### assessment exam analysis

require(foreign)

m = read.spss("C:\\NORTON_0307_Stat1000-4.sav", use.value.labels = TRUE, to.data.frame = TRUE)

m1 = data.frame(m$LAST,m$FIRST,m$V4,m$SSN,m$Q1,m$Q2,m$Q3,m$Q4,m$Q5,m$Q6,m$Q7,m$Q8,m$Q9,m$Q10,m$Q11,m$Q12,m$Q13,m$Q14,m$Q15,m$Q16,m$Q17,m$Q18,m$Q19,m$Q20,m$SCORE)
m1 

edit(m1)

write.table( m1, "C:\\m1.csv", col.names = NA, sep = "," )

m2 = data.frame(m$Q1,m$Q2,m$Q3,m$Q4,m$Q5,m$Q6,m$Q7,m$Q8,m$Q9,m$Q10,m$Q11,m$Q12,m$Q13,m$Q14,m$Q15,m$Q16,m$Q17,m$Q18,m$Q19,m$Q20)
m2 

m2a = m2[2:(dim(m2)[1]),]
m2a

edit(m2a)

write.table( m2a, "C:\\m2a.csv", col.names = NA, sep = "," )

write.table( m2a, "C:\\m2a.txt", quote = F, sep = ",", row.names = F, col.names = F )

ns = dim(m2a)[1] # students
nq = dim(m2a)[2] # questions

m2b = matrix(scan("C:\\m2a.txt",sep = ","),nrow=ns,ncol=nq,byrow=T)
m2b

# analysis

key = m2[1,]
key

score = m1$m.SCORE[2:(dim(m2)[1])]
score

X11()
plot(score)
summary(score)

score1 = as.numeric(as.character(score)) 
score1

X11()
hist(score1,br=c(-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5))
summary(score1)

nr = 4
nc = 5

X11()
par(mfrow=c(nr,nc))
for(i in 1:(nr*nc)){
	plot(m2a[,i],main = paste("Q",i))
}

X11()
par(mfrow=c(nr,nc))
for(i in 1:(nr*nc)){
	hist(as.numeric(as.character(m2a[,i])),main = paste("question",i),br=c(0.5,1.5,2.5,3.5,4.5,5.5),xlab=paste('ans',key[i]))
}

X11()
par(mfrow=c(nr,nc))
for(i in 1:(nr*nc)){
	hist(m2b[,i],main = paste("question",i),br=c(0.5,1.5,2.5,3.5,4.5,5.5),xlab=paste('ans',key[i]))
}

key
summary(m2a)

apply(m2b,1,summary)

grade=c()
for(i in 1:ns){
	grade = c(grade,sum(key==m2b[i,],na.rm=TRUE))
}

X11()
hist(grade,br=c(-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5))
summary(grade)

# right/wrong matrix

w=1
m2c = numeric(nq)
m2c1 = numeric(nq)
for(i in 1:nq){
	if(key[i]==m2b[w,i]) m2c1[i] = 1
}
m2c = m2c1

# convert NA to 0

m2b1 <- matrix(sapply(m2b, function(x) {x[is.na(x)] <- 0; x}) ,nrow=ns,ncol=nq)
m2b1

w=31
m2c = matrix(0,ns,nq)
for(k in 1:ns){
	#m2c1 = numeric(nq)
	for(i in 1:nq){
		if(key[i]==m2b1[k,i]) m2c[k,i] = 1
	}
}

key
m2b[w,]
m2c[w,]
sum(m2c[w,])
grade[w]

# item analysis

library(psychometric)

item.exam(m2c, binary = TRUE)