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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_surveyscores.wasp

Title produced by software

Survey Scores

Date of computation

Tue, 18 Oct 2011 08:18:26 -0400

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/2011/Oct/18/t1318940377i82h71ngjy3gkhl.htm/, Retrieved Sun, 28 Apr 2024 05:06:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=131738, Retrieved Sun, 28 Apr 2024 05:06:21 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

574

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] [] [2011-10-18 12:18:26] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D      [Survey Scores] [] [2011-10-18 12:21:42] [b98453cac15ba1066b407e146608df68] 
-    D        [Survey Scores] [] [2011-10-18 12:23:13] [b98453cac15ba1066b407e146608df68] 
- R             [Survey Scores] [] [2011-10-25 13:47:38] [19d77e37efa419fdc040c74a96874aff] 
- R             [Survey Scores] [] [2011-12-01 13:03:23] [aefb5c2d4042694c5b6b82f93ac1885a] 
- R             [Survey Scores] [E3] [2012-11-06 20:34:44] [ec67509cb0a58a77552cc42e4bdf733e] 
- R             [Survey Scores] [paper one sample ...] [2012-12-05 16:34:46] [1edfe4f7de973a74350ac08c1294a22c] 
- R             [Survey Scores] [] [2012-12-17 18:39:53] [edf0418499cd31d27dbea8ea1d30b3db] 
-                 [Survey Scores] [deel 1 pearsonE3] [2012-12-20 19:29:30] [74be16979710d4c4e7c6647856088456] 
- RM            [Survey Scores] [] [2014-10-15 08:07:04] [eee95947b6243a1febfcd5f41483d733] 
- RM            [Survey Scores] [] [2014-10-15 10:37:08] [394a9522c47495260fca596e959e6202] 
- RM            [Survey Scores] [E3] [2014-10-15 11:00:28] [508ad00fbaced7ad8e80ddb3167ea0fd] 
- RM            [Survey Scores] [E3] [2014-10-15 11:11:10] [56d77bf8347cfd1e104fa9098ce46dbd] 
- RM            [Survey Scores] [] [2014-10-15 11:45:49] [95c11abf048d3a1e472aeccb09199113] 
- RM            [Survey Scores] [] [2014-10-15 11:51:39] [fa1b8827d7de91b8b87087311d3d9fa1] 
- RM            [Survey Scores] [WS3 Question 1] [2014-10-15 12:09:34] [f13ab2b9cdb9c17a0cf473fdf5cac3ab] 
- RM            [Survey Scores] [WS3 task1cc] [2014-10-15 12:15:45] [46c7ebd23dbdec306a09830d8b7528e7] 
- RM            [Survey Scores] [WS3-task1E3] [2014-10-15 12:16:31] [81f624c2f0b20a2549c93e7c3dccf981] 
- RM            [Survey Scores] [ws3] [2014-10-15 12:40:15] [bcd8153d44f369b7624d3c1b4621c4c3] 
- RM            [Survey Scores] [] [2014-10-15 12:48:05] [eee95947b6243a1febfcd5f41483d733] 
- RM            [Survey Scores] [Q1] [2014-10-15 12:49:45] [bcf5edf18529a33bd1494456d2c6cb9a] 
- RM            [Survey Scores] [] [2014-10-15 12:52:24] [eee95947b6243a1febfcd5f41483d733] 
- RM            [Survey Scores] [Kendall tau] [2014-10-15 12:55:25] [ae96d02647dd9ad9c105f1fa6642e295] 
- RM            [Survey Scores] [E3 kendall] [2014-10-15 13:02:38] [e3727f74ca0896859afbe865e40a3465] 
- RM            [Survey Scores] [] [2014-10-15 13:03:11] [6795cd14e59cd8fafcdf800c40b889d9] 
- RM            [Survey Scores] [] [2014-10-15 13:13:00] [63554e339c9381d8e23ab848f4176daf] 
- RM            [Survey Scores] [Task 1.3 WS3] [2014-10-15 13:15:53] [805021881bfa5340347077d26b077617] 
- RMP           [Survey Scores] [] [2014-10-15 13:16:31] [bcf5edf18529a33bd1494456d2c6cb9a] 
- RM            [Survey Scores] [E3] [2014-10-15 13:22:31] [2ba32e9656c7c3fdddad3ba3f1588288] 
- RM            [Survey Scores] [] [2014-10-15 13:25:14] [fda96889f4ef6d31c0c28fd64d281011] 
- RMP           [Survey Scores] [] [2014-10-15 14:00:23] [bcf5edf18529a33bd1494456d2c6cb9a] 
- RM            [Survey Scores] [WS3 Question1.2] [2014-10-15 14:17:41] [ce9f16fa58bb2303d66047ab4343b505] 
- RM            [Survey Scores] [] [2014-10-15 14:30:10] [2b9d0c54c8c845c625e475ed5f1f3af1] 
- RM            [Survey Scores] [WS3 SHW] [2014-10-15 14:30:34] [cac6c5fb035463be46c296b46e439cb5] 
- RM            [Survey Scores] [] [2014-10-15 14:38:14] [8d160a85bfd9526a7d0e42afc5fb569b] 
- RM            [Survey Scores] [] [2014-10-15 15:18:07] [dacad244957cb51472792888970d4390] 
- RM            [Survey Scores] [WSH 3, Task 1b] [2014-10-15 15:25:05] [e7da31d1eb6eab8d5ed70d87d07c747b] 
- RM            [Survey Scores] [] [2014-10-15 16:40:47] [bca3c6529212edfac3e771806c79a908] 
- RM            [Survey Scores] [] [2014-10-15 17:00:14] [261f60062b6e70d0e3f72a6ad4f04654] 
- RM            [Survey Scores] [] [2014-10-15 17:11:53] [69bf0eb8b9b38defaaf4848d8c317571] 
- RM            [Survey Scores] [WS3Q1] [2014-10-15 17:29:43] [1a6d42b46b3d01bc960fcfb45e99fecd] 
- RM            [Survey Scores] [ws3 task 2,3] [2014-10-15 17:57:55] [99723d3e379f668157309b7b2091b15d] 
- RM            [Survey Scores] [WS3 Q1] [2014-10-15 18:06:00] [8523551e1e4e3cbe97fa25692e177b2e] 
- RM            [Survey Scores] [] [2014-10-16 08:33:58] [044144d0728beecdb08e0d94daaff202] 
- RM            [Survey Scores] [] [2014-10-16 13:40:48] [4c4ebb0b36a379d1d949ba77427e658a] 
- RM            [Survey Scores] [task 2 e3] [2014-10-16 15:15:22] [673773038936aef3a5778d7e6bda5c1e] 
- RM            [Survey Scores] [] [2014-10-16 15:21:46] [765bd0d5d4a0c852014c120c6930661d] 
- RM            [Survey Scores] [ws3 6] [2014-10-16 15:23:06] [ce58fb8a0a6d5fe2eedf5e527a9cf2f2] 
- RM            [Survey Scores] [qsfd] [2014-10-16 15:23:35] [118a39334d200089014f927b57d44a19] 

