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Author

*The author of this computation has been verified*

R Software Module

rwasp_surveyscores.wasp

Title produced by software

Survey Scores

Date of computation

Fri, 10 Dec 2010 15:09:44 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/10/t1291993868hxezs307pn4z6cd.htm/, Retrieved Mon, 29 Apr 2024 08:39:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107760, Retrieved Mon, 29 Apr 2024 08:39:15 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

151

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]
-   PD  [Survey Scores] [Task 3.1 deel 1] [2010-10-15 11:32:13] [9f32078fdcdc094ca748857d5ebdb3de]
-    D      [Survey Scores] [Pearson correlatie] [2010-12-10 15:09:44] [7b4029fa8534fd52dfa7d68267386cff] [Current]

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Dataseries X:

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Histogram

Boxplots

3	4	4	4	3	2	4
4	4	4	3	2	4	4
4	5	5	4	4	4	4
2	2	3	4	2	3	3
2	2	4	4	4	4	2
3	4	3	4	2	2	4
2	4	4	4	3	4	4
3	4	3	4	4	2	3
3	4	2	2	1	3	2
3	2	4	4	2	3	3
3	3	2	3	2	3	3
4	4	2	2	2	3	2
2	3	2	2	2	2	2
2	4	1	3	1	3	2
1	4	4	4	2	4	4
1	4	4	5	4	5	4
3	4	2	4	2	4	3
2	2	2	2	2	2	2
2	4	3	4	3	3	3
3	4	3	3	2	4	4
4	4	3	4	3	2	3
4	3	4	2	2	3	3
3	2	3	2	3	2	4
2	4	2	4	2	2	2
3	2	2	4	3	2	4
4	3	4	4	3	3	2
3	4	4	4	4	3	3
2	3	3	3	2	3	3
1	4	4	4	4	3	4
3	4	2	5	3	3	2
2	4	4	4	4	3	4
4	4	4	4	3	3	4
4	4	5	4	4	4	4
5	4	5	4	4	5	5
2	4	4	4	3	4	4
4	5	4	4	5	4	3
4	4	4	4	4	4	4
2	2	2	4	2	3	2
4	4	4	4	4	4	4
4	4	4	5	4	4	4
2	5	4	4	5	2	4
3	4	3	4	4	4	3
2	2	2	2	2	2	2
4	4	3	4	4	3	3
3	4	4	4	4	3	4
1	4	3	4	1	4	3
2	2	2	4	2	2	4
5	5	4	4	5	4	5
2	4	4	4	4	3	4
4	4	3	4	2	4	4
4	4	4	3	3	2	3
2	4	3	4	4	2	2
2	4	4	4	2	2	2
2	3	2	4	1	1	2
4	5	4	3	4	5	5
3	4	4	3	2	4	4
4	4	4	4	4	2	4
2	4	4	4	2	4	4
3	4	3	5	3	2	2
2	2	1	3	2	2	2
2	4	3	4	3	4	4
3	4	3	4	2	4	4
2	4	4	4	2	4	4
3	4	2	4	3	4	4
2	3	3	4	1	3	3
4	4	5	4	5	4	5
2	2	4	4	4	3	3
5	4	4	4	4	4	2
1	4	3	4	1	3	3
3	4	4	4	2	4	4
2	4	2	4	2	4	2
2	4	3	4	2	3	3
3	4	4	4	4	4	4
4	3	3	3	2	4	4
4	4	4	4	3	3	3
2	4	4	4	2	2	2
3	4	3	3	2	3	3
4	4	3	4	2	3	2
2	4	2	4	4	3	4
2	4	4	4	3	4	4
4	4	3	4	3	3	4
2	2	2	4	2	3	2
4	3	2	2	2	4	2
2	5	3	4	3	4	4
3	3	2	3	2	3	3
4	3	2	3	2	3	3
3	4	4	4	3	4	4
1	4	4	4	4	3	3
3	4	4	4	4	4	4
2	4	2	3	2	2	2
2	4	2	2	2	3	2
1	3	4	5	2	2	2
3	4	2	2	2	2	2
2	4	3	4	3	3	3
3	4	3	4	2	2	3
5	5	5	4	5	4	4
3	4	3	3	2	3	3
	4	4	4	4	2	3
2	2	3	3	3	3	2
2	3	2	4	3	2	2
3	3	4	4	3	3	3
2	3	3	3	2	3	3
2	4	3	2	3	3	3
2	4	3	4	2	3	3
3	2	4	3	2	2	4
5	3	1	2	1	3	2
2	3	3	3	2	3	2
3	4	2	4	2	2	2
3	4	3	4	3	2	3
1	2	2	4	2	2	2
1	3	2	2	2	2	2
3	4	2	2	2	2	3
4	4	4	3	2	3	4
5	5	5	5	5	5	5
3	3	5	4	4	5	5
3	3	3	4	3	2	3
3	4	4	4	4	2	4
2	3	4	4	2	2	3
3	3	3	4	2	4	3
2	2	2	3	1	2	1
3	4	4	4	4	4	3
2	3	2	4	2	2	2
4	4	3	3	4	4	3
2	5	2	4	1	4	2
2	4	2	3	2	4	2
4	3	2	4	3	3	2
4	3	4	4	2	2	3
3	3	4	4	3	3	4
3	3	4	4	2	2	3
3	4	4	4	4	3	4
4	4	3	4	3	3	3
2	3	2	4	2	1	2
1	2	4	5	4	3	4
3	3	3	2	3	1	3
2	2	2	4	2	2	2
3	4	4	4	4	3	4
2	4	3	3	1	4	2
2	4	3	3	2	4	3
3	4	3	4	1	3	3
2	4	3	4	2	4	3
4	4	3	3	3	4	2
4	5	4	4	4	4	4
2	3	5	1	4	3	3
2	4	2	3	4	2	4
3	4	2	4	3	3	4
4	4	4	4	4	4	3
4	5	3	4	4	3	2
4	3	3	4	3	2	2
1	2	1	3	1	1	1
4	4	2	4	2	2	2
4	3	4	4	4	4	3
3	4	4	4	3	2	4
1	5	5	4	5	4	5
2	3	2	4	3	3	2
2	3	4	3	4	5	3
2	3	3	4	2	2	3
3	5	2	4	4	3	3
3	4	4	3	3	3	2
3	2	2	3	3	2	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107760&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107760&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107760&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Summary of survey scores (median of Likert score was subtracted)
Question	mean	Sum ofpositives (Ps)	Sum ofnegatives (Ns)	(Ps-Ns)/(Ps+Ns)	Count ofpositives (Pc)	Count ofnegatives (Nc)	(Pc-Nc)/(Pc+Nc)
1	-0.92	10	157	-0.88	10	100	-0.82
2	-0.54	11	97	-0.8	11	63	-0.7
3	-0.64	7	108	-0.88	7	74	-0.83
4	-0.66	7	112	-0.88	7	69	-0.82
5	-1.17	8	194	-0.92	8	113	-0.87
6	-0.95	6	157	-0.93	6	107	-0.89
7	-0.96	6	158	-0.93	6	101	-0.89

