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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, 27 Nov 2018 13:58:00 +0100

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/2018/Nov/27/t15433235305oj12ta6fo2b88z.htm/, Retrieved Fri, 03 May 2024 20:11:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315713, Retrieved Fri, 03 May 2024 20:11:12 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

129

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Survey Scores] [] [2018-11-27 12:58:00] [f73ba1b1d093862c82b779ff48b27fc1] [Current]

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Dataseries X:

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Histogram

Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315713&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315713&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315713&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

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	0.07	91	80	0.06	69	56	0.1
2	-0.08	90	104	-0.07	63	77	-0.1
3	1.41	240	5	0.96	152	4	0.95
4	-0.25	39	81	-0.35	33	72	-0.37
5	1.51	260	7	0.95	151	7	0.91
6	1.43	246	8	0.94	148	7	0.91
7	-0.37	40	102	-0.44	35	85	-0.42
8	-0.79	43	171	-0.6	36	101	-0.47
9	1.33	226	2	0.98	155	2	0.97
10	1.55	258	1	0.99	156	1	0.99
11	1.39	235	3	0.97	159	3	0.96
12	0.99	167	2	0.98	135	1	0.99
13	-0.62	8	109	-0.86	7	104	-0.87
14	0.2	42	22	0.31	33	17	0.32
15	-0.12	6	26	-0.62	6	24	-0.6
16	0.4	76	12	0.73	70	11	0.73
17	1.33	229	8	0.93	144	7	0.91
18	1.34	226	3	0.97	153	2	0.97
19	1.03	180	9	0.9	128	6	0.91
20	0.7	138	21	0.74	109	18	0.72
21	1.32	227	6	0.95	152	5	0.94
22	0.35	90	33	0.46	80	27	0.5
23	0.27	78	36	0.37	64	32	0.33
24	0.7	140	24	0.71	113	23	0.66
25	0.84	148	7	0.91	121	7	0.89
26	0.95	161	4	0.95	139	4	0.94
27	1.23	216	8	0.93	146	7	0.91
28	0.73	126	4	0.94	118	3	0.95
29	1.33	218	2	0.98	154	2	0.97
30	1.35	227	0	1	158	0	1
31	1	121	12	0.82	83	11	0.77
32	0.95	159	2	0.98	143	2	0.97
33	1.2	200	1	0.99	148	1	0.99
34	0.32	53	2	0.93	47	2	0.92

