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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

Wed, 10 Dec 2014 13:54:16 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/10/t1418219676o3p8anctmi8kjx9.htm/, Retrieved Sun, 19 May 2024 16:29:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265190, Retrieved Sun, 19 May 2024 16:29:32 +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] [] [2014-12-10 12:34:32] [5efa6717cfe6505454df834acc87b53b]
- R     [Survey Scores] [] [2014-12-10 12:55:50] [5efa6717cfe6505454df834acc87b53b]
-    D      [Survey Scores] [] [2014-12-10 13:54:16] [4621f922aed0297f88122271e88ec2ef] [Current]
-    D        [Survey Scores] [] [2014-12-10 13:57:46] [5efa6717cfe6505454df834acc87b53b] 

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Dataseries X:

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Histogram

Boxplots

NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	1	3	2	3	4	4	2	4	3	3	4	3	2	3	4	2	2	2	2	2	2	3	2	2	4	3	4	3	4	4	4	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	5	2	4	1	2	4	5	2	1	1	2	2	2	2	4	5	2	3	2	2	2	1	3	2	1	5	4	4	3	4	3	4	3	2
4	3	2	3	2	4	3	4	2	2	2	2	2	2	2	3	3	3	3	3	3	3	3	4	3	3	2	3	3	3	3	3	3	3	3
4	4	2	2	2	4	4	4	2	4	4	3	4	4	2	4	2	2	2	2	2	2	2	3	2	4	4	2	4	3	4	4	2	3	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	3	2	2	2	2	3	3	2	2	2	3	2	2	2	3	3	3	3	3	3	4	2	3	1	2	3	2	3	3	3	3	2	3	2
4	4	3	1	4	3	3	4	3	3	2	2	2	2	2	4	4	4	3	3	3	2	2	4	2	2	4	2	4	4	3	4	4	2	2
5	5	2	4	2	2	4	5	2	4	2	5	1	2	1	5	4	2	4	1	3	1	1	4	1	1	2	4	4	4	5	5	5	3	1
2	2	2	2	2	4	2	4	1	2	4	2	1	1	2	4	2	2	2	2	2	2	1	4	2	2	4	2	2	2	2	2	2	2	1
4	2	2	4	2	4	3	4	2	4	2	3	2	2	4	4	4	4	3	4	2	2	2	4	2	2	4	2	3	4	2	2	3	2	2
4	4	2	2	2	4	4	4	2	2	3	3	1	2	2	2	2	2	4	4	2	2	2	2	2	3	4	2	4	2	4	2	3	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	2	1	4	2	4	4	4	1	4	4	3	2	4	2	4	4	1	3	2	2	4	1	4	2	2	4	3	4	2	3	2	2	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	3	4	3	4	4	4	4	4	3	3	3	3	2	3	4	3	2	2	2	3	1	3	2	3	4	2	4	3	4	3	3	3	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	2	4	2	2	4	4	3	2	4	4	5	2	1	3	5	5	4	4	4	2	2	2	4	2	3	4	4	4	3	2	2	3	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	2	2	4	2	4	4	4	5	5	3	5	3	3	2	4	2	4	4	3	4	2	3	5	4	1	4	4	3	4	3	5	3	3	2
4	4	3	4	3	4	4	4	4	4	4	4	4	3	3	3	4	4	4	3	3	3	3	5	3	3	4	2	4	4	4	3	3	3	3
4	5	3	1	3	4	5	5	4	4	4	4	4	3	4	4	5	3	4	4	4	3	3	4	3	3	5	4	5	4	5	3	3	3	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	2	4	4	4	2	4	2	4	2	4	2	2	2	4	4	2	3	2	2	2	2	2	2	2	4	2	4	4	4	4	4	2	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	3	2	2	2	3	4	2	4	4	3	2	2	2	4	3	2	2	2	2	2	2	2	2	2	4	2	3	3	4	3	2	2	2
