R/itemanalysis2.R

itemanalysis2 <- function (data, options,ngroup=ncol(data)+1,correction=TRUE, span.par=.3,verbose=T) 
{
  
  #########################################################################################
  # data, a data frame with N rows and n columns, where N denotes the number of subjects 
  #        and n denotes the number of items. All items should be scored using nominal/ordinal 
  #        response categories. All variables (columns) must be "numeric". 
  
  # options, numbers representing the response categories (e.g.,0,1,2,3)
  #          make sure each item is consistent, and includes the same response options
  # Recommend that the numerical codes are recoded such that the minimum score is 0
  
  # ngroup, number of score groups
  #
  # correction, TRUE or FALSE, if TRUE item and distractor discrimination is corrected for
  # 		  spuriousnes by removing the item score from the total score
  #########################################################################################
  
  
  total.score <- rowMeans(data,na.rm=TRUE)*ncol(data)
  
  pbis <- c()
  pbis.corrected <- c()
  bis  <- c()
  bis.corrected <- c()
  
  for(k in 1:ncol(data)) { 
    pbis[k]=cor(data[,k],total.score,use="pairwise.complete.obs")
    pbis.corrected[k]=cor(data[,k],
                          rowMeans(data[,-k],na.rm=TRUE)*(ncol(data)-1),
                          use="pairwise.complete.obs")
    bis[k]=polyserial(total.score,data[,k])
    bis.corrected[k]=polyserial(rowMeans(data[,-k],na.rm=TRUE)*(ncol(data)-1),data[,k])
  }
  
  
  item.stat <- matrix(nrow=ncol(data),ncol=4)
  colnames(item.stat) <- c("Mean Score","Item Difficulty","Point-Biserial","Polyserial")
  
  rnames <- ("Item 1")
  for(i in 2:ncol(data)){ rnames <- c(rnames,paste("Item ",i,sep=""))}
  rownames(item.stat) <- rnames	
  item.stat[,1]=colMeans(data,na.rm=TRUE)
  item.stat[,2]=colMeans(data,na.rm=TRUE)/max(options)
  if(correction==TRUE){ item.stat[,3]=pbis.corrected } else { item.stat[,3]=pbis }
  if(correction==TRUE){ item.stat[,4]=bis.corrected } else { item.stat[,4]=bis }
  
  
  sgroups <- cut(total.score,breaks=ngroup)
  slevels <- levels(sgroups)
  
  sgnum <- rowMeans(cbind(lower = as.numeric( sub("\\((.+),.*", "\\1", slevels) ),
                          upper = as.numeric( sub("[^,]*,([^]]*)\\]","\\1",slevels))))
  
  
  
  SG <- vector("list",ngroup)
  
  for(j in 1:ngroup){
    SG[[j]]=which(sgroups==slevels[j])
  }
  
  prop <- vector("list",ncol(data))
  names(prop) <- rnames
  
  for(i in 1:ncol(data)) {
    
    dist <- matrix(nrow=length(options),ncol=ngroup)
    colnames(dist) <- slevels
    rownames(dist) <- options 
    
    for(g in 1:ngroup){
      for(o in 1:length(options)){
        dist[o,g]=length(which(data[SG[[g]],i]==options[o]))/length(SG[[g]])
      }
    }
    
    prop[[i]]=dist
    
  }
  
  dist.sel <- matrix(nrow=ncol(data),ncol=length(options))  
  dist.disc <- matrix(nrow=ncol(data),ncol=length(options))
  dist.disc2 <- matrix(nrow=ncol(data),ncol=length(options))
  colnames(dist.disc) <- options
  rownames(dist.disc) <- rnames
  colnames(dist.disc2) <- options
  rownames(dist.disc2) <- rnames
  colnames(dist.sel) <- options
  rownames(dist.sel) <- rnames
  
  for(i in 1:ncol(data)){
    for(o in 1:length(options)) {      
      temp <- ifelse(data[,i]==options[o],1,0)
      temp[is.na(temp)]=0
      dist.sel[i,o]=mean(temp,na.rm=TRUE)
      if(correction==FALSE){
        dist.disc[i,o]=cor(temp,total.score,use="pairwise.complete.obs")
        dist.disc2[i,o]=polyserial(total.score,temp)
      } else {
        dist.disc[i,o]=cor(temp,rowMeans(data[,-i],na.rm=TRUE)*(ncol(data)-1),use="pairwise.complete.obs")
        dist.disc2[i,o]=polyserial(rowMeans(data[,-i],na.rm=TRUE)*(ncol(data)-1),temp)
      }
    }
    
