Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_surveyscores.wasp
Title produced by softwareSurvey Scores
Date of computationSun, 03 Oct 2010 16:55:42 +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/03/t1286125834b31k3l1ml6js8dp.htm/, Retrieved Thu, 31 Oct 2024 23:22:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=80353, Retrieved Thu, 31 Oct 2024 23:22:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact611
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Survey Scores] [E-Learn 2008 table 1] [2008-09-07 11:33:54] [b98453cac15ba1066b407e146608df68]
F RM D  [Survey Scores] [ATTLES] [2010-04-05 12:47:13] [b98453cac15ba1066b407e146608df68]
F    D      [Survey Scores] [ATTLES Scores] [2010-10-03 16:55:42] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-             [Survey Scores] [Task 5 (b)] [2010-10-12 14:47:16] [247f085ab5b7724f755ad01dc754a3e8]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 1] [2011-10-06 15:56:44] [c53df38315e3cbde2dbe0de809195ef2]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 1] [2011-10-06 15:56:44] [c53df38315e3cbde2dbe0de809195ef2]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 1] [2011-10-06 15:56:44] [c53df38315e3cbde2dbe0de809195ef2]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 1] [2011-10-06 15:56:44] [c53df38315e3cbde2dbe0de809195ef2]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 1] [2011-10-06 15:56:44] [c53df38315e3cbde2dbe0de809195ef2]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 3] [2011-10-06 16:14:04] [c53df38315e3cbde2dbe0de809195ef2]
- R           [Survey Scores] [Workshop 2 - Task 5] [2011-10-06 16:46:07] [fbaf17a8836493f6de0f4e0e997711e1]
- RM          [Survey Scores] [] [2011-10-06 20:55:02] [ee8c3a74bf3b349877806e9a50913c60]
- RM          [Survey Scores] [Week 2 Opdracht 5b] [2011-10-09 08:49:22] [d2d464c5b110c95dc0c66eb9ae81f8ec]
- R           [Survey Scores] [taak 5] [2011-10-10 07:47:04] [c4580079d5d2b3f0ba412f27cdc441be]
- RM          [Survey Scores] [opdracht 5-2] [2011-10-10 18:54:55] [8fcdd1f5b88bf5ac5d2a0b8a91219b89]
- RM          [Survey Scores] [] [2011-10-11 08:16:15] [ad2d4c5ace9fa07b356a7b5098237581]
- R           [Survey Scores] [] [2011-10-11 09:46:12] [18e0b15711387f6270134133fa101957]
- RMPD        [Exercise 1.13] [Task 2] [2011-10-11 14:26:01] [0f81819b439c6e991d1a2004e9982756]
- RMPD        [Exercise 1.13] [Task 3] [2011-10-11 14:26:37] [0f81819b439c6e991d1a2004e9982756]
- RMP         [Kendall tau Correlation Matrix] [test] [2011-10-11 14:47:26] [25b6caf3839c2bdc14961e5bff2d6373]
- R           [Survey Scores] [Workshop 2 Task 5.2] [2011-10-11 15:07:13] [59e9c089bdd600b584669dddc48fbcc3]
- RMPD        [Exercise 1.13] [] [2011-10-11 15:22:50] [a1957df0bc37aec4aa3c994e6a08412c]
- R           [Survey Scores] [] [2011-10-11 15:27:31] [a1957df0bc37aec4aa3c994e6a08412c]
- RMPD        [Exercise 1.13] [OpdebeeckMaarten12] [2011-10-11 18:29:02] [df691308a413f42c104a3a322de89bb4]
-               [Exercise 1.13] [opdebeeckmaarten13] [2011-10-11 18:37:18] [df691308a413f42c104a3a322de89bb4]