5	4	5	5	5	5	4	4	4	3	5	5	1	2	3	3	2	2	2	5	3	5	2	4	3	2	5	2	4	4	3	2	2	2	4
2	4	4	5	2	5	2	4	4	4	4	4	4	2	1	3	4	4	2	1	2	2	4	3	2	2	4	2	3	4	3	2	2	2	2
3	4	2	2	1	2	4	4	2	4	2	2	2	2	2	2	4	4	2	2	4	1	2	1	2	2	4	2	4	2	4	2	4	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	2	4	1	2	4	4	4	2	4	3	2	3	4	3	3	4	2	2	3	1	3	4	2	2	4	2	4	2	4	2	2	3	2
3	4	2	3	2	4	4	4	3	4	4	3	2	2	2	4	4	3	3	3	2	2	2	3	3	2	4	4	4	4	4	4	3	3	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	3	1	2	2	3	4	4	1	2	3	2	1	1	1	4	2	1	2	3	1	1	1	1	1	1	4	2	3	2	3	2	1	2	1
2	4	2	2	2	4	4	4	2	2	4	4	2	2	2	4	4	2	2	2	2	2	2	2	2	2	4	2	2	2	4	4	4	4	2
5	4	5	4	5	5	5	5	4	2	5	5	3	4	2	5	1	4	4	4	2	3	2	5	3	2	5	2	5	5	5	2	3	3	0
2	4	1	1	2	2	4	4	1	2	4	2	1	2	2	4	2	1	2	2	1	1	1	1	1	1	4	1	3	1	4	1	1	2	1
2	5	2	4	3	4	3	4	4	4	3	3	2	2	2	3	3	2	3	2	3	3	3	4	2	3	4	2	3	2	3	4	4	2	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	1	2	2	4	3	4	2	3	2	4	2	2	1	4	4	3	2	2	2	1	2	3	2	1	4	2	4	3	4	2	2	2	1
3	4	1	2	1	3	4	4	2	2	4	3	1	2	2	3	3	2	2	2	2	2	2	4	2	2	4	2	4	4	4	4	4	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	2	4	3	3	4	4	5	4	4	4	2	2	2	4	4	3	2	2	4	2	3	4	4	2	4	4	4	4	4	4	5	3	2
2	2	2	4	2	2	4	5	4	3	2	5	1	2	1	4	2	2	3	1	4	1	1	4	2	1	5	2	4	3	4	2	2	3	1
2	4	2	3	4	4	3	4	2	3	2	2	2	3	2	3	3	4	2	3	4	3	4	3	4	2	4	3	3	2	2	4	3	2	2
1	3	1	1	1	2	2	2	1	3	3	2	1	1	1	3	3	1	1	1	1	1	1	3	1	3	4	2	3	2	2	2	2	1	2
4	5	1	3	3	4	4	4	2	3	4	4	3	2	1	4	4	3	4	2	2	4	1	3	2	3	5	2	5	3	4	2	4	2	3
2	2	2	1	4	5	4	2	4	2	2	5	2	2	1	5	3	4	5	1	1	1	1	5	1	1	2	1	1	5	2	1	2	3	1
4	5	2	4	2	4	5	5	3	3	2	2	4	4	2	2	5	4	2	2	4	1	2	4	4	1	5	4	4	4	5	2	4	4	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	1	2	2	4	4	4	1	3	2	4	1	2	2	4	4	1	2	2	1	1	1	2	2	2	4	5	4	4	4	4	4	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	3	4	4	2	4	4	4	3	4	4	3	2	2	2	3	2	2	3	2	4	2	1	4	2	2	4	3	4	2	4	3	4	2	0
2	4	2	2	2	4	3	4	2	4	3	4	2	4	1	4	2	3	2	2	2	1	2	3	2	1	4	2	4	4	3	2	2	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	1	2	2	2	3	4	2	2	3	3	1	1	1	4	2	1	2	2	2	1	1	3	2	1	3	2	3	3	4	3	2	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	1	2	1	3	4	3	1	4	3	3	1	1	1	2	2	1	2	1	1	2	1	3	1	2	3	3	3	2	3	2	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	5	1	3	1	4	5	5	4	4	2	4	4	3	1	4	4	3	4	1	3	1	3	4	3	2	4	5	5	4	5	4	3	2	1
