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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_surveyscores.wasp
Title produced by softwareSurvey Scores
Date of computationFri, 21 Dec 2012 10:42:32 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356104566njmzux9qroj8ndx.htm/, Retrieved Thu, 25 Apr 2024 17:18:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203832, Retrieved Thu, 25 Apr 2024 17:18:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Survey Scores] [Intrinsic Motivat...] [2010-10-12 11:18:40] [b98453cac15ba1066b407e146608df68]
- R PD  [Survey Scores] [Extrinsic motivat...] [2012-10-16 19:41:05] [0f86cfddc502cf698caf54991235c44d]
-         [Survey Scores] [Extrinsic motivat...] [2012-10-16 19:44:35] [0f86cfddc502cf698caf54991235c44d]
-    D      [Survey Scores] [Extrinsic motivat...] [2012-10-16 19:47:48] [0f86cfddc502cf698caf54991235c44d]
- R PD        [Survey Scores] [I1] [2012-12-21 13:20:17] [0f86cfddc502cf698caf54991235c44d]
- R  D          [Survey Scores] [I2: survey scores] [2012-12-21 14:23:24] [0f86cfddc502cf698caf54991235c44d]
- R  D            [Survey Scores] [I3 survey scores] [2012-12-21 15:03:22] [0f86cfddc502cf698caf54991235c44d]
-   PD              [Survey Scores] [E1 Survey scores] [2012-12-21 15:15:34] [0f86cfddc502cf698caf54991235c44d]
-    D                [Survey Scores] [E2 Survey scores] [2012-12-21 15:19:06] [0f86cfddc502cf698caf54991235c44d]
-    D                  [Survey Scores] [E3 Survey scores] [2012-12-21 15:27:56] [0f86cfddc502cf698caf54991235c44d]
-    D                      [Survey Scores] [A Survey scores] [2012-12-21 15:42:32] [a1c9ee8128156b02a669e54abb47d426] [Current]
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Dataseries X:
1	1	1	1
1	1	1	1
2	1	1	2
2	2	2	2
2	2	2	2
1	1	1	1
1	1	1	1
1	1	4	2
1	2	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
2	3	2	1
1	1	1	1
1	1	1	1
1	1	1	1
2	2	2	2
1	1	1	1
1	4	1	1
1	1	1	1
1	1	1	1
1	2	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
4	6	3	2
3	3	3	1
1	1	1	1
2	2	2	2
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	4
1	1	1	1
2	2	1	1
1	2	1	1
1	1	1	1
4	4	4	4
1	2	1	1
3	3	3	3
2	2	1	1
3	2	2	2
2	2	4	1
1	1	1	1
2	1	1	1
1	1	1	1
1	1	1	1
1	2	1	1
1	1	1	1
1	1	1	1
1	1	1	1
2	1	1	1
1	1	1	1
3	1	1	1
1	1	1	1
1	1	1	1
4	4	5	5
1	1	1	1
1	2	2	1
1	1	1	1
1	1	1	1
1	2	1	1
1	1	1	1
1	1	1	1
1	2	1	1
1	3	3	3
1	2	1	1
2	2	2	2
2	2	2	2
1	2	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	2	1
1	1	1	1
1	1	1	1
2	2	2	2
1	1	1	1
1	2	1	1
4	3	4	3
3	2	2	1
2	2	2	2
1	1	1	1
1	1	1	1
1	3	1	1
1	1	1	1
2	1	2	2
1	1	1	4
1	1	1	1
2	1	1	2
1	1	1	1
3	2	1	1
1	1	1	1
1	1	1	1
2	2	2	2
1	1	1	1
1	1	1	1
2	3	3	2
2	2	2	2
1	2	2	1
1	1	1	1
1	1	1	1
1	1	1	1
1	2	1	1
1	1	1	1
2	2	1	1
1	1	1	1
1	2	1	1
2	2	2	1
2	4	1	1
1	2	1	1
2	1	4	1
2	4	2	2
2	2	3	1
2	1	1	1
4	3	3	2
1	1	1	1
2	1	1	1
1	1	1	1
1	2	1	2
1	1	1	1
1	1	1	1
3	2	1	1
2	2	2	1
1	7	1	1
1	1	1	1
1	2	1	1
2	3	1	2
5	3	2	1
1	4	1	1
1	1	1	1
2	2	2	2
1	2	1	2
2	2	1	2
1	2	1	1
1	1	1	1
1	5	1	1
1	1	1	1
2	3	2	1
1	2	1	2
1	1	1	1
2	3	2	2
1	2	1	1
1	2	2	1
1	1	1	1
1	1	1	1
1	1	1	1
2	1	1	1
2	2	1	1
4	4	4	4
1	3	1	1
1	1	2	2
1	1	1	1
1	1	1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203832&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]1 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=203832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203832&T=0

As an alternative you can also use a 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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Summary of survey scores (median of Likert score was subtracted)
QuestionmeanSum ofpositives (Ps)Sum ofnegatives (Ns)(Ps-Ns)/(Ps+Ns)Count ofpositives (Pc)Count ofnegatives (Nc)(Pc-Nc)/(Pc+Nc)
1-1.568260-0.947148-0.91
2-1.3316232-0.8710139-0.87
3-1.628270-0.947148-0.91
4-1.76281-0.965154-0.94

\begin{tabular}{lllllllll}
\hline
Summary of survey scores (median of Likert score was subtracted) \tabularnewline
Question & mean & Sum ofpositives (Ps) & Sum ofnegatives (Ns) & (Ps-Ns)/(Ps+Ns) & Count ofpositives (Pc) & Count ofnegatives (Nc) & (Pc-Nc)/(Pc+Nc) \tabularnewline
1 & -1.56 & 8 & 260 & -0.94 & 7 & 148 & -0.91 \tabularnewline
2 & -1.33 & 16 & 232 & -0.87 & 10 & 139 & -0.87 \tabularnewline
3 & -1.62 & 8 & 270 & -0.94 & 7 & 148 & -0.91 \tabularnewline
4 & -1.7 & 6 & 281 & -0.96 & 5 & 154 & -0.94 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203832&T=1

