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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 11:50:50 +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/t1418212261jrh8qzf4jzi1g8y.htm/, Retrieved Tue, 10 Feb 2026 18:47:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264968, Retrieved Tue, 10 Feb 2026 18:47:56 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

344

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

-       [Survey Scores] [] [2014-12-10 11:50:50] [921bde233b8ec180d9061abb09deed53] [Current]

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Dataseries X:

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Histogram

Boxplots

4 2 2 2 2 2 2 1 2 3 2 2
4 5 4 5 5 4 5 5 5 5 5 5
3 1 3 3 2 3 5 3 3 5 3 3
6 5 7 6 4 6 6 5 6 6 5 5
4 3 3 4 3 4 4 3 5 3 3 4
4 4 5 4 4 5 4 4 5 5 4 4
5 5 5 5 4 5 5 5 1 4 3 5
5 3 4 3 2 4 6 2 4 5 2 3
7 7 7 7 7 7 7 7 7 7 7 7
7 6 5 6 6 5 7 6 6 6 2 5
3 5 3 4 3 5 3 5 5 5 3 5
7 4 6 7 6 5 6 6 6 6 6 5
2 3 2 3 5 5 5 4 6 6 5 6
5 3 5 5 3 5 7 3 6 7 3 6
6 2 5 5 5 6 7 5 7 7 6 7
5 5 4 5 5 5 6 5 6 6 5 5
4 3 4 3 3 4 4 3 5 4 3 3
6 4 5 5 4 5 5 4 4 5 4 5
5 3 5 5 4 4 6 4 4 6 5 5
7 7 6 6 4 7 7 4 6 7 6 7
7 6 6 5 3 6 7 3 5 7 3 7
7 5 5 7 4 5 6 4 5 6 4 5
6 4 5 6 5 4 5 4 5 5 4 5
5 4 3 4 3 5 6 4 6 6 6 5
5 5 6 6 4 6 5 4 5 6 5 6
4 4 5 5 4 5 6 3 4 5 4 4
5 6 4 6 6 5 4 3 3 6 5 4
5 5 4 5 3 5 5 2 4 5 3 5
6 6 4 5 5 6 5 5 6 6 5 5
6 4 4 5 2 5 4 4 5 6 2 6
2 1 3 2 3 5 2 1 5 2 1 2
6 4 5 5 4 5 6 2 5 5 3 4
3 5 4 5 5 5 7 4 6 5 5 4
6 4 2 6 1 1 6 1 2 6 6 2
4 4 3 5 4 5 4 3 6 4 3 6
3 2 5 5 3 5 5 5 5 7 3 5
7 2 5 6 4 6 5 3 3 5 3 5
5 4 5 5 4 4 5 4 6 5 5 4
5 4 5 5 5 5 6 5 5 6 5 5
4 2 4 4 3 3 5 2 4 6 3 7
4 2 4 4 3 3 4 2 5 3 3 2
6 2 3 3 3 4 5 3 5 6 3 5
5 2 4 5 3 4 6 3 4 6 3 5
5 2 2 2 4 2 4 2 2 6 2 2
4 2 2 2 2 3 4 1 2 4 2 2
7 4 5 5 6 5 6 6 6 6 6 6
4 4 5 5 2 5 4 2 5 4 4 5
6 5 5 5 4 6 5 5 5 7 4 4
6 4 4 6 5 6 6 5 6 7 6 6
4 2 6 4 2 5 4 3 6 4 2 5
5 3 5 3 4 5 5 4 6 5 5 6
