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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, 09 Oct 2012 03:37:38 -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/09/t134976827531kz8shixava2fi.htm/, Retrieved Tue, 10 Feb 2026 08:40:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=174447, Retrieved Tue, 10 Feb 2026 08:40:13 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

372

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]
- R         [Survey Scores] [WS2: Taak 5] [2012-10-09 07:37:38] [ed5db9d6207bcb51aca69986e23f030b] [Current]

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Dataseries X:

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Histogram

Boxplots

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	2	3	2	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
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
3	4	5	4	3	4	2	5	3	2	4	4	2	3	2	NA	NA	NA	NA	NA
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	2	2	3	NA	NA	NA	3	3	3	3	3	NA	3	3	3	3	3	4	4
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	5	4	4	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
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	9 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 & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=174447&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]9 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=174447&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=174447&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	9 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	407	26	0.88	282	23	0.85
2	-0.27	116	206	-0.28	97	150	-0.21
3	1.07	382	28	0.86	260	25	0.82
4	0.18	169	108	0.22	146	86	0.26
5	0.92	346	40	0.79	254	32	0.78
6	1.15	399	19	0.91	274	17	0.88
7	0.59	251	56	0.64	194	46	0.62
8	1.05	375	28	0.86	275	25	0.83
9	-0.49	72	235	-0.53	62	171	-0.47
10	0.98	347	23	0.88	259	22	0.84
11	0.74	274	29	0.81	215	26	0.78
12	0.51	227	57	0.6	195	46	0.62
13	0.6	256	57	0.64	207	50	0.61
14	0.42	203	63	0.53	173	53	0.53
15	0.72	290	53	0.69	222	46	0.66
16	0.35	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	228	52	0.63	197	44	0.63
20	0.08	148	123	0.09	123	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 & 407 & 26 & 0.88 & 282 & 23 & 0.85 \tabularnewline
2 & -0.27 & 116 & 206 & -0.28 & 97 & 150 & -0.21 \tabularnewline
3 & 1.07 & 382 & 28 & 0.86 & 260 & 25 & 0.82 \tabularnewline
4 & 0.18 & 169 & 108 & 0.22 & 146 & 86 & 0.26 \tabularnewline
5 & 0.92 & 346 & 40 & 0.79 & 254 & 32 & 0.78 \tabularnewline
6 & 1.15 & 399 & 19 & 0.91 & 274 & 17 & 0.88 \tabularnewline
7 & 0.59 & 251 & 56 & 0.64 & 194 & 46 & 0.62 \tabularnewline
8 & 1.05 & 375 & 28 & 0.86 & 275 & 25 & 0.83 \tabularnewline
9 & -0.49 & 72 & 235 & -0.53 & 62 & 171 & -0.47 \tabularnewline
10 & 0.98 & 347 & 23 & 0.88 & 259 & 22 & 0.84 \tabularnewline
11 & 0.74 & 274 & 29 & 0.81 & 215 & 26 & 0.78 \tabularnewline
12 & 0.51 & 227 & 57 & 0.6 & 195 & 46 & 0.62 \tabularnewline
13 & 0.6 & 256 & 57 & 0.64 & 207 & 50 & 0.61 \tabularnewline
14 & 0.42 & 203 & 63 & 0.53 & 173 & 53 & 0.53 \tabularnewline
15 & 0.72 & 290 & 53 & 0.69 & 222 & 46 & 0.66 \tabularnewline
16 & 0.35 & 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 & 228 & 52 & 0.63 & 197 & 44 & 0.63 \tabularnewline
20 & 0.08 & 148 & 123 & 0.09 & 123 & 94 & 0.13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=174447&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]407[/C][C]26[/C][C]0.88[/C][C]282[/C][C]23[/C][C]0.85[/C][/ROW]
[ROW][C]2[/C][C]-0.27[/C][C]116[/C][C]206[/C][C]-0.28[/C][C]97[/C][C]150[/C][C]-0.21[/C][/ROW]
[ROW][C]3[/C][C]1.07[/C][C]382[/C][C]28[/C][C]0.86[/C][C]260[/C][C]25[/C][C]0.82[/C][/ROW]
[ROW][C]4[/C][C]0.18[/C][C]169[/C][C]108[/C][C]0.22[/C][C]146[/C][C]86[/C][C]0.26[/C][/ROW]
[ROW][C]5[/C][C]0.92[/C][C]346[/C][C]40[/C][C]0.79[/C][C]254[/C][C]32[/C][C]0.78[/C][/ROW]
[ROW][C]6[/C][C]1.15[/C][C]399[/C][C]19[/C][C]0.91[/C][C]274[/C][C]17[/C][C]0.88[/C][/ROW]
[ROW][C]7[/C][C]0.59[/C][C]251[/C][C]56[/C][C]0.64[/C][C]194[/C][C]46[/C][C]0.62[/C][/ROW]
[ROW][C]8[/C][C]1.05[/C][C]375[/C][C]28[/C][C]0.86[/C][C]275[/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]23[/C][C]0.88[/C][C]259[/C][C]22[/C][C]0.84[/C][/ROW]
[ROW][C]11[/C][C]0.74[/C][C]274[/C][C]29[/C][C]0.81[/C][C]215[/C][C]26[/C][C]0.78[/C][/ROW]
[ROW][C]12[/C][C]0.51[/C][C]227[/C][C]57[/C][C]0.6[/C][C]195[/C][C]46[/C][C]0.62[/C][/ROW]
[ROW][C]13[/C][C]0.6[/C][C]256[/C][C]57[/C][C]0.64[/C][C]207[/C][C]50[/C][C]0.61[/C][/ROW]
[ROW][C]14[/C][C]0.42[/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]53[/C][C]0.69[/C][C]222[/C][C]46[/C][C]0.66[/C][/ROW]
[ROW][C]16[/C][C]0.35[/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]228[/C][C]52[/C][C]0.63[/C][C]197[/C][C]44[/C][C]0.63[/C][/ROW]
[ROW][C]20[/C][C]0.08[/C][C]148[/C][C]123[/C][C]0.09[/C][C]123[/C][C]94[/C][C]0.13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=174447&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=174447&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	407	26	0.88	282	23	0.85
2	-0.27	116	206	-0.28	97	150	-0.21
3	1.07	382	28	0.86	260	25	0.82
4	0.18	169	108	0.22	146	86	0.26
5	0.92	346	40	0.79	254	32	0.78
6	1.15	399	19	0.91	274	17	0.88
7	0.59	251	56	0.64	194	46	0.62
8	1.05	375	28	0.86	275	25	0.83
9	-0.49	72	235	-0.53	62	171	-0.47
10	0.98	347	23	0.88	259	22	0.84
11	0.74	274	29	0.81	215	26	0.78
12	0.51	227	57	0.6	195	46	0.62
13	0.6	256	57	0.64	207	50	0.61
14	0.42	203	63	0.53	173	53	0.53
15	0.72	290	53	0.69	222	46	0.66
16	0.35	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	228	52	0.63	197	44	0.63
20	0.08	148	123	0.09	123	94	0.13