\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 & 0.07 & 91 & 80 & 0.06 & 69 & 56 & 0.1 \tabularnewline
2 & -0.08 & 90 & 104 & -0.07 & 63 & 77 & -0.1 \tabularnewline
3 & 1.41 & 240 & 5 & 0.96 & 152 & 4 & 0.95 \tabularnewline
4 & -0.25 & 39 & 81 & -0.35 & 33 & 72 & -0.37 \tabularnewline
5 & 1.51 & 260 & 7 & 0.95 & 151 & 7 & 0.91 \tabularnewline
6 & 1.43 & 246 & 8 & 0.94 & 148 & 7 & 0.91 \tabularnewline
7 & -0.37 & 40 & 102 & -0.44 & 35 & 85 & -0.42 \tabularnewline
8 & -0.79 & 43 & 171 & -0.6 & 36 & 101 & -0.47 \tabularnewline
9 & 1.33 & 226 & 2 & 0.98 & 155 & 2 & 0.97 \tabularnewline
10 & 1.55 & 258 & 1 & 0.99 & 156 & 1 & 0.99 \tabularnewline
11 & 1.39 & 235 & 3 & 0.97 & 159 & 3 & 0.96 \tabularnewline
12 & 0.99 & 167 & 2 & 0.98 & 135 & 1 & 0.99 \tabularnewline
13 & -0.62 & 8 & 109 & -0.86 & 7 & 104 & -0.87 \tabularnewline
14 & 0.2 & 42 & 22 & 0.31 & 33 & 17 & 0.32 \tabularnewline
15 & -0.12 & 6 & 26 & -0.62 & 6 & 24 & -0.6 \tabularnewline
16 & 0.4 & 76 & 12 & 0.73 & 70 & 11 & 0.73 \tabularnewline
17 & 1.33 & 229 & 8 & 0.93 & 144 & 7 & 0.91 \tabularnewline
18 & 1.34 & 226 & 3 & 0.97 & 153 & 2 & 0.97 \tabularnewline
19 & 1.03 & 180 & 9 & 0.9 & 128 & 6 & 0.91 \tabularnewline
20 & 0.7 & 138 & 21 & 0.74 & 109 & 18 & 0.72 \tabularnewline
21 & 1.32 & 227 & 6 & 0.95 & 152 & 5 & 0.94 \tabularnewline
22 & 0.35 & 90 & 33 & 0.46 & 80 & 27 & 0.5 \tabularnewline
23 & 0.27 & 78 & 36 & 0.37 & 64 & 32 & 0.33 \tabularnewline
24 & 0.7 & 140 & 24 & 0.71 & 113 & 23 & 0.66 \tabularnewline
25 & 0.84 & 148 & 7 & 0.91 & 121 & 7 & 0.89 \tabularnewline
26 & 0.95 & 161 & 4 & 0.95 & 139 & 4 & 0.94 \tabularnewline
27 & 1.23 & 216 & 8 & 0.93 & 146 & 7 & 0.91 \tabularnewline
28 & 0.73 & 126 & 4 & 0.94 & 118 & 3 & 0.95 \tabularnewline
29 & 1.33 & 218 & 2 & 0.98 & 154 & 2 & 0.97 \tabularnewline
30 & 1.35 & 227 & 0 & 1 & 158 & 0 & 1 \tabularnewline
31 & 1 & 121 & 12 & 0.82 & 83 & 11 & 0.77 \tabularnewline
32 & 0.95 & 159 & 2 & 0.98 & 143 & 2 & 0.97 \tabularnewline
33 & 1.2 & 200 & 1 & 0.99 & 148 & 1 & 0.99 \tabularnewline
34 & 0.32 & 53 & 2 & 0.93 & 47 & 2 & 0.92 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315713&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]0.07[/C][C]91[/C][C]80[/C][C]0.06[/C][C]69[/C][C]56[/C][C]0.1[/C][/ROW]
[ROW][C]2[/C][C]-0.08[/C][C]90[/C][C]104[/C][C]-0.07[/C][C]63[/C][C]77[/C][C]-0.1[/C][/ROW]
[ROW][C]3[/C][C]1.41[/C][C]240[/C][C]5[/C][C]0.96[/C][C]152[/C][C]4[/C][C]0.95[/C][/ROW]
[ROW][C]4[/C][C]-0.25[/C][C]39[/C][C]81[/C][C]-0.35[/C][C]33[/C][C]72[/C][C]-0.37[/C][/ROW]
[ROW][C]5[/C][C]1.51[/C][C]260[/C][C]7[/C][C]0.95[/C][C]151[/C][C]7[/C][C]0.91[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]246[/C][C]8[/C][C]0.94[/C][C]148[/C][C]7[/C][C]0.91[/C][/ROW]
[ROW][C]7[/C][C]-0.37[/C][C]40[/C][C]102[/C][C]-0.44[/C][C]35[/C][C]85[/C][C]-0.42[/C][/ROW]
[ROW][C]8[/C][C]-0.79[/C][C]43[/C][C]171[/C][C]-0.6[/C][C]36[/C][C]101[/C][C]-0.47[/C][/ROW]
[ROW][C]9[/C][C]1.33[/C][C]226[/C][C]2[/C][C]0.98[/C][C]155[/C][C]2[/C][C]0.97[/C][/ROW]
[ROW][C]10[/C][C]1.55[/C][C]258[/C][C]1[/C][C]0.99[/C][C]156[/C][C]1[/C][C]0.99[/C][/ROW]
[ROW][C]11[/C][C]1.39[/C][C]235[/C][C]3[/C][C]0.97[/C][C]159[/C][C]3[/C][C]0.96[/C][/ROW]
