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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareInternational Conference on Virtual Learning 2008
Date of computationThu, 22 Oct 2015 07:37:44 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Oct/22/t14454958778idy43zlok0da0h.htm/, Retrieved Tue, 24 Jun 2025 18:28:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=282745, Retrieved Tue, 24 Jun 2025 18:28:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact261

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [International Conference on Virtual Learning 2008] [ICVL 2008 - Figure 2] [2008-06-30 15:48:43] [74be16979710d4c4e7c6647856088456]
- RM      [International Conference on Virtual Learning 2008] [] [2015-10-22 06:37:44] [63a9f0ea7bb98050796b649e85481845] [Current]
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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=282745&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=282745&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=282745&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Correlations between reported feedback submissions and actual feedback submissions
Pearson rhoPearson p-valueKendall tauKendall p-value
Female Bachelor Students-0.06250.6414-0.04270.6505
Male Bachelor Students-0.19460.1627-0.15330.1172
Female Switching Students0.20030.15040.13690.1678
Male Switching Students-0.02450.8338-0.01850.8212

\begin{tabular}{lllllllll}
\hline
Correlations between reported feedback submissions and actual feedback submissions \tabularnewline
 & Pearson rho & Pearson p-value & Kendall tau & Kendall p-value \tabularnewline
Female Bachelor Students & -0.0625 & 0.6414 & -0.0427 & 0.6505 \tabularnewline
Male Bachelor Students & -0.1946 & 0.1627 & -0.1533 & 0.1172 \tabularnewline
Female Switching Students & 0.2003 & 0.1504 & 0.1369 & 0.1678 \tabularnewline
Male Switching Students & -0.0245 & 0.8338 & -0.0185 & 0.8212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=282745&T=1

[TABLE]
[ROW][C]Correlations between reported feedback submissions and actual feedback submissions[/C][/ROW]
[ROW][C][/C][C]Pearson rho[/C][C]Pearson p-value[/C][C]Kendall tau[/C][C]Kendall p-value[/C][/ROW]
[ROW][C]Female Bachelor Students[/C][C]-0.0625[/C][C]0.6414[/C][C]-0.0427[/C][C]0.6505[/C][/ROW]
[ROW][C]Male Bachelor Students[/C][C]-0.1946[/C][C]0.1627[/C][C]-0.1533[/C][C]0.1172[/C][/ROW]
[ROW][C]Female Switching Students[/C][C]0.2003[/C][C]0.1504[/C][C]0.1369[/C][C]0.1678[/C][/ROW]
[ROW][C]Male Switching Students[/C][C]-0.0245[/C][C]0.8338[/C][C]-0.0185[/C][C]0.8212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=282745&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=282745&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Correlations between reported feedback submissions and actual feedback submissions
Pearson rhoPearson p-valueKendall tauKendall p-value
Female Bachelor Students-0.06250.6414-0.04270.6505
Male Bachelor Students-0.19460.1627-0.15330.1172
Female Switching Students0.20030.15040.13690.1678
Male Switching Students-0.02450.8338-0.01850.8212


