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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_surveyscores.wasp
Title produced by softwareSurvey Scores
Date of computationTue, 19 Oct 2010 14:36:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Oct/19/t1287499091ssewsc3ijuiac3m.htm/, Retrieved Mon, 29 Apr 2024 01:23:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=86550, Retrieved Mon, 29 Apr 2024 01:23:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

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 11:28:29] [b98453cac15ba1066b407e146608df68]
F   PD    [Survey Scores] [] [2010-10-19 14:36:55] [a7dcbcc9dd9573c89c41df6a7f8b5a0d] [Current]
Feedback Forum
2010-10-20 17:33:11 [] [reply] 
opnieuw beoordeelt de student de cijfergegevens hier fout. In de eerste tabel spreken we niet van correlatie, maar pas in de tweede tabel. Wel heeft de student gelijk dat als er een lage correlatie zou zijn we geen besluiten kunnen vormen met deze methode, maar aangezien de correlatie ook hier weer boven de 0.95 ligt kunnen we wel besluiten vormen. Ook hier zien we dat het gemiddelde alweer afwijkt van de twee andere kolommen.
2010-10-22 13:00:10 [] [reply] 
Pearson correlations of survey scores (and p-values)
in deze kolom moet je kijken voor de correlatie. Ik denk je je hebt vergist qua tabel om in te kijken. Ook hier is de correlatie weer bijna 1.
2010-10-23 10:36:59 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Hier kan ik het eens zijn met de twee andere reviewers, de resultaten werden inderdaad verkeerd geïnterpreteerd, waardoor de student foute conclusies trekt. De conclusie is hier namelijk gelijkaardig aan die van de eerste twee types van extrinsieke motivatie. Men ziet met andere woorden een hoge correlatie tussen de drie methodes van berekening waaruit men kan besluiten dat men zich voor verdere berekingen kan baseren op het gemiddelde en de resultaten dus kan interpreteren als getallen waarmee men kan rekenen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	7	5	7
5	5	5	5
4	6	5	5
3	6	6	6
6	6	6	6
5	5	5	7
5	7	5	6
1	6	6	7
4	7	7	7
5	6	6	6
6	7	7	7
7	7	6	7
5	6	6	5
6	6	6	6
5	7	6	7
4	6	6	6
7	7	7	7
7	7	7	7
6	7	7	7
6	7	6	6
2	5	5	4
7	7	7	7
5	5	5	6
4	6	7	7
7	7	6	7
1	6	2	5
1	6	2	5
7	6	7	7
4	6	4	6
5	6	4	6
5	6	5	6
5	5	5	6
4	3	4	1
5	3	7	5
5	5	7	7
4	6	4	5
7	7	7	7
7	7	4	5
7	7	6	7
4	6	6	6
7	7	6	7
7	7	6	6
4	6	6	6
3	6	7	5
2	5	6	6
6	6	6	6
4	6	4	5
5	7	7	7
4	6	6	6
7	7	7	7
4	6	6	5
6	5	7	5
7	7	7	7
1	4	2	3
5	7	6	6
4	5	6	6
5	6	5	5
5	7	6	6
5	7	6	6
5	7	6	7
5	7	6	7
5	6	6	6
6	6	4	5
3	6	3	4
4	4	4	5
6	6	7	6
6	6	6	6
3	7	6	7
5	7	7	6
2	7	7	7
7	7	7	7
7	7	5	7
4	6	6	6
6	5	3	5
6	7	6	7
3	5	5	5
3	5	5	5
6	7	6	6
7	7	6	7
3	2	4	3
1	6	4	4
5	6	5	5
5	6	6	6
5	6	5	6
5	5	5	6
6	6	6	6
6	7	7	7
5	5	5	7
7	7	7	7
6	6	7	7
1	5	2	2
3	6	5	5
5	7	4	6
1	7	7	6
6	6	6	6
4	7	7	7
5	6	5	5
5	5	5	5
6	5	4	6
5	6	7	6
5	6	5	7
4	6	4	4
6	6	6	6
6	6	6	6
4	5	5	5
5	5	5	5
5	5	3	5
2	7	5	6
7	7	6	7
5	6	6	6
5	7	7	7
2	7	7	7
3	4	5	5
5	5	5	6
5	7	6	7
5	7	4	7
6	7	7	7
6	6	5	7
4	5	6	5
6	7	7	7
3	4	7	7
6	6	6	6
4	6	6	5
3	3	4	5
4	7	7	7
6	6	6	7
4	7	5	6
6	6	6	6
5	5	5	6
5	6	5	6
5	6	6	6
3	7	6	6
5	6	4	5
4	7	6	6
5	6	7	7
5	6	6	6
1	7	7	7
4	7	7	7
7	7	6	6
4	7	5	6
6	6	5	7
7	7	5	5
6	7	7	5
5	6	3	6
6	7	6	7
5	6	5	5
5	7	7	7
6	6	4	5
5	5	6	6
3	4	4	5
6	7	6	7
7	7	7	7
4	5	4	5
5	7	6	7
4	6	7	6
5	5	5	6
2	6	6	6
7	7	4	7
5	7	4	6
4	6	6	5
2	5	4	5
4	6	3	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86550&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86550&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86550&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 time0 seconds
R Server'George Udny Yule' @ 72.249.76.132