[ROW][C]12[/C][C]0.99[/C][C]167[/C][C]2[/C][C]0.98[/C][C]135[/C][C]1[/C][C]0.99[/C][/ROW]
[ROW][C]13[/C][C]-0.62[/C][C]8[/C][C]109[/C][C]-0.86[/C][C]7[/C][C]104[/C][C]-0.87[/C][/ROW]
[ROW][C]14[/C][C]0.2[/C][C]42[/C][C]22[/C][C]0.31[/C][C]33[/C][C]17[/C][C]0.32[/C][/ROW]
[ROW][C]15[/C][C]-0.12[/C][C]6[/C][C]26[/C][C]-0.62[/C][C]6[/C][C]24[/C][C]-0.6[/C][/ROW]
[ROW][C]16[/C][C]0.4[/C][C]76[/C][C]12[/C][C]0.73[/C][C]70[/C][C]11[/C][C]0.73[/C][/ROW]
[ROW][C]17[/C][C]1.33[/C][C]229[/C][C]8[/C][C]0.93[/C][C]144[/C][C]7[/C][C]0.91[/C][/ROW]
[ROW][C]18[/C][C]1.34[/C][C]226[/C][C]3[/C][C]0.97[/C][C]153[/C][C]2[/C][C]0.97[/C][/ROW]
[ROW][C]19[/C][C]1.03[/C][C]180[/C][C]9[/C][C]0.9[/C][C]128[/C][C]6[/C][C]0.91[/C][/ROW]
[ROW][C]20[/C][C]0.7[/C][C]138[/C][C]21[/C][C]0.74[/C][C]109[/C][C]18[/C][C]0.72[/C][/ROW]
[ROW][C]21[/C][C]1.32[/C][C]227[/C][C]6[/C][C]0.95[/C][C]152[/C][C]5[/C][C]0.94[/C][/ROW]
[ROW][C]22[/C][C]0.35[/C][C]90[/C][C]33[/C][C]0.46[/C][C]80[/C][C]27[/C][C]0.5[/C][/ROW]
[ROW][C]23[/C][C]0.27[/C][C]78[/C][C]36[/C][C]0.37[/C][C]64[/C][C]32[/C][C]0.33[/C][/ROW]
[ROW][C]24[/C][C]0.7[/C][C]140[/C][C]24[/C][C]0.71[/C][C]113[/C][C]23[/C][C]0.66[/C][/ROW]
[ROW][C]25[/C][C]0.84[/C][C]148[/C][C]7[/C][C]0.91[/C][C]121[/C][C]7[/C][C]0.89[/C][/ROW]
[ROW][C]26[/C][C]0.95[/C][C]161[/C][C]4[/C][C]0.95[/C][C]139[/C][C]4[/C][C]0.94[/C][/ROW]
[ROW][C]27[/C][C]1.23[/C][C]216[/C][C]8[/C][C]0.93[/C][C]146[/C][C]7[/C][C]0.91[/C][/ROW]
[ROW][C]28[/C][C]0.73[/C][C]126[/C][C]4[/C][C]0.94[/C][C]118[/C][C]3[/C][C]0.95[/C][/ROW]
[ROW][C]29[/C][C]1.33[/C][C]218[/C][C]2[/C][C]0.98[/C][C]154[/C][C]2[/C][C]0.97[/C][/ROW]
[ROW][C]30[/C][C]1.35[/C][C]227[/C][C]0[/C][C]1[/C][C]158[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]121[/C][C]12[/C][C]0.82[/C][C]83[/C][C]11[/C][C]0.77[/C][/ROW]
[ROW][C]32[/C][C]0.95[/C][C]159[/C][C]2[/C][C]0.98[/C][C]143[/C][C]2[/C][C]0.97[/C][/ROW]
[ROW][C]33[/C][C]1.2[/C][C]200[/C][C]1[/C][C]0.99[/C][C]148[/C][C]1[/C][C]0.99[/C][/ROW]
[ROW][C]34[/C][C]0.32[/C][C]53[/C][C]2[/C][C]0.93[/C][C]47[/C][C]2[/C][C]0.92[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315713&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315713&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	0.07	91	80	0.06	69	56	0.1
2	-0.08	90	104	-0.07	63	77	-0.1
3	1.41	240	5	0.96	152	4	0.95
4	-0.25	39	81	-0.35	33	72	-0.37
5	1.51	260	7	0.95	151	7	0.91
6	1.43	246	8	0.94	148	7	0.91
7	-0.37	40	102	-0.44	35	85	-0.42
8	-0.79	43	171	-0.6	36	101	-0.47
9	1.33	226	2	0.98	155	2	0.97
10	1.55	258	1	0.99	156	1	0.99
11	1.39	235	3	0.97	159	3	0.96
12	0.99	167	2	0.98	135	1	0.99
13	-0.62	8	109	-0.86	7	104	-0.87
14	0.2	42	22	0.31	33	17	0.32
15	-0.12	6	26	-0.62	6	24	-0.6
16	0.4	76	12	0.73	70	11	0.73
17	1.33	229	8	0.93	144	7	0.91
18	1.34	226	3	0.97	153	2	0.97
19	1.03	180	9	0.9	128	6	0.91
20	0.7	138	21	0.74	109	18	0.72
21	1.32	227	6	0.95	152	5	0.94
22	0.35	90	33	0.46	80	27	0.5
23	0.27	78	36	0.37	64	32	0.33
24	0.7	140	24	0.71	113	23	0.66
25	0.84	148	7	0.91	121	7	0.89
26	0.95	161	4	0.95	139	4	0.94
27	1.23	216	8	0.93	146	7	0.91
28	0.73	126	4	0.94	118	3	0.95
29	1.33	218	2	0.98	154	2	0.97
30	1.35	227	0	1	158	0	1
31	1	121	12	0.82	83	11	0.77
32	0.95	159	2	0.98	143	2	0.97
33	1.2	200	1	0.99	148	1	0.99
34	0.32	53	2	0.93	47	2	0.92