[Truncated]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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=131738&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=131738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=131738&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	1 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

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	1.77	294	7	0.95	142	6	0.92
2	1.99	326	4	0.98	149	2	0.97
3	1.49	252	10	0.92	134	7	0.9
4	1.78	295	6	0.96	148	3	0.96

\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 & 1.77 & 294 & 7 & 0.95 & 142 & 6 & 0.92 \tabularnewline
2 & 1.99 & 326 & 4 & 0.98 & 149 & 2 & 0.97 \tabularnewline
3 & 1.49 & 252 & 10 & 0.92 & 134 & 7 & 0.9 \tabularnewline
4 & 1.78 & 295 & 6 & 0.96 & 148 & 3 & 0.96 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=131738&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]1.77[/C][C]294[/C][C]7[/C][C]0.95[/C][C]142[/C][C]6[/C][C]0.92[/C][/ROW]
[ROW][C]2[/C][C]1.99[/C][C]326[/C][C]4[/C][C]0.98[/C][C]149[/C][C]2[/C][C]0.97[/C][/ROW]
[ROW][C]3[/C][C]1.49[/C][C]252[/C][C]10[/C][C]0.92[/C][C]134[/C][C]7[/C][C]0.9[/C][/ROW]
[ROW][C]4[/C][C]1.78[/C][C]295[/C][C]6[/C][C]0.96[/C][C]148[/C][C]3[/C][C]0.96[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=131738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=131738&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	1.77	294	7	0.95	142	6	0.92
2	1.99	326	4	0.98	149	2	0.97
3	1.49	252	10	0.92	134	7	0.9
4	1.78	295	6	0.96	148	3	0.96

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.989 (0.011)	0.879 (0.121)
(Ps-Ns)/(Ps+Ns)	0.989 (0.011)	1 (0)	0.938 (0.062)
(Pc-Nc)/(Pc+Nc)	0.879 (0.121)	0.938 (0.062)	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) & 0.989 (0.011) & 0.879 (0.121) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.989 (0.011) & 1 (0) & 0.938 (0.062) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.879 (0.121) & 0.938 (0.062) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=131738&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]0.989 (0.011)[/C][C]0.879 (0.121)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.989 (0.011)[/C][C]1 (0)[/C][C]0.938 (0.062)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.879 (0.121)[/C][C]0.938 (0.062)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=131738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=131738&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)	0.989 (0.011)	0.879 (0.121)
(Ps-Ns)/(Ps+Ns)	0.989 (0.011)	1 (0)	0.938 (0.062)
(Pc-Nc)/(Pc+Nc)	0.879 (0.121)	0.938 (0.062)	1 (0)

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

\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) & 1 (0.083) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 1 (0.083) & 1 (0.083) & 1 (0.083) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 1 (0.083) & 1 (0.083) & 1 (0.083) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=131738&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]1 (0.083)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]1 (0.083)[/C][C]1 (0.083)[/C][C]1 (0.083)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]1 (0.083)[/C][C]1 (0.083)[/C][C]1 (0.083)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=131738&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=131738&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 (0.083)	1 (0.083)	1 (0.083)
(Ps-Ns)/(Ps+Ns)	1 (0.083)	1 (0.083)	1 (0.083)
(Pc-Nc)/(Pc+Nc)	1 (0.083)	1 (0.083)	1 (0.083)

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