4	2	1	4	5	3	3	4	1	1	5	3	2	2	2	3	3	1	2	2	4	1	2	2	2	4	3	2	2	4	2	1	2	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	2	2	2	3	4	4	2	3	4	4	2	2	2	4	2	2	2	2	2	2	2	4	2	2	4	3	4	4	4	2	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	3	2	2	4	3	4	2	2	4	4	2	2	1	4	3	2	3	2	3	2	2	4	2	3	4	2	3	4	4	4	3	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	2	4	2	4	4	4	3	3	4	4	3	3	2	4	3	5	4	3	3	2	2	4	2	2	4	3	4	4	4	3	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	5	1	2	1	4	5	5	5	1	2	4	1	1	1	4	1	1	2	1	1	1	1	4	1	1	1	1	5	2	5	2	2	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	2	3	3	4	3	2	4	2	1	4	3	1	1	3	2	2	3	3	3	3	2	2	4	2	2	3	1	3	3	2	3	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	1	3	2	4	3	4	2	2	4	3	2	2	2	4	3	2	3	2	2	3	2	3	2	3	4	2	3	3	4	2	3	2	2
4	4	2	2	2	4	4	4	1	2	2	2	2	1	2	4	2	2	1	2	2	1	1	4	2	2	4	1	3	4	4	2	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	2	3	2	3	4	4	2	3	4	2	2	2	2	3	3	2	2	2	2	2	2	3	2	2	4	2	3	3	4	2	2	2	2
5	2	2	4	2	4	2	3	4	4	4	4	2	2	3	2	4	4	4	3	4	4	3	4	4	2	3	5	2	4	2	4	4	4	4
4	4	3	3	2	4	4	4	2	3	4	4	2	2	2	4	3	2	2	2	2	1	2	4	2	2	4	2	4	3	5	3	2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	1	2	1	3	4	4	3	3	2	3	2	2	2	4	2	2	3	3	2	1	1	4	3	2	4	2	4	2	4	2	2	3	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	3	1	4	2	4	4	4	2	4	2	3	1	1	1	4	4	2	2	2	2	2	2	3	2	2	4	2	4	3	3	4	4	3	2
2	2	1	3	2	4	2	3	2	2	2	4	2	2	1	3	4	3	3	2	2	2	2	3	2	2	3	4	3	3	3	2	2	2	2
4	3	1	3	3	4	4	4	4	4	1	2	1	2	1	4	5	2	3	2	4	2	2	3	4	2	4	4	3	3	4	2	2	3	2
4	5	3	3	3	4	5	5	4	4	4	4	3	3	3	4	4	4	4	3	2	4	3	4	2	2	5	3	5	3	5	2	5	4	2
4	4	4	3	3	4	4	5	2	3	5	4	3	3	2	4	2	3	2	2	4	3	3	3	3	3	4	2	4	3	4	2	2	4	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	2	3	2	3	4	3	4	5	3	3	2	2	4	4	4	2	1	2	4	2	4	2	4	2	4	2	4	4	3	4	3	4	2
2	4	2	4	1	4	4	5	3	2	2	4	2	2	1	2	2	2	4	2	1	1	1	4	2	2	4	2	2	4	4	4	4	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	4	1	3	1	4	5	4	4	3	2	2	4	4	1	4	4	2	4	1	4	1	2	5	2	1	4	5	5	4	5	1	2	2	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265190&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265190&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265190&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	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.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	0.1	31	25	0.11	27	24	0.06
2	0.68	54	11	0.66	45	11	0.61
3	-0.95	8	68	-0.79	6	48	-0.78
4	-0.1	24	30	-0.11	22	25	-0.06
5	-0.73	11	57	-0.68	8	46	-0.7
6	0.56	45	10	0.64	41	10	0.61
7	0.68	50	7	0.75	43	7	0.72
8	1.02	66	2	0.94	55	2	0.93
9	-0.38	22	46	-0.35	19	37	-0.32
10	0.05	28	25	0.06	26	21	0.11
11	0.13	33	25	0.14	29	23	0.12
