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36 10 10 10 10 21 32 8 8 9 15 22 33 8 6 12 14 17 39 9 10 14 14 21 34 5 8 6 8 19 39 10 10 13 19 23 36 8 7 12 17 21 33 9 10 13 18 22 30 8 6 6 10 11 39 7 7 12 15 20 37 10 9 10 16 18 37 10 6 9 12 16 35 9 7 12 13 18 32 4 6 7 10 13 36 4 4 10 14 17 36 8 6 11 15 20 41 9 8 15 20 20 36 10 9 10 9 15 37 8 8 12 12 18 29 5 6 10 13 15 39 10 6 12 16 19 37 8 10 11 12 19 32 7 8 11 14 19 36 8 8 12 15 20 43 8 7 15 19 20 30 9 4 12 16 16 33 8 9 11 16 18 28 6 8 9 14 17 30 8 10 11 14 18 28 8 8 11 14 13 39 5 6 9 13 20 34 9 7 15 18 21 34 8 8 12 15 17 29 8 5 9 15 19 32 8 10 12 15 20 33 6 2 12 13 15 27 6 6 9 14 15 35 9 7 9 15 19 38 8 5 11 14 18 40 9 8 12 19 22 34 10 7 12 16 20 34 8 7 12 16 18 26 8 10 12 12 14 39 7 7 6 10 15 34 7 6 11 11 17 39 10 10 12 13 16 26 8 6 9 14 17 30 7 5 11 11 15 34 10 8 9 11 17 34 7 8 10 16 18 29 7 5 10 9 16 41 9 8 9 16 18 43 9 10 12 19 22 31 8 7 11 13 16 33 6 7 9 15 16 34 8 7 9 14 20 30 9 7 12 15 18 23 2 2 6 11 16 29 6 4 10 14 16 35 8 6 12 15 20 40 8 7 11 17 21 27 7 9 14 16 18 30 8 9 8 13 15 27 6 4 9 15 18 29 10 9 10 14 18 33 10 9 10 15 20 32 10 8 10 14 18 33 8 7 11 12 16 36 8 9 10 12 19 34 7 7 12 15 20 45 10 6 14 17 22 30 5 7 10 13 18 22 3 2 8 5 8 24 2 3 8 7 13 25 3 4 7 10 13 26 4 5 11 15 18 27 2 2 6 9 12 27 6 6 9 9 16 35 8 8 12 15 21 36 8 5 12 14 20 32 5 4 12 11 18 35 10 10 9 18 22 35 9 10 15 20 23 36 8 10 15 20 23 37 9 9 13 16 21 33 8 5 9 15 16 25 5 5 12 14 14 35 7 7 9 13 18 37 9 10 15 18 22 36 8 9 11 14 20 35 4 8 11 12 18 29 7 8 6 9 12 35 8 8 14 19 17 31 7 8 11 13 15 30 7 8 8 12 18 37 9 7 10 14 18 36 6 6 10 6 15 35 7 8 9 14 16 32 4 2 8 11 15 34 6 5 9 11 16 37 10 4 10 14 19 36 9 9 11 12 19 39 10 10 14 19 23 37 8 6 12 13 20 31 4 4 9 14 18 40 8 10 13 17 21 38 5 6 8 12 19 35 8 7 12 16 18 38 9 7 14 15 19 32 8 8 9 15 17 41 4 6 10 15 21 28 8 5 12 16 19 40 10 6 12 15 24 25 6 7 9 12 12 28 7 6 9 13 15 37 10 9 12 14 18 37 9 9 15 17 19 40 8 7 12 14 22 26 3 6 11 14 19 30 8 7 8 14 16 32 7 7 11 15 19 31 7 8 11 11 18 28 8 7 10 11 18 34 8 8 12 16 19 39 7 7 9 12 21 33 7 4 11 12 19 43 9 10 15 19 22 37 9 8 14 18 23 31 9 8 6 16 17 31 4 2 9 16 18 34 6 6 9 13 19 32 6 4 8 11 15 27 6 4 7 10 14 34 8 9 10 14 18 28 3 2 6 14 17 32 8 6 9 14 19 39 8 7 9 16 16 28 6 4 7 10 14 39 10 10 11 16 20 32 2 3 9 7 16 36 9 7 12 16 18 31 6 4 9 15 16 39 6 8 10 17 21 23 5 4 11 11 16 25 4 5 7 11 14 32 7 6 12 10 16 32 5 5 8 13 19 36 8 9 13 14 19 39 6 6 11 13 19 31 9 8 11 13 18 32 6 4 12 12 16 28 4 4 11 10 14 34 7 8 12 15 19 28 2 4 3 6 11 38 8 10 10 15 18 35 9 8 13 15 18 32 6 5 10 11 16 26 5 3 6 14 20 32 7 7 11 14 18 28 8 6 12 16 20 31 4 5 9 12 16 33 9 5 10 15 18 38 9 9 15 20 19 38 9 2 9 12 19 36 7 7 6 9 15 31 5 7 9 13 17 36 7 5 15 15 21 43 9 9 15 19 24 37 8 4 9 11 16 28 6 5 11 11 13 35 9 9 9 17 21 34 8 7 11 15 16 40 7 6 10 14 17 31 7 8 9 15 17 41 7 7 6 11 18 35 8 6 12 12 18 38 10 8 13 15 23 37 6 6 12 16 20 31 6 7 12 16 20
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System_Quality Intention_to_Use Relative_Advantage Perceived_Usefulness Perceived_Ease_of_Use Information_Quality
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R Code
