# Dataset TIMSS 2011 of Australian and Taiwanese Students Mathematics items of TIMSS 2011 of 1773 Australian and Taiwanese students. The dataset `data.timssAusTwn` contains raw responses while `data.timssAusTwn.scored` contains scored item responses. data ```r data(data.timssAusTwn) data(data.timssAusTwn.scored) ``` ## Format A data frame with 1773 observations on the following 14 variables. - **`M032166`**: a mathematics item - **`M032721`**: a mathematics item - **`M032757`**: a mathematics item - **`M032760A`**: a mathematics item - **`M032760B`**: a mathematics item - **`M032760C`**: a mathematics item - **`M032761`**: a mathematics item - **`M032692`**: a mathematics item - **`M032626`**: a mathematics item - **`M032595`**: a mathematics item - **`M032673`**: a mathematics item - **`IDCNTRY`**: Country identifier - **`ITSEX`**: Gender - **`IDBOOK`**: Booklet identifier ## See Also [http://www.edmeasurementsurveys.com/TAM/Tutorials/5PartialCredit.htm](http://www.edmeasurementsurveys.com/TAM/Tutorials/5PartialCredit.htm) [http://www.edmeasurementsurveys.com/TAM/Tutorials/6Population.htm](http://www.edmeasurementsurveys.com/TAM/Tutorials/6Population.htm) ## Examples ```r data(data.timssAusTwn) raw_resp <- data.timssAusTwn #Recode data resp <- raw_resp[,1:11] #Column 12 is country code. Column 13 is gender code. Column 14 is Book ID. all.na <- rowMeans( is.na(resp) )==1 #Find records where all responses are missing. resp <- resp[!all.na,] #Delete records with all missing responses resp[resp==20 | resp==21] <- 2 #TIMSS double-digit coding: "20" or "21" is a score of 2 resp[resp==10 | resp==11] <- 1 #TIMSS double-digit coding: "10" or "11" is a score of 1 resp[resp==70 | resp==79] <- 0 #TIMSS double-digit coding: "70" or "79" is a score of 0 resp[resp==99] <- 0 #"99" is omitted responses. Score it as wrong here. resp[resp==96 | resp==6] <- NA #"96" and "6" are not-reached items. Treat these as missing. #Score multiple-choice items #"resp" contains raw responses for MC items. Scored <- resp Scored[,9] <- (resp[,9]==4)*1 #Key for item 9 is D. Scored[,c(1,2)] <- (resp[,c(1,2)]==2)*1 #Key for items 1 and 2 is B. Scored[,c(10,11)] <- (resp[,c(10,11)]==3)*1 #Key for items 10 and 11 is C. #Run IRT analysis for partial credit model (MML estimation) mod1 <- TAM::tam.mml(Scored) #Item parameters mod1$xsi #Thurstonian thresholds tthresh <- TAM::tam.threshold(mod1) tthresh ## Not run: #Plot Thurstonian thresholds windows (width=8, height=7) par(ps=9) dotchart(t(tthresh), pch=19) # plot expected response curves plot( mod1, ask=TRUE) #Re-run IRT analysis in JML mod1.2 <- TAM::tam.jml(Scored) stats::var(mod1.2$WLE) #Re-run the model with "not-reached" coded as incorrect. Scored2 <- Scored Scored2[is.na(Scored2)] <- 0 #Prepare anchor parameter values nparam <- length(mod1$xsi$xsi) xsi <- mod1$xsi$xsi anchor <- matrix(c(seq(1,nparam),xsi), ncol=2) #Run IRT with item parameters anchored on mod1 values mod2 <- TAM::tam.mml(Scored2, xsi.fixed=anchor) #WLE ability estimates ability <- TAM::tam.wle(mod2) ability #CTT statistics ctt <- TAM::tam.ctt(resp, ability$theta) write.csv(ctt,"TIMSS_CTT.csv") #plot histograms of ability and item parameters in the same graph windows(width=4.45, height=4.45, pointsize=12) layout(matrix(c(1,1,2),3,byrow=TRUE)) layout.show(2) hist(ability$theta,xlim=c(-3,3),breaks=20) hist(tthresh,xlim=c(-3,3),breaks=20) #Extension #Score equivalence table dummy <- matrix(0,nrow=16,ncol=11) dummy[lower.tri(dummy)] <- 1 dummy[12:16,c(3,4,7,8)][lower.tri(dummy[12:16,c(3,4,7,8)])]<-2 mod3 <- TAM::tam.mml(dummy, xsi.fixed=anchor) wle3 <- TAM::tam.wle(mod3) ## End(Not run) ```

data.timssAusTwn dataset