, 3), "\n")
        } else {
          cat("Kuder–Richardson Formula 20 (KR-20) and 21 (KR-21) will be displayed","\n", "if the input data is binary (0/1).")
        }

                
    })


    kr21.result <- reactive({
        iNo <- input$k
        M <- input$M
        SD <- input$SD
        KR21 <- round(iNo/(iNo-1)*(1-(M*(iNo-M)/(iNo*SD^2))),3)
        cat("KR21 =", round(KR21, 3), "\n")
                
    })
    
    
    
    makedistPlot <- function(){
        if (input$colname == 0) {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            
            if (input$meantotal1 == "mean1") {
                x <- rowMeans(x, na.rm=T)
            } else {
                x <- rowSums(x, na.rm=T)
            }
            
        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            
            if (input$meantotal1 == "mean1") {
                x <- rowMeans(x, na.rm=T)
            } else {
                x <- rowSums(x, 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, na.rm= T, 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$distPlot <- renderPlot({
        print(makedistPlot())
    })
    
    
    
    
    
    makeboxPlot <- function(){
        if (input$colname == 0) {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            
            if (input$meantotal2 == "mean2") {
                x <- rowMeans(x, na.rm=T)
            } else {
                x <- rowSums(x, na.rm=T)
            }
            
        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            
            if (input$meantotal2 == "mean2") {
                x <- rowMeans(x, na.rm=T)
            } else {
                x <- rowSums(x, 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$boxPlot <- renderPlot({
        print(makeboxPlot())
    })
    
    
    
    
    
    testnorm <- reactive({
        if (input$colname == 0) {

            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            x <- rowMeans(x, na.rm=T)

        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            x <- rowMeans(x, na.rm=T)
        }

        list(ks.test(scale(x), "pnorm"), shapiro.test(x))
    })
    
    
    
    
    
    
    makeqqPlot <- function(){
        if (input$colname == 0) {
            
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."), header=F)
            x <- as.matrix(x)
            x <- rowMeans(x, na.rm=T)
            
        } else {
            x <- read.csv(text=input$text, sep="", na.strings=c("","NA","."))
            x <- rowMeans(x, na.rm=T)
        }
        
        qqnorm(x, las=1)
        qqline(x, col=2)
    }
    
    output$qqPlot <- renderPlot({
        print(makeqqPlot())
    })





    info <- reactive({
        info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
        info2 <- paste("It was executed on ", date(), ".", sep = "")
        cat(sprintf(info1), "\n")
        cat(sprintf(info2), "\n")
    })





    output$textarea.out <- renderPrint({
        bs()
    })
    
    output$alpha.result.out <- renderPrint({
        alpha.result()
    })

    output$kr.result.out <- renderPrint({
        kr.result()
    })
    
    output$kr21.result.out <- renderPrint({
        kr21.result()
    })
        
    output$testnorm.out <- renderPrint({
        testnorm()
    })
    
    output$info.out <- renderPrint({
        info()
    })

})

library(shiny)
library(shinyAce)



shinyUI(bootstrapPage(

    headerPanel("Cronbach's Coefficient Alpha"),

    mainPanel(
        tabsetPanel(

        tabPanel("Main",

            strong('Option:'),

            checkboxInput("colname", label = strong("The input data includes variable names (header)."), value = T),

            br(),

            p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),

            aceEditor("text", value="Item1\tItem2\tItem3\tItem4\n2\t3\t3\t3\n3\t3\t4\t4\n4\t4\t3\t4\n5\t4\t3\t4\n3\t4\t2\t4\n3\t3\t4\t3\n4\t3\t4\t4\n3\t3\t2\t2\n4\t5\t5\t5\n2\t2\t1\t2\n4\t3\t4\t3\n3\t4\t3\t3\n3\t4\t4\t3\n3\t4\t3\t4\n5\t5\t5\t4",
                mode="r", theme="cobalt"),

            br(),

            h3("Basic statistics of the scale (test)"),
            verbatimTextOutput("textarea.out"),

            br(),

            h3("Cronbach's coefficient alpha"),
            verbatimTextOutput("alpha.result.out"),

            br(),

            h3("KR (Kuder–Richardson) 20 & 21"),
            verbatimTextOutput("kr.result.out"),
            
            checkboxInput("msdnKR21", label = ("Calculate KR21 from k, M, and SD."), value = F),

                conditionalPanel(
                    condition = "input.msdnKR21==1",
                        p("k = the number of items, M = mean, and SD = standard deviation"),
                        fluidRow(
                            column(4, numericInput(inputId = "k",
                                     label = "k",
                                     value = 20,
                                     width = '100%')),
                            column(4, numericInput(inputId = "M",
                                     label = "M",
                                     value = 9.48,
                                     width = '100%')),
                            column(4, numericInput(inputId = "SD",
                                     label = "SD",
                                     value = 4.52,
                                     width = '100%'))),
                        verbatimTextOutput("kr21.result.out")


                ),
            
            br(),

            h3("Histogram"),
            radioButtons("meantotal1", "",
                list("Average" = "mean1",
                     "Total" = "total1"), selected = "mean1"),
            plotOutput("distPlot"),

            br(),

            h3("Box plot with individual data points"),

            radioButtons("meantotal2", "",
                list("Average" = "mean2",
                     "Total" = "total2"), selected = "mean2"),
            plotOutput("boxPlot"),

            br(),

            h3("Test of normality"),
            verbatimTextOutput("testnorm.out"),

            br(),

            h3("Q-Q plot"),
            plotOutput("qqPlot", width="70%"),

            br(),
            br(),

            strong('R session info'),
            verbatimTextOutput("info.out")

            ),


        tabPanel("About",

            strong('Note'),
            p('This web application is developed with',
            a("Shiny.", href="http://shiny.rstudio.com/", target="_blank"),
            ''),

            br(),

            strong('List of Packages Used'), br(),
            code('library(shiny)'),br(),
            code('library(shinyAce)'),br(),
            code('library(psych)'),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")),

            p('The code for this web application is available at',
            a('GitHub.', href='https://github.com/mizumot/rel', 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("rel","mizumot")')
            ),

            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())

            )

))
))