\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 & -0.92 & 10 & 157 & -0.88 & 10 & 100 & -0.82 \tabularnewline
2 & -0.54 & 11 & 97 & -0.8 & 11 & 63 & -0.7 \tabularnewline
3 & -0.64 & 7 & 108 & -0.88 & 7 & 74 & -0.83 \tabularnewline
4 & -0.66 & 7 & 112 & -0.88 & 7 & 69 & -0.82 \tabularnewline
5 & -1.17 & 8 & 194 & -0.92 & 8 & 113 & -0.87 \tabularnewline
6 & -0.95 & 6 & 157 & -0.93 & 6 & 107 & -0.89 \tabularnewline
7 & -0.96 & 6 & 158 & -0.93 & 6 & 101 & -0.89 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107760&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]-0.92[/C][C]10[/C][C]157[/C][C]-0.88[/C][C]10[/C][C]100[/C][C]-0.82[/C][/ROW]
[ROW][C]2[/C][C]-0.54[/C][C]11[/C][C]97[/C][C]-0.8[/C][C]11[/C][C]63[/C][C]-0.7[/C][/ROW]
[ROW][C]3[/C][C]-0.64[/C][C]7[/C][C]108[/C][C]-0.88[/C][C]7[/C][C]74[/C][C]-0.83[/C][/ROW]
[ROW][C]4[/C][C]-0.66[/C][C]7[/C][C]112[/C][C]-0.88[/C][C]7[/C][C]69[/C][C]-0.82[/C][/ROW]
[ROW][C]5[/C][C]-1.17[/C][C]8[/C][C]194[/C][C]-0.92[/C][C]8[/C][C]113[/C][C]-0.87[/C][/ROW]
[ROW][C]6[/C][C]-0.95[/C][C]6[/C][C]157[/C][C]-0.93[/C][C]6[/C][C]107[/C][C]-0.89[/C][/ROW]
[ROW][C]7[/C][C]-0.96[/C][C]6[/C][C]158[/C][C]-0.93[/C][C]6[/C][C]101[/C][C]-0.89[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107760&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107760&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)
Question	mean	Sum ofpositives (Ps)	Sum ofnegatives (Ns)	(Ps-Ns)/(Ps+Ns)	Count ofpositives (Pc)	Count ofnegatives (Nc)	(Pc-Nc)/(Pc+Nc)
1	-0.92	10	157	-0.88	10	100	-0.82
2	-0.54	11	97	-0.8	11	63	-0.7
3	-0.64	7	108	-0.88	7	74	-0.83
4	-0.66	7	112	-0.88	7	69	-0.82
5	-1.17	8	194	-0.92	8	113	-0.87
6	-0.95	6	157	-0.93	6	107	-0.89
7	-0.96	6	158	-0.93	6	101	-0.89