12	0.33	35	14	0.43	28	14	0.33
13	-0.89	8	64	-0.78	8	48	-0.71
14	-0.79	6	56	-0.81	6	46	-0.77
15	-1.08	4	72	-0.89	4	53	-0.86
16	0.59	44	7	0.73	40	7	0.7
17	0.17	33	22	0.2	28	20	0.17
18	-0.44	16	44	-0.47	15	35	-0.4
19	-0.29	16	34	-0.36	15	31	-0.35
20	-0.73	7	53	-0.77	6	44	-0.76
21	-0.49	14	45	-0.53	14	37	-0.45
22	-0.98	7	69	-0.82	6	48	-0.78
23	-1.03	3	68	-0.92	3	49	-0.88
24	0.4	38	13	0.49	33	10	0.53
25	-0.76	7	55	-0.77	7	47	-0.74
26	-0.95	2	62	-0.94	2	49	-0.92
27	0.87	60	5	0.85	52	4	0.86
28	-0.43	18	45	-0.43	14	40	-0.48
29	0.59	44	7	0.73	37	6	0.72
30	0.21	29	16	0.29	27	15	0.29
31	0.63	49	9	0.69	40	9	0.63
32	-0.24	21	36	-0.26	19	32	-0.25
33	-0.17	21	32	-0.21	18	30	-0.25
34	-0.62	6	45	-0.76	6	39	-0.73
35	-1.13	3	74	-0.92	3	53	-0.89

\begin{tabular}{lllllllll}
\hline
Summary of survey scores (median of Likert score was subtracted) \tabularnewline
Question & mean & Sum ofpositives (Ps) & Sum ofnegatives (Ns) & (Ps-Ns)/(Ps+Ns) & Count ofpositives (Pc) & Count ofnegatives (Nc) & (Pc-Nc)/(Pc+Nc) \tabularnewline
1 & 0.1 & 31 & 25 & 0.11 & 27 & 24 & 0.06 \tabularnewline
2 & 0.68 & 54 & 11 & 0.66 & 45 & 11 & 0.61 \tabularnewline
3 & -0.95 & 8 & 68 & -0.79 & 6 & 48 & -0.78 \tabularnewline
4 & -0.1 & 24 & 30 & -0.11 & 22 & 25 & -0.06 \tabularnewline
5 & -0.73 & 11 & 57 & -0.68 & 8 & 46 & -0.7 \tabularnewline
6 & 0.56 & 45 & 10 & 0.64 & 41 & 10 & 0.61 \tabularnewline
7 & 0.68 & 50 & 7 & 0.75 & 43 & 7 & 0.72 \tabularnewline
8 & 1.02 & 66 & 2 & 0.94 & 55 & 2 & 0.93 \tabularnewline
9 & -0.38 & 22 & 46 & -0.35 & 19 & 37 & -0.32 \tabularnewline
10 & 0.05 & 28 & 25 & 0.06 & 26 & 21 & 0.11 \tabularnewline
11 & 0.13 & 33 & 25 & 0.14 & 29 & 23 & 0.12 \tabularnewline
12 & 0.33 & 35 & 14 & 0.43 & 28 & 14 & 0.33 \tabularnewline
13 & -0.89 & 8 & 64 & -0.78 & 8 & 48 & -0.71 \tabularnewline
14 & -0.79 & 6 & 56 & -0.81 & 6 & 46 & -0.77 \tabularnewline
15 & -1.08 & 4 & 72 & -0.89 & 4 & 53 & -0.86 \tabularnewline
16 & 0.59 & 44 & 7 & 0.73 & 40 & 7 & 0.7 \tabularnewline
17 & 0.17 & 33 & 22 & 0.2 & 28 & 20 & 0.17 \tabularnewline
18 & -0.44 & 16 & 44 & -0.47 & 15 & 35 & -0.4 \tabularnewline
19 & -0.29 & 16 & 34 & -0.36 & 15 & 31 & -0.35 \tabularnewline
20 & -0.73 & 7 & 53 & -0.77 & 6 & 44 & -0.76 \tabularnewline
21 & -0.49 & 14 & 45 & -0.53 & 14 & 37 & -0.45 \tabularnewline
22 & -0.98 & 7 & 69 & -0.82 & 6 & 48 & -0.78 \tabularnewline
23 & -1.03 & 3 & 68 & -0.92 & 3 & 49 & -0.88 \tabularnewline