docor <- function(x,y,method) { r <- cor.test(x,y,method=method) paste(round(r$estimate,3),' (',round(r$p.value,3),')',sep='') } x <- t(x) nx <- length(x[,1]) cx <- length(x[1,]) mymedian <- median(as.numeric(strsplit(par1,' ')[[1]])) myresult <- array(NA, dim = c(cx,7)) rownames(myresult) <- paste('Q',1:cx,sep='') colnames(myresult) <- c('mean','Sum of<br />positives (Ps)','Sum of<br />negatives (Ns)', '(Ps-Ns)/(Ps+Ns)', 'Count of<br />positives (Pc)', 'Count of<br />negatives (Nc)', '(Pc-Nc)/(Pc+Nc)') for (i in 1:cx) { spos <- 0 sneg <- 0 cpos <- 0 cneg <- 0 for (j in 1:nx) { if (!is.na(x[j,i])) { myx <- as.numeric(x[j,i]) - mymedian if (myx > 0) { spos = spos + myx cpos = cpos + 1 } if (myx < 0) { sneg = sneg + abs(myx) cneg = cneg + 1 } } } myresult[i,1] <- round(mean(as.numeric(x[,i]),na.rm=T)-mymedian,2) myresult[i,2] <- spos myresult[i,3] <- sneg myresult[i,4] <- round((spos - sneg) / (spos + sneg),2) myresult[i,5] <- cpos myresult[i,6] <- cneg myresult[i,7] <- round((cpos - cneg) / (cpos + cneg),2) } print(myresult) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Summary of survey scores (median of Likert score was subtracted)',8,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Question',header=TRUE) for (i in 1:7) { a<-table.element(a,colnames(myresult)[i],header=TRUE) } a<-table.row.end(a) for (i in 1:cx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) for (j in 1:7) { a<-table.element(a,myresult[i,j],align='right') } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Pearson correlations of survey scores (and p-values)',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',header=TRUE) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE) a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,docor(myresult[,1],myresult[,1],method='pearson'),align='right') a<-table.element(a,docor(myresult[,1],myresult[,4],method='pearson'),align='right') a<-table.element(a,docor(myresult[,1],myresult[,7],method='pearson'),align='right') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE) a<-table.element(a,docor(myresult[,4],myresult[,1],method='pearson'),align='right') a<-table.element(a,docor(myresult[,4],myresult[,4],method='pearson'),align='right') a<-table.element(a,docor(myresult[,4],myresult[,7],method='pearson'),align='right') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE) a<-table.element(a,docor(myresult[,7],myresult[,1],method='pearson'),align='right') a<-table.element(a,docor(myresult[,7],myresult[,4],method='pearson'),align='right') a<-table.element(a,docor(myresult[,7],myresult[,7],method='pearson'),align='right') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Kendall tau rank correlations of survey scores (and p-values)',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',header=TRUE) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE) a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,docor(myresult[,1],myresult[,1],method='kendall'),align='right') a<-table.element(a,docor(myresult[,1],myresult[,4],method='kendall'),align='right') a<-table.element(a,docor(myresult[,1],myresult[,7],method='kendall'),align='right') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE) a<-table.element(a,docor(myresult[,4],myresult[,1],method='kendall'),align='right') a<-table.element(a,docor(myresult[,4],myresult[,4],method='kendall'),align='right') a<-table.element(a,docor(myresult[,4],myresult[,7],method='kendall'),align='right') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE) a<-table.element(a,docor(myresult[,7],myresult[,1],method='kendall'),align='right') a<-table.element(a,docor(myresult[,7],myresult[,4],method='kendall'),align='right') a<-table.element(a,docor(myresult[,7],myresult[,7],method='kendall'),align='right') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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