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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

Thu, 04 Oct 2012 15:09:59 -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/2012/Oct/04/t1349377822kifyw7vnkey953s.htm/, Retrieved Mon, 29 Apr 2024 11:49:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=172082, Retrieved Mon, 29 Apr 2024 11:49:29 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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] [b98453cac15ba1066b407e146608df68]
- R         [Survey Scores] [Survey 2010] [2012-10-04 19:06:57] [0883bf8f4217d775edf6393676d58a73]
-    D          [Survey Scores] [Resultaten survey...] [2012-10-04 19:09:59] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]

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Dataseries X:

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Histogram

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=172082&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]10 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=172082&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=172082&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	10 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	1.14	402	26	0.88	279	23	0.85
2	-0.28	113	204	-0.29	95	148	-0.22
3	1.07	379	27	0.87	258	24	0.83
4	0.18	167	107	0.22	144	85	0.26
5	0.92	344	40	0.79	253	32	0.78
6	1.15	398	19	0.91	273	17	0.88
7	0.6	251	55	0.64	194	45	0.62
8	1.05	373	28	0.86	274	25	0.83
9	-0.49	72	235	-0.53	62	171	-0.47
10	0.98	347	22	0.88	259	21	0.85
11	0.74	273	29	0.81	214	26	0.78
12	0.51	226	57	0.6	194	46	0.62
13	0.61	256	56	0.64	207	49	0.62
14	0.43	203	63	0.53	173	53	0.53
15	0.72	290	52	0.7	222	45	0.66
16	0.36	168	51	0.53	140	48	0.49
17	0.58	234	42	0.7	189	38	0.67
18	0.62	244	39	0.72	195	33	0.71
19	0.54	227	52	0.63	196	44	0.63
20	0.07	147	123	0.09	122	94	0.13