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = reported feedback ; par2 = actual feedback ;
Parameters (R input):
par1 = reported feedback ; par2 = actual feedback ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
x <- as.data.frame(read.table(file='https://automated.biganalytics.eu/download/statdb.csv',sep=',',header=T))
library(GenKern)
load(file='createtable')
doplot1 <- function(myxcol,myycol,myxlab='',myylab='') {
p0 <- x[x$Pop==0,]
p1 <- x[x$Pop==1,]
dum <- cbind(p0[,myxcol],rank(p0[,myxcol]),p0[,'Gender'])
xf0 <- dum[dum[,3]==0,2]
xm0 <- dum[dum[,3]==1,2]
dum <- cbind(p0[,myycol],rank(p0[,myycol]),p0[,'Gender'])
yf0 <- dum[dum[,3]==0,2]
ym0 <- dum[dum[,3]==1,2]
dum <- cbind(p1[,myxcol],rank(p1[,myxcol]),p1[,'Gender'])
xf1 <- dum[dum[,3]==0,2]
xm1 <- dum[dum[,3]==1,2]
dum <- cbind(p1[,myycol],rank(p1[,myycol]),p1[,'Gender'])
yf1 <- dum[dum[,3]==0,2]
ym1 <- dum[dum[,3]==1,2]
xf <- rank(x[x$Gender==0,myxcol])
yf <- rank(x[x$Gender==0,myycol])
xm <- rank(x[x$Gender==1,myxcol])
ym <- rank(x[x$Gender==1,myycol])
x0 <- rank(x[x$Pop==0,myxcol])
y0 <- rank(x[x$Pop==0,myycol])
x1 <- rank(x[x$Pop==1,myxcol])
y1 <- rank(x[x$Pop==1,myycol])
mycorr <- array(NA,dim=c(4,4))
rownames(mycorr) <- c('Female Bachelor Students','Male Bachelor Students','Female Switching Students','Male Switching Students')
colnames(mycorr) <- c('Pearson rho','Pearson p-value','Kendall tau','Kendall p-value')
bitmap(file='mypicture.png')
par(mfrow=c(2,2))
r <- cor.test(xf0,yf0,method='pearson')
mycorr[1,1] <- r$estimate
mycorr[1,2] <- r$p.value
r <- cor.test(xf0,yf0,method='kendall')
mycorr[1,3] <- r$estimate
mycorr[1,4] <- r$p.value
op <- KernSur(xf0,yf0, xgridsize=150, ygridsize=150,na.rm=T)
image(op$xords, op$yords, op$zden, col=topo.colors(100), axes=TRUE, xlab=myxlab, ylab=myylab, main=rownames(mycorr)[1])
contour(op$xords, op$yords, op$zden, add=TRUE)
box()
abline(0,1)
lines(stats::lowess(cbind(xf0,yf0)),col='white')
r <- cor.test(xm0,ym0,method='pearson')
mycorr[2,1] <- r$estimate
mycorr[2,2] <- r$p.value
r <- cor.test(xm0,ym0,method='kendall')
mycorr[2,3] <- r$estimate
mycorr[2,4] <- r$p.value
op <- KernSur(xm0,ym0, xgridsize=150, ygridsize=150,na.rm=T)
image(op$xords, op$yords, op$zden, col=topo.colors(100), axes=TRUE, xlab=myxlab, ylab=myylab, main=rownames(mycorr)[2])
contour(op$xords, op$yords, op$zden, add=TRUE)
box()
abline(0,1)
lines(stats::lowess(cbind(xm0,ym0)),col='white')
r <- cor.test(xf1,yf1,method='pearson')
mycorr[3,1] <- r$estimate
mycorr[3,2] <- r$p.value
r <- cor.test(xf1,yf1,method='kendall')
mycorr[3,3] <- r$estimate
mycorr[3,4] <- r$p.value
op <- KernSur(xf1,yf1, xgridsize=150, ygridsize=150,na.rm=T)
image(op$xords, op$yords, op$zden, col=topo.colors(100), axes=TRUE, xlab=myxlab, ylab=myylab, main=rownames(mycorr)[3])
contour(op$xords, op$yords, op$zden, add=TRUE)
box()
abline(0,1)
lines(stats::lowess(cbind(xf1,yf1)),col='white')
r <- cor.test(xm1,ym1,method='pearson')
mycorr[4,1] <- r$estimate
mycorr[4,2] <- r$p.value
r <- cor.test(xm1,ym1,method='kendall')
mycorr[4,3] <- r$estimate
mycorr[4,4] <- r$p.value
op <- KernSur(xm1,ym1, xgridsize=150, ygridsize=150,na.rm=T)
image(op$xords, op$yords, op$zden, col=topo.colors(100), axes=TRUE, xlab=myxlab, ylab=myylab, main=rownames(mycorr)[4])
contour(op$xords, op$yords, op$zden, add=TRUE)
box()
abline(0,1)
lines(stats::lowess(cbind(xm1,ym1)),col='white')
dev.off()
mycorr
}
if (par1 == 'actual feedback') {
myxcol='nnzfg'
myxlab = '# submitted messages'
}
if (par1 == 'reported feedback') {
myxcol = 'Reflection'
myxlab = 'reported feedback submissions'
}
if (par1 == 'reported computing') {
myxcol = 'Future'
myxlab = 'reported intention to use'
}
if (par2 == 'actual computing') {
myycol = 'Bcount'
myylab = '# reproducible computations'
}
if (par2 == 'actual feedback') {
myycol = 'nnzfg'
myylab = 'actual feedback submissions'
}
if (par2 == 'reported computing') {
myycol = 'Future'
myylab = 'reported intention to use'
}
mycorr <- doplot1(myxcol=myxcol,myycol=myycol,myxlab=myxlab,myylab=myylab)
a<-table.start()
a<-table.row.start(a)
dum <- 'Correlations between'
dum <- paste(dum,myxlab,sep=' ')
dum <- paste(dum,myylab,sep=' and ')
a<-table.element(a,dum,5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
a<-table.element(a,'Pearson rho',header=TRUE)
a<-table.element(a,'Pearson p-value',header=TRUE)
a<-table.element(a,'Kendall tau',header=TRUE)
a<-table.element(a,'Kendall p-value',header=TRUE)
a<-table.row.end(a)
for (myrow in 1:4) {
a<-table.row.start(a)
a<-table.element(a,rownames(mycorr)[myrow],header=TRUE)
a<-table.element(a,round(mycorr[myrow,1],4))
a<-table.element(a,round(mycorr[myrow,2],4))
a<-table.element(a,round(mycorr[myrow,3],4))
a<-table.element(a,round(mycorr[myrow,4],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')