24 & 0.4 & 38 & 13 & 0.49 & 33 & 10 & 0.53 \tabularnewline
25 & -0.76 & 7 & 55 & -0.77 & 7 & 47 & -0.74 \tabularnewline
26 & -0.95 & 2 & 62 & -0.94 & 2 & 49 & -0.92 \tabularnewline
27 & 0.87 & 60 & 5 & 0.85 & 52 & 4 & 0.86 \tabularnewline
28 & -0.43 & 18 & 45 & -0.43 & 14 & 40 & -0.48 \tabularnewline
29 & 0.59 & 44 & 7 & 0.73 & 37 & 6 & 0.72 \tabularnewline
30 & 0.21 & 29 & 16 & 0.29 & 27 & 15 & 0.29 \tabularnewline
31 & 0.63 & 49 & 9 & 0.69 & 40 & 9 & 0.63 \tabularnewline
32 & -0.24 & 21 & 36 & -0.26 & 19 & 32 & -0.25 \tabularnewline
33 & -0.17 & 21 & 32 & -0.21 & 18 & 30 & -0.25 \tabularnewline
34 & -0.62 & 6 & 45 & -0.76 & 6 & 39 & -0.73 \tabularnewline
35 & -1.13 & 3 & 74 & -0.92 & 3 & 53 & -0.89 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265190&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]0.1[/C][C]31[/C][C]25[/C][C]0.11[/C][C]27[/C][C]24[/C][C]0.06[/C][/ROW]
[ROW][C]2[/C][C]0.68[/C][C]54[/C][C]11[/C][C]0.66[/C][C]45[/C][C]11[/C][C]0.61[/C][/ROW]
[ROW][C]3[/C][C]-0.95[/C][C]8[/C][C]68[/C][C]-0.79[/C][C]6[/C][C]48[/C][C]-0.78[/C][/ROW]
[ROW][C]4[/C][C]-0.1[/C][C]24[/C][C]30[/C][C]-0.11[/C][C]22[/C][C]25[/C][C]-0.06[/C][/ROW]
[ROW][C]5[/C][C]-0.73[/C][C]11[/C][C]57[/C][C]-0.68[/C][C]8[/C][C]46[/C][C]-0.7[/C][/ROW]
[ROW][C]6[/C][C]0.56[/C][C]45[/C][C]10[/C][C]0.64[/C][C]41[/C][C]10[/C][C]0.61[/C][/ROW]
[ROW][C]7[/C][C]0.68[/C][C]50[/C][C]7[/C][C]0.75[/C][C]43[/C][C]7[/C][C]0.72[/C][/ROW]
[ROW][C]8[/C][C]1.02[/C][C]66[/C][C]2[/C][C]0.94[/C][C]55[/C][C]2[/C][C]0.93[/C][/ROW]
[ROW][C]9[/C][C]-0.38[/C][C]22[/C][C]46[/C][C]-0.35[/C][C]19[/C][C]37[/C][C]-0.32[/C][/ROW]
[ROW][C]10[/C][C]0.05[/C][C]28[/C][C]25[/C][C]0.06[/C][C]26[/C][C]21[/C][C]0.11[/C][/ROW]
[ROW][C]11[/C][C]0.13[/C][C]33[/C][C]25[/C][C]0.14[/C][C]29[/C][C]23[/C][C]0.12[/C][/ROW]
[ROW][C]12[/C][C]0.33[/C][C]35[/C][C]14[/C][C]0.43[/C][C]28[/C][C]14[/C][C]0.33[/C][/ROW]
[ROW][C]13[/C][C]-0.89[/C][C]8[/C][C]64[/C][C]-0.78[/C][C]8[/C][C]48[/C][C]-0.71[/C][/ROW]
[ROW][C]14[/C][C]-0.79[/C][C]6[/C][C]56[/C][C]-0.81[/C][C]6[/C][C]46[/C][C]-0.77[/C][/ROW]
[ROW][C]15[/C][C]-1.08[/C][C]4[/C][C]72[/C][C]-0.89[/C][C]4[/C][C]53[/C][C]-0.86[/C][/ROW]
[ROW][C]16[/C][C]0.59[/C][C]44[/C][C]7[/C][C]0.73[/C][C]40[/C][C]7[/C][C]0.7[/C][/ROW]
[ROW][C]17[/C][C]0.17[/C][C]33[/C][C]22[/C][C]0.2[/C][C]28[/C][C]20[/C][C]0.17[/C][/ROW]
[ROW][C]18[/C][C]-0.44[/C][C]16[/C][C]44[/C][C]-0.47[/C][C]15[/C][C]35[/C][C]-0.4[/C][/ROW]