\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 & 1.14 & 402 & 26 & 0.88 & 279 & 23 & 0.85 \tabularnewline
2 & -0.28 & 113 & 204 & -0.29 & 95 & 148 & -0.22 \tabularnewline
3 & 1.07 & 379 & 27 & 0.87 & 258 & 24 & 0.83 \tabularnewline
4 & 0.18 & 167 & 107 & 0.22 & 144 & 85 & 0.26 \tabularnewline
5 & 0.92 & 344 & 40 & 0.79 & 253 & 32 & 0.78 \tabularnewline
6 & 1.15 & 398 & 19 & 0.91 & 273 & 17 & 0.88 \tabularnewline
7 & 0.6 & 251 & 55 & 0.64 & 194 & 45 & 0.62 \tabularnewline
8 & 1.05 & 373 & 28 & 0.86 & 274 & 25 & 0.83 \tabularnewline
9 & -0.49 & 72 & 235 & -0.53 & 62 & 171 & -0.47 \tabularnewline
10 & 0.98 & 347 & 22 & 0.88 & 259 & 21 & 0.85 \tabularnewline
11 & 0.74 & 273 & 29 & 0.81 & 214 & 26 & 0.78 \tabularnewline
12 & 0.51 & 226 & 57 & 0.6 & 194 & 46 & 0.62 \tabularnewline
13 & 0.61 & 256 & 56 & 0.64 & 207 & 49 & 0.62 \tabularnewline
14 & 0.43 & 203 & 63 & 0.53 & 173 & 53 & 0.53 \tabularnewline
15 & 0.72 & 290 & 52 & 0.7 & 222 & 45 & 0.66 \tabularnewline
16 & 0.36 & 168 & 51 & 0.53 & 140 & 48 & 0.49 \tabularnewline
17 & 0.58 & 234 & 42 & 0.7 & 189 & 38 & 0.67 \tabularnewline
18 & 0.62 & 244 & 39 & 0.72 & 195 & 33 & 0.71 \tabularnewline
19 & 0.54 & 227 & 52 & 0.63 & 196 & 44 & 0.63 \tabularnewline
20 & 0.07 & 147 & 123 & 0.09 & 122 & 94 & 0.13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=172082&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]1.14[/C][C]402[/C][C]26[/C][C]0.88[/C][C]279[/C][C]23[/C][C]0.85[/C][/ROW]
[ROW][C]2[/C][C]-0.28[/C][C]113[/C][C]204[/C][C]-0.29[/C][C]95[/C][C]148[/C][C]-0.22[/C][/ROW]
[ROW][C]3[/C][C]1.07[/C][C]379[/C][C]27[/C][C]0.87[/C][C]258[/C][C]24[/C][C]0.83[/C][/ROW]
[ROW][C]4[/C][C]0.18[/C][C]167[/C][C]107[/C][C]0.22[/C][C]144[/C][C]85[/C][C]0.26[/C][/ROW]
[ROW][C]5[/C][C]0.92[/C][C]344[/C][C]40[/C][C]0.79[/C][C]253[/C][C]32[/C][C]0.78[/C][/ROW]
[ROW][C]6[/C][C]1.15[/C][C]398[/C][C]19[/C][C]0.91[/C][C]273[/C][C]17[/C][C]0.88[/C][/ROW]
[ROW][C]7[/C][C]0.6[/C][C]251[/C][C]55[/C][C]0.64[/C][C]194[/C][C]45[/C][C]0.62[/C][/ROW]
[ROW][C]8[/C][C]1.05[/C][C]373[/C][C]28[/C][C]0.86[/C][C]274[/C][C]25[/C][C]0.83[/C][/ROW]
[ROW][C]9[/C][C]-0.49[/C][C]72[/C][C]235[/C][C]-0.53[/C][C]62[/C][C]171[/C][C]-0.47[/C][/ROW]
[ROW][C]10[/C][C]0.98[/C][C]347[/C][C]22[/C][C]0.88[/C][C]259[/C][C]21[/C][C]0.85[/C][/ROW]
[ROW][C]11[/C][C]0.74[/C][C]273[/C][C]29[/C][C]0.81[/C][C]214[/C][C]26[/C][C]0.78[/C][/ROW]
[ROW][C]12[/C][C]0.51[/C][C]226[/C][C]57[/C][C]0.6[/C][C]194[/C][C]46[/C][C]0.62[/C][/ROW]
[ROW][C]13[/C][C]0.61[/C][C]256[/C][C]56[/C][C]0.64[/C][C]207[/C][C]49[/C][C]0.62[/C][/ROW]
[ROW][C]14[/C][C]0.43[/C][C]203[/C][C]63[/C][C]0.53[/C][C]173[/C][C]53[/C][C]0.53[/C][/ROW]
[ROW][C]15[/C][C]0.72[/C][C]290[/C][C]52[/C][C]0.7[/C][C]222[/C][C]45[/C][C]0.66[/C][/ROW]
[ROW][C]16[/C][C]0.36[/C][C]168[/C][C]51[/C][C]0.53[/C][C]140[/C][C]48[/C][C]0.49[/C][/ROW]
[ROW][C]17[/C][C]0.58[/C][C]234[/C][C]42[/C][C]0.7[/C][C]189[/C][C]38[/C][C]0.67[/C][/ROW]
[ROW][C]18[/C][C]0.62[/C][C]244[/C][C]39[/C][C]0.72[/C][C]195[/C][C]33[/C][C]0.71[/C][/ROW]
[ROW][C]19[/C][C]0.54[/C][C]227[/C][C]52[/C][C]0.63[/C][C]196[/C][C]44[/C][C]0.63[/C][/ROW]
[ROW][C]20[/C][C]0.07[/C][C]147[/C][C]123[/C][C]0.09[/C][C]122[/C][C]94[/C][C]0.13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=172082&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=172082&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	1.14	402	26	0.88	279	23	0.85
2	-0.28	113	204	-0.29	95	148	-0.22
3	1.07	379	27	0.87	258	24	0.83
4	0.18	167	107	0.22	144	85	0.26
5	0.92	344	40	0.79	253	32	0.78
6	1.15	398	19	0.91	273	17	0.88
7	0.6	251	55	0.64	194	45	0.62
8	1.05	373	28	0.86	274	25	0.83
9	-0.49	72	235	-0.53	62	171	-0.47
10	0.98	347	22	0.88	259	21	0.85
11	0.74	273	29	0.81	214	26	0.78
12	0.51	226	57	0.6	194	46	0.62
13	0.61	256	56	0.64	207	49	0.62
14	0.43	203	63	0.53	173	53	0.53
15	0.72	290	52	0.7	222	45	0.66
16	0.36	168	51	0.53	140	48	0.49
17	0.58	234	42	0.7	189	38	0.67
18	0.62	244	39	0.72	195	33	0.71
19	0.54	227	52	0.63	196	44	0.63
20	0.07	147	123	0.09	122	94	0.13

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.961 (0)	0.961 (0)
(Ps-Ns)/(Ps+Ns)	0.961 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.961 (0)	0.999 (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) & 0.961 (0) & 0.961 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.961 (0) & 1 (0) & 0.999 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.961 (0) & 0.999 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=172082&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]0.961 (0)[/C][C]0.961 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.961 (0)[/C][C]1 (0)[/C][C]0.999 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.961 (0)[/C][C]0.999 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=172082&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=172082&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)	0.961 (0)	0.961 (0)
(Ps-Ns)/(Ps+Ns)	0.961 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.961 (0)	0.999 (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)	0.926 (0)	0.899 (0)
(Ps-Ns)/(Ps+Ns)	0.926 (0)	1 (0)	0.962 (0)
(Pc-Nc)/(Pc+Nc)	0.899 (0)	0.962 (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) & 0.926 (0) & 0.899 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.926 (0) & 1 (0) & 0.962 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.899 (0) & 0.962 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=172082&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]0.926 (0)[/C][C]0.899 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.926 (0)[/C][C]1 (0)[/C][C]0.962 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.899 (0)[/C][C]0.962 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=172082&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=172082&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)	0.926 (0)	0.899 (0)
(Ps-Ns)/(Ps+Ns)	0.926 (0)	1 (0)	0.962 (0)
(Pc-Nc)/(Pc+Nc)	0.899 (0)	0.962 (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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code