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.959 (0)	0.959 (0)
(Ps-Ns)/(Ps+Ns)	0.959 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.959 (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) & 0.959 (0) & 0.959 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.959 (0) & 1 (0) & 0.999 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.959 (0) & 0.999 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=174447&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]0.959 (0)[/C][C]0.959 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.959 (0)[/C][C]1 (0)[/C][C]0.999 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.959 (0)[/C][C]0.999 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=174447&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=174447&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)	0.959 (0)	0.959 (0)
(Ps-Ns)/(Ps+Ns)	0.959 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.959 (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)	0.915 (0)	0.878 (0)
(Ps-Ns)/(Ps+Ns)	0.915 (0)	1 (0)	0.952 (0)
(Pc-Nc)/(Pc+Nc)	0.878 (0)	0.952 (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) & 0.915 (0) & 0.878 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.915 (0) & 1 (0) & 0.952 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.878 (0) & 0.952 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=174447&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]0.915 (0)[/C][C]0.878 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.915 (0)[/C][C]1 (0)[/C][C]0.952 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.878 (0)[/C][C]0.952 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=174447&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=174447&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)	0.915 (0)	0.878 (0)
(Ps-Ns)/(Ps+Ns)	0.915 (0)	1 (0)	0.952 (0)
(Pc-Nc)/(Pc+Nc)	0.878 (0)	0.952 (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