- RM          [Survey Scores] [Task 4: new data] [2011-10-11 18:57:45] [e51846b5e808727784baa8d5c183dcd5]
- R           [Survey Scores] [Workshop 2 opdrac...] [2011-10-11 22:02:45] [43a0606d8103c0ba382f0586f4417c48]
- RM          [Survey Scores] [ATTLES Scores] [2011-12-22 15:08:59] [1321c14511baa35aebbc5dda661708fe]
- R           [Survey Scores] [Survey 2010] [2012-10-04 19:06:57] [0883bf8f4217d775edf6393676d58a73]
-    D          [Survey Scores] [Resultaten survey...] [2012-10-04 19:09:59] [0883bf8f4217d775edf6393676d58a73]
- RM          [Survey Scores] [WS2 - taak5b] [2012-10-05 11:06:41] [8ce6c7315af51b5eb6923c5fe455d382]
- RMPD        [Notched Boxplots] [WS2: Task 6] [2012-10-06 10:14:24] [edf0418499cd31d27dbea8ea1d30b3db]
- R           [Survey Scores] [Task 5.2] [2012-10-06 11:28:00] [9d6050326bbbd058eed49c2dec5f39c1]
-               [Survey Scores] [Taak 5] [2012-10-08 19:30:45] [c5e00e3d2459b4cd6380f7873395bbc5]
- R           [Survey Scores] [Taak 5] [2012-10-08 15:58:52] [c5e00e3d2459b4cd6380f7873395bbc5]
- R           [Survey Scores] [WS2: Taak 5-2] [2012-10-09 07:38:32] [b43eb6e2e60f3928e6b8367ff6c5b484]
- RM          [Survey Scores] [connected & separ...] [2012-10-09 08:30:10] [87b90d598c60c012567ac118c8f3a654]
- RMPD        [Exercise 1.13] [Workshop 2 - Task 2] [2012-10-09 10:57:42] [7073e6f3275d7ce0870c213648af111a]
- R           [Survey Scores] [workshop 2 Taak 5] [2012-10-09 21:15:38] [081ff4808467d7c84e980fa7f896f721]
- R           [Survey Scores] [workshop 2 Taak 5...] [2012-10-09 21:27:36] [081ff4808467d7c84e980fa7f896f721]
- R           [Survey Scores] [Paper 4] [2012-11-30 22:28:17] [f4f48270c576c45d216b84daa061a85b]
- RMPD        [Exercise 1.13] [Workshop 2 vraag 1] [2013-10-11 12:14:52] [ca911141a7ec8daf372faa3c5d9535e0]
- RMPD        [Exercise 1.13] [Worshop 2 vraag 3] [2013-10-11 12:34:03] [ca911141a7ec8daf372faa3c5d9535e0]
- RMP         [Notched Boxplots] [Notched Boxplots ...] [2013-10-12 14:28:22] [74be16979710d4c4e7c6647856088456]
- R           [Survey Scores] [Connected and sep...] [2013-10-13 14:49:02] [e39a9fdb43d44bc7a3500a1d00251334]
- RMPD        [Exercise 1.13] [Task 3: Probabili...] [2013-10-14 16:37:09] [e62a289106a5b580d3faaf52f3fb6acb]
- RM          [Survey Scores] [Workshop 2 - vraag 5] [2013-10-15 08:52:31] [6e6101806504d86600f236fbba4f8aec]
- R           [Survey Scores] [WS 2 Vraag 5.1] [2013-10-15 09:15:28] [d1af3aceda32c4de5f8ef20fdfdffdc9]
- RM          [Survey Scores] [Workshop 2 - Task 5] [2013-10-15 12:25:05] [fc82b8c760ed89156a53b254f8e95ae0]
- RM          [Survey Scores] [Workshop 2 task 5] [2013-10-15 12:46:41] [f12bfb29749f0c3f544bf278d0782c85]
- RMPD        [Pearson Correlation] [WS2.5a] [2013-10-15 18:48:18] [31634b7e94db88df109226f71dc63e83]

[Truncated]
Feedback Forum
2010-10-13 16:29:41 [Pascal Wijnen] [reply
Daar hier over alle antwoorden te discuteren valt, kunnen we niet echt een goed of fout antwoord geven. Zelf kom ik ook op de gedachte dat er een verschil is tussen de groepen, en dat groep 2 negatiever staat ten opzichte van groep 1. Ook vind ik groep 1 meer connected.