[ROW][C]19[/C][C]-0.29[/C][C]16[/C][C]34[/C][C]-0.36[/C][C]15[/C][C]31[/C][C]-0.35[/C][/ROW]
[ROW][C]20[/C][C]-0.73[/C][C]7[/C][C]53[/C][C]-0.77[/C][C]6[/C][C]44[/C][C]-0.76[/C][/ROW]
[ROW][C]21[/C][C]-0.49[/C][C]14[/C][C]45[/C][C]-0.53[/C][C]14[/C][C]37[/C][C]-0.45[/C][/ROW]
[ROW][C]22[/C][C]-0.98[/C][C]7[/C][C]69[/C][C]-0.82[/C][C]6[/C][C]48[/C][C]-0.78[/C][/ROW]
[ROW][C]23[/C][C]-1.03[/C][C]3[/C][C]68[/C][C]-0.92[/C][C]3[/C][C]49[/C][C]-0.88[/C][/ROW]
[ROW][C]24[/C][C]0.4[/C][C]38[/C][C]13[/C][C]0.49[/C][C]33[/C][C]10[/C][C]0.53[/C][/ROW]
[ROW][C]25[/C][C]-0.76[/C][C]7[/C][C]55[/C][C]-0.77[/C][C]7[/C][C]47[/C][C]-0.74[/C][/ROW]
[ROW][C]26[/C][C]-0.95[/C][C]2[/C][C]62[/C][C]-0.94[/C][C]2[/C][C]49[/C][C]-0.92[/C][/ROW]
[ROW][C]27[/C][C]0.87[/C][C]60[/C][C]5[/C][C]0.85[/C][C]52[/C][C]4[/C][C]0.86[/C][/ROW]
[ROW][C]28[/C][C]-0.43[/C][C]18[/C][C]45[/C][C]-0.43[/C][C]14[/C][C]40[/C][C]-0.48[/C][/ROW]
[ROW][C]29[/C][C]0.59[/C][C]44[/C][C]7[/C][C]0.73[/C][C]37[/C][C]6[/C][C]0.72[/C][/ROW]
[ROW][C]30[/C][C]0.21[/C][C]29[/C][C]16[/C][C]0.29[/C][C]27[/C][C]15[/C][C]0.29[/C][/ROW]
[ROW][C]31[/C][C]0.63[/C][C]49[/C][C]9[/C][C]0.69[/C][C]40[/C][C]9[/C][C]0.63[/C][/ROW]
[ROW][C]32[/C][C]-0.24[/C][C]21[/C][C]36[/C][C]-0.26[/C][C]19[/C][C]32[/C][C]-0.25[/C][/ROW]
[ROW][C]33[/C][C]-0.17[/C][C]21[/C][C]32[/C][C]-0.21[/C][C]18[/C][C]30[/C][C]-0.25[/C][/ROW]
[ROW][C]34[/C][C]-0.62[/C][C]6[/C][C]45[/C][C]-0.76[/C][C]6[/C][C]39[/C][C]-0.73[/C][/ROW]
[ROW][C]35[/C][C]-1.13[/C][C]3[/C][C]74[/C][C]-0.92[/C][C]3[/C][C]53[/C][C]-0.89[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265190&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265190&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of survey scores (median of Likert score was subtracted)
Question	mean	Sum ofpositives (Ps)	Sum ofnegatives (Ns)	(Ps-Ns)/(Ps+Ns)	Count ofpositives (Pc)	Count ofnegatives (Nc)	(Pc-Nc)/(Pc+Nc)
1	0.1	31	25	0.11	27	24	0.06
2	0.68	54	11	0.66	45	11	0.61
3	-0.95	8	68	-0.79	6	48	-0.78
4	-0.1	24	30	-0.11	22	25	-0.06
5	-0.73	11	57	-0.68	8	46	-0.7
6	0.56	45	10	0.64	41	10	0.61
7	0.68	50	7	0.75	43	7	0.72
8	1.02	66	2	0.94	55	2	0.93
9	-0.38	22	46	-0.35	19	37	-0.32
10	0.05	28	25	0.06	26	21	0.11
11	0.13	33	25	0.14	29	23	0.12
12	0.33	35	14	0.43	28	14	0.33
13	-0.89	8	64	-0.78	8	48	-0.71
14	-0.79	6	56	-0.81	6	46	-0.77
15	-1.08	4	72	-0.89	4	53	-0.86
16	0.59	44	7	0.73	40	7	0.7
17	0.17	33	22	0.2	28	20	0.17
18	-0.44	16	44	-0.47	15	35	-0.4
19	-0.29	16	34	-0.36	15	31	-0.35