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.796 (0.032)	0.756 (0.049)
(Ps-Ns)/(Ps+Ns)	0.796 (0.032)	1 (0)	0.996 (0)
(Pc-Nc)/(Pc+Nc)	0.756 (0.049)	0.996 (0)	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) & 0.796 (0.032) & 0.756 (0.049) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.796 (0.032) & 1 (0) & 0.996 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.756 (0.049) & 0.996 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107760&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]0.796 (0.032)[/C][C]0.756 (0.049)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.796 (0.032)[/C][C]1 (0)[/C][C]0.996 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.756 (0.049)[/C][C]0.996 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107760&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107760&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)
mean	1 (0)	0.796 (0.032)	0.756 (0.049)
(Ps-Ns)/(Ps+Ns)	0.796 (0.032)	1 (0)	0.996 (0)
(Pc-Nc)/(Pc+Nc)	0.756 (0.049)	0.996 (0)	1 (0)

Kendall tau rank correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.688 (0.039)	0.551 (0.091)
(Ps-Ns)/(Ps+Ns)	0.688 (0.039)	1 (0.004)	0.946 (0.006)
(Pc-Nc)/(Pc+Nc)	0.551 (0.091)	0.946 (0.006)	1 (0.003)

\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) & 0.688 (0.039) & 0.551 (0.091) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.688 (0.039) & 1 (0.004) & 0.946 (0.006) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.551 (0.091) & 0.946 (0.006) & 1 (0.003) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107760&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)[/C][C]0.688 (0.039)[/C][C]0.551 (0.091)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.688 (0.039)[/C][C]1 (0.004)[/C][C]0.946 (0.006)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.551 (0.091)[/C][C]0.946 (0.006)[/C][C]1 (0.003)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107760&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107760&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)
mean	1 (0)	0.688 (0.039)	0.551 (0.091)
(Ps-Ns)/(Ps+Ns)	0.688 (0.039)	1 (0.004)	0.946 (0.006)
(Pc-Nc)/(Pc+Nc)	0.551 (0.091)	0.946 (0.006)	1 (0.003)

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code