Post a new message
Dataseries X:
5	3	5	3	4	4	4	4	4	5	5	4	4	3	4	3	4	4	4	3
4	4	4	3	5	5	4	4	3	3	3	3	2	4	4	3	3	3	4	3
3	4	4	2	4	2	4	4	1	2	3	3	4	4	4	2	3	4	4	4
4	2	4	2	2	4	4	4	2	3	4	2	4	3	4	3	3	4	3	3
4	5	3	1	4	2	4	4	2	5	2	4	2	5	4	3	5	4	4	4
3	5	4	4	4	4	5	3	2	1	1	4	1	5	4	1	2	3	3	5
4	5	5	2	4	4	3	4	5	3	2	4	2	4	3	4	1	4	4	3
4	2	4	2	3	4	4	5	2	4	4	4	3	4	3	3	4	4	4	3
4	4	3	4	3	5	4	3	2	4	5	4	3	3	4	4	3	2	3	4
4	3	4	5	4	5	4	3	3	2	5	4	4	3	3	4	5	2	4	4
4	4	5	3	5	5	5	3	1	3	5	3	4	5	2	1	3	3	4	1
5	4	4	3	3	3	3	4	3	4	3	4	3	4	4	4	4	3	2	3
4	3	5	2	4	4	5	4	3	4	4	3	3	4	4	3	4	4	4	2
4	5	4	3	4	4	5	4	2	4	2	4	4	4	4	5	4	4	3	4
4	2	4	2	2	5	2	4	3	5	4	3	5	4	4	3	4	4	4	2
4	2	3	3	4	5	3	4	1	3	5	3	5	2	3	3	3	4	3	2
4	3	4	4	4	2	5	4	2	4	4	4	2	2	2	4	4	4	2	4
5	2	4	3	4	4	3	5	3	5	5	2	4	4	4	4	5	4	4	2
5	2	5	2	3	5	4	5	3	5	4	3	3	5	5	3	2	5	5	3
3	4	2	3	4	4	4	3	2	3	2	4	4	3	2	3	4	4	3	3
4	2	4	2	4	5	3	4	2	2	5	2	2	3	4	3	4	4	4	2
4	5	2	2	3	2	4	2	3	4	2	3	2	5	3	2	4	4	2	4
4	5	4	3	4	4	4	3	4	4	2	4	2	5	4	4	4	5	4	4
5	2	4	3	4	4	5	4	2	4	4	4	4	4	4	4	4	3	4	4
5	5	4	2	3	3	5	5	2	5	4	2	4	3	4	2	3	4	4	2
2	5	4	4	4	5	5	4	4	4	1	2	4	2	2	4	4	5	4	4
4	4	4	2	4	5	3	4	1	5	4	3	4	4	3	4	4	4	3	2
4	4	3	3	2	3	4	4	3	3	3	4	3	4	2	4	5	4	4	4
2	4	4	3	4	4	4	2	2	4	3	4	4	3	4	4	4	3	2	2
4	3	4	3	4	4	2	4	2	4	4	3	4	3	4	2	3	4	4	2
5	4	4	1	2	3	3	3	1	5	NA	4	4	4	4	3	4	2	2	1
3	2	5	1	1	2	4	4	2	3	4	3	3	2	4	3	2	4	4	3
4	4	3	4	4	4	4	2	4	4	2	3	2	2	4	4	4	4	3	3
4	3	4	3	2	4	4	3	3	4	3	4	4	4	4	2	4	4	4	4
4	2	4	4	3	4	5	2	2	4	2	1	4	2	4	4	4	3	2	4
4	2	4	3	3	4	2	3	2	5	4	2	4	4	2	4	3	4	4	2
3	1	4	3	3	4	3	4	1	3	4	3	4	1	4	4	2	4	4	1
5	2	4	3	4	4	4	3	3	4	3	4	4	2	4	3	4	3	4	2
4	4	3	2	3	3	3	2	1	4	3	3	4	3	3	4	4	3	2	2
4	2	4	2	4	4	4	4	3	4	4	3	4	3	4	3	3	3	4	2
5	4	5	3	3	4	4	4	2	3	4	3	4	3	4	2	4	4	2	2
4	2	4	3	4	4	3	4	2	4	4	4	2	4	2	3	4	4	2	4
5	3	4	4	4	4	4	4	2	4	4	4	3	3	3	1	3	4	4	3
4	4	2	5	3	3	5	3	2	4	3	3	3	4	3	4	3	3	4	3
2	4	4	5	4	4	4	4	2	5	4	4	2	4	1	2	4	1	2	4
5	4	3	3	2	4	4	4	4	4	4	3	4	4	2	3	4	4	4	3
4	2	4	4	5	4	4	4	2	5	4	4	4	4	5	2	4	4	4	4
4	3	4	3	3	3	4	3	2	4	4	4	4	3	3	3	4	3	4	2
4	3	4	3	3	4	4	4	3	4	5	4	4	3	4	3	4	4	4	3
4	3	5	3	4	4	3	4	3	5	4	3	4	4	3	2	4	4	2	2
4	3	2	2	4	4	4	4	2	3	4	3	4	4	4	3	4	4	4	4
4	2	4	3	3	4	3	3	2	4	3	3	4	2	3	3	3	4	3	2
4	3	5	2	5	2	4	4	1	2	4	2	3	2	4	2	4	4	4	4
4	4	4	3	2	2	3	5	3	4	4	4	4	3	4	4	4	4	4	3