20	-0.73	7	53	-0.77	6	44	-0.76
21	-0.49	14	45	-0.53	14	37	-0.45
22	-0.98	7	69	-0.82	6	48	-0.78
23	-1.03	3	68	-0.92	3	49	-0.88
24	0.4	38	13	0.49	33	10	0.53
25	-0.76	7	55	-0.77	7	47	-0.74
26	-0.95	2	62	-0.94	2	49	-0.92
27	0.87	60	5	0.85	52	4	0.86
28	-0.43	18	45	-0.43	14	40	-0.48
29	0.59	44	7	0.73	37	6	0.72
30	0.21	29	16	0.29	27	15	0.29
31	0.63	49	9	0.69	40	9	0.63
32	-0.24	21	36	-0.26	19	32	-0.25
33	-0.17	21	32	-0.21	18	30	-0.25
34	-0.62	6	45	-0.76	6	39	-0.73
35	-1.13	3	74	-0.92	3	53	-0.89

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.992 (0)	0.991 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	0.998 (0)
(Pc-Nc)/(Pc+Nc)	0.991 (0)	0.998 (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) & 0.992 (0) & 0.991 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.992 (0) & 1 (0) & 0.998 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.991 (0) & 0.998 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265190&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]0.992 (0)[/C][C]0.991 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.992 (0)[/C][C]1 (0)[/C][C]0.998 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.991 (0)[/C][C]0.998 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265190&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265190&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)	0.992 (0)	0.991 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	0.998 (0)
(Pc-Nc)/(Pc+Nc)	0.991 (0)	0.998 (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)	0.948 (0)	0.926 (0)
(Ps-Ns)/(Ps+Ns)	0.948 (0)	1 (0)	0.97 (0)
(Pc-Nc)/(Pc+Nc)	0.926 (0)	0.97 (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) & 0.948 (0) & 0.926 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.948 (0) & 1 (0) & 0.97 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.926 (0) & 0.97 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265190&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]0.948 (0)[/C][C]0.926 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.948 (0)[/C][C]1 (0)[/C][C]0.97 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.926 (0)[/C][C]0.97 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265190&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265190&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)	0.948 (0)	0.926 (0)
(Ps-Ns)/(Ps+Ns)	0.948 (0)	1 (0)	0.97 (0)
(Pc-Nc)/(Pc+Nc)	0.926 (0)	0.97 (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