R/itemanalysis1.R

itemanalysis1 <- function (data, key, options,ngroup=ncol(data)+1,correction=TRUE) 
{

      #########################################################################################
	# 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 
      #        response categories. All variables (columns) must be "character". 
      #        Missing values ("NA") are allowed and scored as incorrect in item analysis
            
	# key,     a character vector of length n, where n denotes the number of items. 

      # options, number of possible nominal options for items (e.g., "A","B","C","D")
	#          make sure each item is consistent, and includes the same response options

      # 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
      #########################################################################################


    for (i in 1:ncol(data)) {
        if (is.character(data[, i]) != TRUE) {
            data[,i]=as.character(data[,i])
        }
    }
	
    scored.data <- as.data.frame(matrix(nrow = nrow(data), ncol = ncol(data)))

    for (i in 1:ncol(scored.data)) {
        scored.data[,i] <- ifelse(data[,i] == key[i],1,0)
	  if(length(which(is.na(scored.data[,i])))!=0) {
   	     scored.data[which(is.na(scored.data[,i])==TRUE),i]=0
	  }
    }

    total.score <- rowSums(scored.data)
	  ybar <- mean(total.score)
  	sdt <- sd(total.score)
	  p <- colMeans(scored.data)
  
    pbis <- c()
    pbis.corrected <- c()
    bis  <- c()
    bis.corrected <- c()

	for(k in 1:ncol(data)) {  
		pbis[k]=cor(scored.data[,k],total.score,use="pairwise.complete.obs")
		pbis.corrected[k]=cor(scored.data[,k],
		                      rowMeans(scored.data[,-k],na.rm=TRUE)*(ncol(scored.data)-1),
		                      use="pairwise.complete.obs")
		bis[k]=polyserial(total.score,scored.data[,k])
		bis.corrected[k]=polyserial(rowMeans(scored.data[,-k],na.rm=TRUE)*(ncol(scored.data)-1),scored.data[,k])
	}


    item.stat <- matrix(nrow=ncol(data),ncol=4)
	  colnames(item.stat) <- c("Item Difficulty","Item Threshold","Point-Biserial","Biserial")

    rnames <- ("Item 1")
    for(i in 2:ncol(data)){ rnames <- c(rnames,paste("Item ",i,sep=""))}
    rownames(item.stat) <- rnames	
    item.stat[,1]=p
    item.stat[,2]=qnorm(1-p)
    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}


#    eff.size           <- matrix(nrow=ncol(data),ncol=4)
#    colnames(eff.size) <- c("Incorrect","Correct","p","d")
#    for(i in 1:ncol(data)){
#      gr1 = rowSums(scored.data[scored.data[,i]==0,])
#      gr2 = rowSums(scored.data[scored.data[,i]==1,])
#      t = t.test(gr1,gr2)
#      eff.size[i,1] = mean(gr1)
#      eff.size[i,2] = mean(gr2)
#      eff.size[i,3] = t$p.value
#      eff.size[i,4] =  abs(cohen.d(gr1,gr2)$estimate)
#    }
    
#    eff.size <- as.data.frame(eff.size)
#    
#    eff.size$p2 <- ifelse(eff.size$p<.001,"p<.001",
#                          ifelse(eff.size$p>.001 & eff.size$p <.01,"p<.01",
#                                 ifelse(eff.size$p > .01 & eff.size$p <.05,"p<.05","Not significant")))
#    
#    eff.size <- eff.size[,c(1,2,5,4)]
#    eff.size[,1]=round(eff.size[,1],2)
#    eff.size[,2]=round(eff.size[,2],2)
#    eff.size[,4]=round(eff.size[,4],2)
#    colnames(eff.size) <- c("Incorrect","Correct","P.value","Cohen.d")
    
    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(scored.data[,-i],na.rm=TRUE)*(ncol(scored.data)-1),use="pairwise.complete.obs")
        dist.disc2[i,o]=polyserial(rowMeans(scored.data[,-i],na.rm=TRUE)*(ncol(scored.data)-1),temp)
      }
	  }
	}

	plots <- vector("list",ncol(data))

	for(i in 1:ncol(data)) {

		options.d <- c()
		for(u in 1:length(options)){ 
				options.d[u] <- paste(options[u],"( ",round(dist.disc2[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())+
		             theme(legend.justification=c(0,1),legend.position=c(0,1),legend.text=element_text(size=12,face="bold"))+
		             scale_x_continuous(limits = c(0,ncol(data)),breaks=seq(0,ncol(data),ceiling(ncol(data)/10)))+
		            scale_y_continuous(limits = c(0,1))+xlab("Score Groups")+ylab("Proporion of Being Selected")
		
		plots[[i]] <- pp
	}

	###############################################################

	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 Miami","\n")
	cat("Department of Educational and Psychological Studies","\n")
	cat("Research, Measurement, and Evaluation Program","\n")
	cat("","\n")
	cat("[email protected]","\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\n")

	cat(sprintf("%50s","ITEM STATISTICS"),"\n")
	cat("","\n")
	print(round(item.stat,3))
	cat("","\n")
	cat("","\n")
	cat("","\n")

	cat(sprintf("%50s","DISTRACTOR SELECTION PROPORTIONS"),"\n")
	cat("","\n")
	print(round(dist.sel,3))
	cat("","\n")
	cat("","\n")
	cat("","\n")
  
  cat(sprintf("%50s","DISTRACTOR Point-Biserial"),"\n")
	cat("","\n")
	print(round(dist.disc,3))
	cat("","\n")
	cat("","\n")
	cat("","\n")
	
	cat(sprintf("%50s","DISTRACTOR Biserial"),"\n")
	cat("","\n")
	print(round(dist.disc2,3))	
	cat("","\n")
	cat("","\n")
	cat("","\n")

	return(list(plots=plots))
}

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itemanalysis documentation built on May 2, 2019, 1:05 p.m.