2	5	3	2	3	4	4	4	2	3	3	4	4	4	2	4	4	3	2	2
4	5	4	4	5	3	2	4	1	5	5	3	4	4	5	NA	3	3	3	2
5	4	4	2	4	3	4	4	4	5	3	4	1	4	3	3	4	4	4	4
4	3	4	2	2	2	4	4	1	3	4	2	4	4	4	3	4	4	2	3
4	3	4	3	4	4	3	4	4	4	4	4	4	3	3	4	4	3	3	4
4	2	4	2	4	4	4	4	2	5	4	3	3	4	3	4	4	3	4	2
4	1	3	3	4	4	3	4	1	3	2	4	3	1	4	2	4	3	2	3
4	4	3	4	4	4	5	3	4	3	3	4	4	2	2	5	4	4	3	3
5	1	3	2	4	4	3	5	2	5	4	2	5	2	4	3	4	4	4	3
4	3	2	2	2	4	5	3	1	5	4	4	2	3	4	3	4	4	3	4
4	2	3	2	3	3	5	4	3	5	3	2	3	4	4	3	4	1	3	4
5	4	3	2	3	4	4	4	1	5	4	4	4	4	4	3	4	4	4	2
4	3	4	2	4	4	3	4	4	4	4	4	3	4	3	3	3	4	3	4
4	2	4	4	2	4	3	4	1	2	4	1	4	3	3	3	3	3	2	1
4	3	4	4	5	4	4	4	3	4	4	4	3	4	5	4	4	4	4	4
4	2	4	4	3	4	4	4	2	4	4	4	4	3	4	3	4	4	4	3
4	2	4	5	4	5	4	3	3	4	4	4	4	4	3	4	3	4	3	3
4	4	3	1	3	3	3	4	2	4	4	4	4	4	4	3	4	4	3	4
2	2	4	3	4	4	4	4	2	5	5	4	3	5	5	3	3	4	3	4
5	3	5	2	3	5	4	5	1	5	3	3	5	4	5	4	4	5	5	3
4	2	4	3	3	4	4	4	2	4	4	2	4	2	2	3	3	3	2	2
3	4	5	5	5	5	4	4	2	2	3	4	2	4	4	2	3	1	3	4
4	2	4	2	5	5	4	3	4	4	5	4	2	4	4	4	3	3	4	4
3	2	4	4	4	4	3	4	2	4	3	3	3	3	3	3	4	3	3	3
2	4	2	2	3	4	4	2	3	2	4	2	2	4	4	4	2	3	4	2
4	2	4	4	5	5	3	4	2	4	3	3	4	2	3	2	2	4	2	2
3	2	4	3	2	4	4	4	3	4	4	4	4	3	4	3	2	4	4	4
5	3	4	3	5	4	4	4	2	3	4	4	5	3	4	4	3	5	4	2
4	2	4	4	4	4	3	4	2	4	4	4	4	4	4	2	4	4	4	3
4	2	4	3	4	4	5	4	3	4	4	3	5	2	4	2	3	4	4	2
4	2	3	2	4	4	3	4	2	4	4	3	4	4	2	4	4	4	3	2
4	2	3	2	3	4	5	4	2	4	4	3	4	2	2	4	4	4	2	2
4	3	4	4	5	4	4	4	2	4	3	4	4	4	4	4	4	4	4	4
4	3	4	2	4	3	4	4	2	3	4	4	4	4	3	3	4	3	3	2
4	3	3	3	3	3	3	2	1	4	4	3	4	3	4	3	3	3	2	3
4	3	4	3	4	4	2	4	1	4	4	3	5	2	2	4	3	4	4	2
5	1	4	1	3	4	3	4	1	5	4	3	3	5	4	3	4	4	3	3
5	4	5	5	1	3	1	5	3	4	3	3	3	4	2	4	4	3	3	3
4	3	5	2	3	4	4	4	2	4	4	4	5	4	5	4	4	4	4	3
4	4	3	2	3	2	4	4	2	4	2	3	3	4	2	4	3	2	2	3
2	2	4	2	4	4	4	2	1	4	4	4	2	3	3	2	4	4	2	2
4	1	5	3	4	5	4	5	3	5	5	4	4	3	4	3	4	4	4	1
3	4	4	3	4	4	4	4	3	4	3	4	2	4	3	4	4	4	3	4
4	4	3	2	3	4	5	3	3	4	4	4	2	4	3	3	4	3	2	2
5	3	4	3	4	4	3	4	3	4	3	3	4	4	3	3	4	3	3	4
4	2	5	3	4	4	2	3	2	3	5	4	4	4	4	2	3	4	4	2
4	4	4	2	4	3	4	4	4	5	3	4	2	2	4	4	5	4	4	4
4	4	5	3	1	4	3	5	3	5	4	5	3	1	4	5	4	3	2	4
4	4	4	2	3	5	4	5	3	2	4	5	2	5	4	2	5	2	4	4
4	2	2	3	3	4	4	4	2	4	3	2	4	4	3	2	4	2	2	2
4	3	3	2	3	4	4	4	2	4	5	4	4	4	3	4	4	3	4	4
3	4	5	4	5	5	4	5	1	4	3	4	4	2	2	2	4	4	4	3
5	2	4	4	5	4	4	4	2	4	4	5	4	2	4	4	4	4	2	2
4	5	5	5	4	5	2	4	2	5	4	4	4	3	4	4	2	5	5	4