5 3 2 4 4 4 6 4 5 5 3 5
4 4 4 4 2 2 5 2 3 5 3 5
5 5 6 6 5 6 6 5 6 6 5 6
6 3 5 6 4 5 6 4 6 6 6 5
5 2 5 6 4 5 6 4 5 6 4 5
5 2 2 3 2 6 2 2 6 3 2 6
4 4 3 5 4 6 5 4 5 6 4 4
6 3 3 4 3 5 5 2 3 5 3 3
5 3 5 5 2 5 5 3 5 4 2 4
6 2 7 5 4 6 6 3 6 5 6 5
4 2 4 5 5 3 6 3 6 7 5 6
3 5 2 3 2 3 4 2 6 5 1 5
5 4 2 4 2 4 4 2 5 4 2 5
5 4 4 5 4 5 4 5 5 5 5 2
5 3 4 5 3 4 5 2 3 5 2 3
4 6 5 6 5 5 6 5 6 6 6 6
5 5 4 4 2 4 6 5 7 6 4 6
5 2 4 4 2 5 5 1 4 7 5 2
5 4 3 4 1 3 3 1 5 4 1 3
5 5 4 5 4 3 5 3 5 5 4 3
5 3 3 6 4 2 5 4 4 5 5 5
6 4 6 5 4 5 4 3 4 5 5 5
6 5 6 5 6 5 6 5 5 6 6 5
5 2 2 1 1 2 6 1 4 6 1 6
6 5 5 5 5 5 5 5 4 6 3 5
2 2 2 3 3 4 6 2 5 5 3 5
4 2 4 4 1 3 4 2 3 5 3 3
6 3 7 7 4 6 6 4 7 5 5 6
4 2 3 3 2 3 3 2 5 3 2 2
4 6 4 5 3 4 5 3 5 5 4 5
5 4 4 5 3 4 4 3 3 6 4 4
5 4 4 5 3 4 6 4 5 6 4 5
4 1 6 4 3 5 6 3 6 5 2 4
4 5 3 5 5 4 5 6 5 7 5 5
3 2 5 4 1 4 4 2 6 4 2 3
5 4 5 5 4 5 6 4 5 5 5 5
6 3 4 6 5 5 6 5 4 6 5 5
5 4 6 5 4 7 5 3 6 7 5 6
4 3 4 5 4 5 5 4 5 6 5 6
5 4 4 5 3 4 6 3 6 5 4 5
4 5 4 4 3 4 5 3 5 6 4 5
4 2 3 2 2 3 5 2 3 3 2 3
4 3 4 7 6 5 7 6 7 7 7 6
2 2 2 2 1 5 4 1 4 3 1 5
4 7 4 5 2 5 4 2 5 4 2 4
4 6 5 6 5 6 6 5 6 6 6 6
5 4 4 5 5 5 5 5 6 5 4 5
7 3 4 5 4 5 6 3 6 4 5 5
4 2 4 3 3 4 3 3 4 5 3 4
6 4 6 6 5 6 5 4 4 6 6 6
5 5 4 5 4 5 6 5 5 4 5 5
4 5 5 5 5 7 6 5 6 7 6 5
2 1 1 5 1 2 5 1 2 4 1 1
6 3 4 6 5 4 6 5 6 7 4 5
4 2 5 4 2 6 5 2 6 5 4 7
4 4 5 4 2 4 4 3 5 7 4 5
7 4 2 7 7 4 4 2 4 7 2 5
6 6 3 5 2 5 5 1 6 5 2 4
5 6 5 6 6 4 5 4 4 6 4 5
5 3 5 6 4 6 5 4 6 5 2 5
6 5 5 5 4 6 5 4 6 6 5 6
6 5 1 6 5 6 5 6 5 6 4 6
4 4 4 5 3 5 5 4 4 6 4 4
1 1 2 1 1 2 2 1 1 2 1 1
5 3 3 3 2 5 4 2 7 3 2 7
4 4 5 4 4 5 5 4 5 5 6 5
7 3 3 5 4 4 5 2 5 7 3 4
5 6 4 6 2 6 6 3 3 5 6 3
6 4 5 5 3 4 5 3 4 5 2 4
6 6 6 5 4 6 6 3 4 6 3 4
5 4 5 5 5 6 5 4 5 5 4 7
5 4 5 5 3 4 5 3 4 5 4 5
4 4 5 4 3 3 5 3 2 5 3 3
6 5 6 6 5 6 7 5 5 6 5 5
4 4 5 4 4 5 4 4 5 4 4 5
5 5 5 5 3 5 5 3 5 5 4 5
4 2 2 4 3 4 3 2 3 3 3 4
4 4 4 4 4 7 7 4 4 7 4 1
6 5 7 6 5 7 7 6 6 7 5 5