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.912 (0)	0.906 (0)
(Ps-Ns)/(Ps+Ns)	0.912 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.906 (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) & 0.912 (0) & 0.906 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.912 (0) & 1 (0) & 0.999 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.906 (0) & 0.999 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315713&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]0.912 (0)[/C][C]0.906 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.912 (0)[/C][C]1 (0)[/C][C]0.999 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.906 (0)[/C][C]0.999 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315713&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315713&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)	0.912 (0)	0.906 (0)
(Ps-Ns)/(Ps+Ns)	0.912 (0)	1 (0)	0.999 (0)
(Pc-Nc)/(Pc+Nc)	0.906 (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)	0.697 (0)	0.653 (0)
(Ps-Ns)/(Ps+Ns)	0.697 (0)	1 (0)	0.952 (0)
(Pc-Nc)/(Pc+Nc)	0.653 (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) & 0.697 (0) & 0.653 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.697 (0) & 1 (0) & 0.952 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.653 (0) & 0.952 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315713&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]0.697 (0)[/C][C]0.653 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.697 (0)[/C][C]1 (0)[/C][C]0.952 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.653 (0)[/C][C]0.952 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315713&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315713&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)	0.697 (0)	0.653 (0)
(Ps-Ns)/(Ps+Ns)	0.697 (0)	1 (0)	0.952 (0)
(Pc-Nc)/(Pc+Nc)	0.653 (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)
}
print(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