4	2	4	4	4	4	4	2	3	5	4	2	4	2	4	4	5	4	4	2
5	5	5	3	1	4	5	5	5	5	3	5	3	5	4	5	5	3	4	5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80353&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]2 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=80353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80353&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 time2 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)
10.9711470.889370.86
20.0855460.0943410.02
30.8610270.878370.84
4-0.123447-0.162742-0.22
50.5276190.663150.62
60.8710590.848690.81
70.759290.827580.81
80.789590.838290.8
9-0.661588-0.711368-0.68
100.92111100.838390.8
110.6585140.7271120.71
120.4163180.5659160.57
130.4775230.5367210.52
140.470260.4660230.45
150.4872190.5865180.57
160.2149260.3145230.32
170.6582100.787490.78
180.5977120.737190.78
190.3462250.4359250.4
20-0.054146-0.063941-0.02

\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 & 0.97 & 114 & 7 & 0.88 & 93 & 7 & 0.86 \tabularnewline
2 & 0.08 & 55 & 46 & 0.09 & 43 & 41 & 0.02 \tabularnewline
3 & 0.86 & 102 & 7 & 0.87 & 83 & 7 & 0.84 \tabularnewline
4 & -0.12 & 34 & 47 & -0.16 & 27 & 42 & -0.22 \tabularnewline
5 & 0.52 & 76 & 19 & 0.6 & 63 & 15 & 0.62 \tabularnewline
6 & 0.87 & 105 & 9 & 0.84 & 86 & 9 & 0.81 \tabularnewline
7 & 0.75 & 92 & 9 & 0.82 & 75 & 8 & 0.81 \tabularnewline
8 & 0.78 & 95 & 9 & 0.83 & 82 & 9 & 0.8 \tabularnewline
9 & -0.66 & 15 & 88 & -0.71 & 13 & 68 & -0.68 \tabularnewline
10 & 0.92 & 111 & 10 & 0.83 & 83 & 9 & 0.8 \tabularnewline
11 & 0.65 & 85 & 14 & 0.72 & 71 & 12 & 0.71 \tabularnewline
12 & 0.41 & 63 & 18 & 0.56 & 59 & 16 & 0.57 \tabularnewline
13 & 0.47 & 75 & 23 & 0.53 & 67 & 21 & 0.52 \tabularnewline
14 & 0.4 & 70 & 26 & 0.46 & 60 & 23 & 0.45 \tabularnewline
15 & 0.48 & 72 & 19 & 0.58 & 65 & 18 & 0.57 \tabularnewline
16 & 0.21 & 49 & 26 & 0.31 & 45 & 23 & 0.32 \tabularnewline
17 & 0.65 & 82 & 10 & 0.78 & 74 & 9 & 0.78 \tabularnewline
18 & 0.59 & 77 & 12 & 0.73 & 71 & 9 & 0.78 \tabularnewline
19 & 0.34 & 62 & 25 & 0.43 & 59 & 25 & 0.4 \tabularnewline
20 & -0.05 & 41 & 46 & -0.06 & 39 & 41 & -0.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80353&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]0.97[/C][C]114[/C][C]7[/C][C]0.88[/C][C]93[/C][C]7[/C][C]0.86[/C][/ROW]