[TABLE]
[ROW][C]Summary of survey scores (median of Likert score was subtracted)[/C][/ROW]
[ROW][C]Question[/C][C]mean[/C][C]Sum ofpositives (Ps)[/C][C]Sum ofnegatives (Ns)[/C][C](Ps-Ns)/(Ps+Ns)[/C][C]Count ofpositives (Pc)[/C][C]Count ofnegatives (Nc)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]1[/C][C]-1.56[/C][C]8[/C][C]260[/C][C]-0.94[/C][C]7[/C][C]148[/C][C]-0.91[/C][/ROW]
[ROW][C]2[/C][C]-1.33[/C][C]16[/C][C]232[/C][C]-0.87[/C][C]10[/C][C]139[/C][C]-0.87[/C][/ROW]
[ROW][C]3[/C][C]-1.62[/C][C]8[/C][C]270[/C][C]-0.94[/C][C]7[/C][C]148[/C][C]-0.91[/C][/ROW]
[ROW][C]4[/C][C]-1.7[/C][C]6[/C][C]281[/C][C]-0.96[/C][C]5[/C][C]154[/C][C]-0.94[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203832&T=1

As an alternative you can also use a QR Code:  

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

Summary of survey scores (median of Likert score was subtracted)
QuestionmeanSum ofpositives (Ps)Sum ofnegatives (Ns)(Ps-Ns)/(Ps+Ns)Count ofpositives (Pc)Count ofnegatives (Nc)(Pc-Nc)/(Pc+Nc)
1-1.568260-0.947148-0.91
2-1.3316232-0.8710139-0.87
3-1.628270-0.947148-0.91
4-1.76281-0.965154-0.94







Pearson correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.984 (0.016)0.972 (0.028)
(Ps-Ns)/(Ps+Ns)0.984 (0.016)1 (0)0.963 (0.037)
(Pc-Nc)/(Pc+Nc)0.972 (0.028)0.963 (0.037)1 (0)

\begin{tabular}{lllllllll}
\hline
Pearson correlations of survey scores (and p-values) \tabularnewline
 & mean & (Ps-Ns)/(Ps+Ns) & (Pc-Nc)/(Pc+Nc) \tabularnewline
mean & 1 (0) & 0.984 (0.016) & 0.972 (0.028) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.984 (0.016) & 1 (0) & 0.963 (0.037) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.972 (0.028) & 0.963 (0.037) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203832&T=2

[TABLE]
[ROW][C]Pearson correlations of survey scores (and p-values)[/C][/ROW]
[ROW][C][/C][C]mean[/C][C](Ps-Ns)/(Ps+Ns)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]mean[/C][C]1 (0)[/C][C]0.984 (0.016)[/C][C]0.972 (0.028)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.984 (0.016)[/C][C]1 (0)[/C][C]0.963 (0.037)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.972 (0.028)[/C][C]0.963 (0.037)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203832&T=2

As an alternative you can also use a QR Code:  

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

Pearson correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.984 (0.016)0.972 (0.028)
(Ps-Ns)/(Ps+Ns)0.984 (0.016)1 (0)0.963 (0.037)
(Pc-Nc)/(Pc+Nc)0.972 (0.028)0.963 (0.037)1 (0)







Kendall tau rank correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0.083)0.913 (0.071)0.913 (0.071)
(Ps-Ns)/(Ps+Ns)0.913 (0.071)1 (0.056)1 (0.056)
(Pc-Nc)/(Pc+Nc)0.913 (0.071)1 (0.056)1 (0.056)

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations of survey scores (and p-values) \tabularnewline
 & mean & (Ps-Ns)/(Ps+Ns) & (Pc-Nc)/(Pc+Nc) \tabularnewline
mean & 1 (0.083) & 0.913 (0.071) & 0.913 (0.071) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.913 (0.071) & 1 (0.056) & 1 (0.056) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.913 (0.071) & 1 (0.056) & 1 (0.056) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203832&T=3

[TABLE]
[ROW][C]Kendall tau rank correlations of survey scores (and p-values)[/C][/ROW]
[ROW][C][/C][C]mean[/C][C](Ps-Ns)/(Ps+Ns)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]mean[/C][C]1 (0.083)[/C][C]0.913 (0.071)[/C][C]0.913 (0.071)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.913 (0.071)[/C][C]1 (0.056)[/C][C]1 (0.056)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.913 (0.071)[/C][C]1 (0.056)[/C][C]1 (0.056)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203832&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203832&T=3

As an alternative you can also use a QR Code:  

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

Kendall tau rank correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0.083)0.913 (0.071)0.913 (0.071)
(Ps-Ns)/(Ps+Ns)0.913 (0.071)1 (0.056)1 (0.056)
(Pc-Nc)/(Pc+Nc)0.913 (0.071)1 (0.056)1 (0.056)



Parameters (Session):
Parameters (R input):
par1 = 1 2 3 4 5 6 7 ;
R code (references can be found in the software module):
par1 <- '1 2 3 4 5'
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
positives (Ps)','Sum of
negatives (Ns)', '(Ps-Ns)/(Ps+Ns)', 'Count of
positives (Pc)', 'Count of
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)
}
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')