  }
  
  
  plots <- vector("list",ncol(data))
  
  for(i in 1:ncol(data)) {
    
    options.d <- c()
    for(u in 1:length(options)){ 
      if(correction==TRUE){
        options.d[u] <- paste(options[u],"( ",round(dist.disc2[i,u],2)," )",sep="")
      } else { options.d[u] <- paste(options[u],"( ",round(dist.disc[i,u],2)," )",sep="") }
    }
    
    d <- as.data.frame(cbind(sg=sgnum,p=prop[[i]][1,]))
    for(u in 2:length(options)){ d <- rbind(d,cbind(sg=sgnum,p=prop[[i]][u,]))}
    optt <- c()
    for(u in 1:length(options)){ optt <- c(optt,rep(options.d[u],ngroup))}
    d$opt <- optt
    
    
    pp <- ggplot(data=d,aes_string(x="sg",y="p",group="opt",shape="opt"))+
      geom_line()+
      geom_point(size=3)+
      ggtitle(paste("Item ",i,sep=""))+
      theme(panel.background = element_blank(),legend.title=element_blank(),legend.key = element_blank())+
      scale_x_continuous(limits = c(0,ncol(data)*max(options)),breaks=seq(0,ncol(data)*max(options),ceiling(ncol(data)/10)))+
      scale_y_continuous(limits = c(0,1))+xlab("Score Groups")+ylab("Proporion of Being Selected")
    #  theme(legend.justification=c(0,1),legend.position=c(0,1),legend.text=element_text(size=12,face="bold"))
    
    plots[[i]] <- pp
  }
  
  ###############################################################
  if (verbose == T){
    
    cat("************************************************************************","\n")
    cat("itemanalysis: An R package for Classical Test Theory Item Analysis","\n")
    cat("","\n")
    cat("Cengiz Zopluoglu","\n")
    cat("","\n")
    cat("University of Oregon","\n")
    cat("College of Education","\n")
    cat("","\n")
    cat("cen.zop@gmail.com","\n")
    cat("","\n")
    cat("Please report any programming bug or problem you experience to improve the code.","\n")
    cat("*************************************************************************","\n")
    
    cat("Processing Date: ",date(),"\n")
    
    cat(sprintf("%50s","ITEM STATISTICS"),"\n")
    cat("","\n")
    print(round(item.stat,3))
    cat("","\n")
    cat("    * Item difficulty is the ratio of mean score to possible maximum score","\n")
    cat("      and assumes the minimum score is 0","\n")
    cat("","\n")
    cat("","\n")
    
    cat(sprintf("%50s","RESPONSE CATEGORY SELECTION PROPORTIONS"),"\n")
    cat("","\n")
    print(round(dist.sel,3))
    cat("","\n")
    cat("","\n")
    cat("","\n")
    
    cat(sprintf("%50s","RESPONSE CATEGORY Point-Biserial Correlation"),"\n")
    cat("","\n")
    print(round(dist.disc,3))
    cat("","\n")
    cat("","\n")
    
    cat(sprintf("%50s","RESPONSE CATEGORY Biserial Correlation"),"\n")
    cat("","\n")
    print(round(dist.disc2,3))
    cat("","\n")
    cat("","\n")
    cat("","\n")
  } else {
    
  }
  return(list(item.stat=item.stat,
              dist.sel=dist.sel,
              dist.disc=dist.disc,
              dist.disc2=dist.disc2,
              plots=plots))
}

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itemanalysis documentation built on June 14, 2022, 1:06 a.m.