[ROW][C]2[/C][C]0.08[/C][C]55[/C][C]46[/C][C]0.09[/C][C]43[/C][C]41[/C][C]0.02[/C][/ROW]
[ROW][C]3[/C][C]0.86[/C][C]102[/C][C]7[/C][C]0.87[/C][C]83[/C][C]7[/C][C]0.84[/C][/ROW]
[ROW][C]4[/C][C]-0.12[/C][C]34[/C][C]47[/C][C]-0.16[/C][C]27[/C][C]42[/C][C]-0.22[/C][/ROW]
[ROW][C]5[/C][C]0.52[/C][C]76[/C][C]19[/C][C]0.6[/C][C]63[/C][C]15[/C][C]0.62[/C][/ROW]
[ROW][C]6[/C][C]0.87[/C][C]105[/C][C]9[/C][C]0.84[/C][C]86[/C][C]9[/C][C]0.81[/C][/ROW]
[ROW][C]7[/C][C]0.75[/C][C]92[/C][C]9[/C][C]0.82[/C][C]75[/C][C]8[/C][C]0.81[/C][/ROW]
[ROW][C]8[/C][C]0.78[/C][C]95[/C][C]9[/C][C]0.83[/C][C]82[/C][C]9[/C][C]0.8[/C][/ROW]
[ROW][C]9[/C][C]-0.66[/C][C]15[/C][C]88[/C][C]-0.71[/C][C]13[/C][C]68[/C][C]-0.68[/C][/ROW]
[ROW][C]10[/C][C]0.92[/C][C]111[/C][C]10[/C][C]0.83[/C][C]83[/C][C]9[/C][C]0.8[/C][/ROW]
[ROW][C]11[/C][C]0.65[/C][C]85[/C][C]14[/C][C]0.72[/C][C]71[/C][C]12[/C][C]0.71[/C][/ROW]
[ROW][C]12[/C][C]0.41[/C][C]63[/C][C]18[/C][C]0.56[/C][C]59[/C][C]16[/C][C]0.57[/C][/ROW]
[ROW][C]13[/C][C]0.47[/C][C]75[/C][C]23[/C][C]0.53[/C][C]67[/C][C]21[/C][C]0.52[/C][/ROW]
[ROW][C]14[/C][C]0.4[/C][C]70[/C][C]26[/C][C]0.46[/C][C]60[/C][C]23[/C][C]0.45[/C][/ROW]
[ROW][C]15[/C][C]0.48[/C][C]72[/C][C]19[/C][C]0.58[/C][C]65[/C][C]18[/C][C]0.57[/C][/ROW]
[ROW][C]16[/C][C]0.21[/C][C]49[/C][C]26[/C][C]0.31[/C][C]45[/C][C]23[/C][C]0.32[/C][/ROW]
[ROW][C]17[/C][C]0.65[/C][C]82[/C][C]10[/C][C]0.78[/C][C]74[/C][C]9[/C][C]0.78[/C][/ROW]
[ROW][C]18[/C][C]0.59[/C][C]77[/C][C]12[/C][C]0.73[/C][C]71[/C][C]9[/C][C]0.78[/C][/ROW]
[ROW][C]19[/C][C]0.34[/C][C]62[/C][C]25[/C][C]0.43[/C][C]59[/C][C]25[/C][C]0.4[/C][/ROW]
[ROW][C]20[/C][C]-0.05[/C][C]41[/C][C]46[/C][C]-0.06[/C][C]39[/C][C]41[/C][C]-0.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80353&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)
10.9711470.889370.86
20.0855460.0943410.02
30.8610270.878370.84
4-0.123447-0.162742-0.22
50.5276190.663150.62
60.8710590.848690.81
70.759290.827580.81
80.789590.838290.8
9-0.661588-0.711368-0.68
100.92111100.838390.8
110.6585140.7271120.71
120.4163180.5659160.57
130.4775230.5367210.52
140.470260.4660230.45