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)
10.79179510.56104280.58
22.0834250.9715340.95
31.56266130.9113190.87
41.9632570.9615440.95

\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 & 0.79 & 179 & 51 & 0.56 & 104 & 28 & 0.58 \tabularnewline
2 & 2.08 & 342 & 5 & 0.97 & 153 & 4 & 0.95 \tabularnewline
3 & 1.56 & 266 & 13 & 0.91 & 131 & 9 & 0.87 \tabularnewline
4 & 1.96 & 325 & 7 & 0.96 & 154 & 4 & 0.95 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86550&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]0.79[/C][C]179[/C][C]51[/C][C]0.56[/C][C]104[/C][C]28[/C][C]0.58[/C][/ROW]
[ROW][C]2[/C][C]2.08[/C][C]342[/C][C]5[/C][C]0.97[/C][C]153[/C][C]4[/C][C]0.95[/C][/ROW]
[ROW][C]3[/C][C]1.56[/C][C]266[/C][C]13[/C][C]0.91[/C][C]131[/C][C]9[/C][C]0.87[/C][/ROW]
[ROW][C]4[/C][C]1.96[/C][C]325[/C][C]7[/C][C]0.96[/C][C]154[/C][C]4[/C][C]0.95[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86550&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86550&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:

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)
10.79179510.56104280.58
22.0834250.9715340.95
31.56266130.9113190.87
41.9632570.9615440.95






Pearson correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.967 (0.033)0.983 (0.017)
(Ps-Ns)/(Ps+Ns)0.967 (0.033)1 (0)0.996 (0.004)
(Pc-Nc)/(Pc+Nc)0.983 (0.017)0.996 (0.004)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) & 0.967 (0.033) & 0.983 (0.017) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.967 (0.033) & 1 (0) & 0.996 (0.004) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.983 (0.017) & 0.996 (0.004) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86550&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]0.967 (0.033)[/C][C]0.983 (0.017)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.967 (0.033)[/C][C]1 (0)[/C][C]0.996 (0.004)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.983 (0.017)[/C][C]0.996 (0.004)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86550&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86550&T=2

As an alternative you can also use a QR Code:  
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)0.967 (0.033)0.983 (0.017)
(Ps-Ns)/(Ps+Ns)0.967 (0.033)1 (0)0.996 (0.004)
(Pc-Nc)/(Pc+Nc)0.983 (0.017)0.996 (0.004)1 (0)






Kendall tau rank correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0.083)1 (0.083)0.913 (0.071)
(Ps-Ns)/(Ps+Ns)1 (0.083)1 (0.083)0.913 (0.071)
(Pc-Nc)/(Pc+Nc)0.913 (0.071)0.913 (0.071)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) & 1 (0.083) & 0.913 (0.071) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 1 (0.083) & 1 (0.083) & 0.913 (0.071) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.913 (0.071) & 0.913 (0.071) & 1 (0.056) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86550&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]1 (0.083)[/C][C]0.913 (0.071)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]1 (0.083)[/C][C]1 (0.083)[/C][C]0.913 (0.071)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.913 (0.071)[/C][C]0.913 (0.071)[/C][C]1 (0.056)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86550&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86550&T=3

As an alternative you can also use a QR Code:  
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)1 (0.083)0.913 (0.071)
(Ps-Ns)/(Ps+Ns)1 (0.083)1 (0.083)0.913 (0.071)
(Pc-Nc)/(Pc+Nc)0.913 (0.071)0.913 (0.071)1 (0.056)


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 2 3 4 5 6 7 ;
Parameters (R input):
par1 = 1 2 3 4 5 6 7 ;
R code (references can be found in the software module):
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')