150.4872190.5865180.57
160.2149260.3145230.32
170.6582100.787490.78
180.5977120.737190.78
190.3462250.4359250.4
20-0.054146-0.063941-0.02







Pearson correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.985 (0)0.977 (0)
(Ps-Ns)/(Ps+Ns)0.985 (0)1 (0)0.997 (0)
(Pc-Nc)/(Pc+Nc)0.977 (0)0.997 (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) & 0.985 (0) & 0.977 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.985 (0) & 1 (0) & 0.997 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.977 (0) & 0.997 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80353&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]0.985 (0)[/C][C]0.977 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.985 (0)[/C][C]1 (0)[/C][C]0.997 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.977 (0)[/C][C]0.997 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80353&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80353&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)0.985 (0)0.977 (0)
(Ps-Ns)/(Ps+Ns)0.985 (0)1 (0)0.997 (0)
(Pc-Nc)/(Pc+Nc)0.977 (0)0.997 (0)1 (0)







Kendall tau rank correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.942 (0)0.912 (0)
(Ps-Ns)/(Ps+Ns)0.942 (0)1 (0)0.971 (0)
(Pc-Nc)/(Pc+Nc)0.912 (0)0.971 (0)1 (0)

\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) & 0.942 (0) & 0.912 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.942 (0) & 1 (0) & 0.971 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.912 (0) & 0.971 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80353&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]0.942 (0)[/C][C]0.912 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.942 (0)[/C][C]1 (0)[/C][C]0.971 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.912 (0)[/C][C]0.971 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80353&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80353&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)0.942 (0)0.912 (0)
(Ps-Ns)/(Ps+Ns)0.942 (0)1 (0)0.971 (0)
(Pc-Nc)/(Pc+Nc)0.912 (0)0.971 (0)1 (0)



Parameters (Session):
par1 = 1 2 3 4 5 ;
Parameters (R input):
par1 = 1 2 3 4 5 ;
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')