4 3 4 4 4 5 7 4 3 5 4 4
6 6 2 2 2 4 4 4 5 5 4 4
5 5 5 6 4 5 5 5 4 6 5 5
6 2 6 4 4 5 6 1 4 6 2 4
5 5 6 5 4 6 5 5 6 5 5 6
4 2 2 4 2 2 5 1 2 4 2 3
6 7 5 5 5 6 5 5 5 6 6 6
3 3 3 4 3 4 5 4 4 5 4 4
5 4 4 5 2 6 6 2 6 6 2 6
5 4 5 5 4 5 5 4 6 6 4 6
6 4 6 7 2 6 6 2 5 6 4 7
2 3 3 3 2 2 3 2 3 3 3 4
5 3 2 5 2 6 4 1 7 5 1 6
6 4 6 6 6 6 6 5 6 6 5 7
5 3 7 4 2 7 4 2 6 4 2 6
6 5 5 5 2 5 5 2 6 6 3 5
5 4 3 4 2 4 4 2 3 4 2 4
5 3 7 7 5 7 7 4 7 7 7 7
4 1 5 5 2 5 5 3 6 6 5 5
5 5 5 4 3 5 5 2 3 5 5 3
6 3 4 4 4 3 6 4 4 5 3 5
6 3 6 5 3 7 6 3 6 7 2 6
5 2 4 5 5 6 6 3 5 5 4 6
4 4 6 5 4 5 5 3 6 5 4 5
3 1 3 4 2 3 2 2 1 4 2 2
6 5 5 6 6 5 6 5 5 6 6 5
7 5 4 7 6 6 7 4 5 7 4 4
7 7 7 6 4 7 7 4 3 7 7 7
6 2 4 5 5 6 6 2 6 5 2 6
6 4 5 6 5 5 5 5 6 6 5 6
6 2 1 3 2 4 4 2 2 6 3 5
4 2 4 4 3 3 4 3 5 6 3 5
6 5 4 5 5 5 5 3 4 7 4 5
5 3 2 5 2 2 5 2 5 6 5 1
6 5 5 5 4 6 5 4 6 6 4 5
4 3 6 5 2 7 4 2 6 4 3 5
3 6 3 4 5 6 4 3 6 4 3 3
5 4 4 6 2 5 5 3 4 5 4 5
5 4 6 5 2 5 5 2 6 5 3 6
7 4 4 6 6 6 6 6 6 7 3 5
4 3 5 5 6 6 6 6 5 4 6 5
7 7 7 7 6 7 7 5 7 7 6 7
5 4 4 5 3 5 5 3 4 6 3 4
5 4 4 4 4 5 5 4 6 5 4 6
5 3 5 6 3 5 5 3 6 6 4 5
5 3 5 6 4 5 5 4 5 5 4 5
5 5 4 5 3 5 4 3 4 6 4 5
5 2 3 4 3 4 5 2 5 5 4 5
4 1 1 1 2 2 2 1 2 4 3 2
4 3 4 5 4 5 4 3 5 4 3 3
6 5 4 5 3 4 4 2 2 4 3 4
5 3 4 5 3 5 5 2 5 5 4 4
4 2 5 5 3 4 4 2 2 4 1 3
6 1 5 4 2 6 5 2 6 6 2 6
5 4 5 5 4 5 5 4 5 6 5 5
3 4 2 3 1 3 2 2 5 4 2 4
4 5 4 7 4 6 5 4 2 7 5 5
4 5 6 7 4 6 5 4 5 6 4 4
4 5 6 6 4 6 5 4 6 7 4 5
4 5 6 7 4 6 4 4 6 6 5 6
5 3 4 6 4 6 5 3 6 4 2 5
4 3 3 5 4 4 4 4 4 5 3 3
3 3 6 6 6 6 6 6 6 6 7 6
6 3 3 7 6 3 5 5 5 6 5 5
5 6 6 5 4 6 6 6 4 6 5 5
7 3 1 4 3 4 4 2 2 5 2 1
5 4 5 4 4 3 4 4 5 4 4 4
5 5 3 5 2 5 5 2 6 4 2 4
4 4 4 4 4 4 4 3 5 4 4 4
5 2 4 3 3 2 5 3 5 6 4 5
6 5 5 6 5 5 4 5 6 7 6 6
2 6 4 2 7 5 2 6 6 2 3 2
6 5 5 6 5 6 4 5 5 6 5 5
6 5 5 6 4 4 5 3 6 6 3 5
4 3 5 3 2 5 4 2 4 4 2 6
4 5 3 4 4 5 4 3 6 5 3 5
6 5 2 7 3 1 1 4 1 7 2 4
5 3 5 6 2 3 7 2 5 7 4 6
5 1 2 5 2 4 5 2 2 3 4 3
5 4 3 4 3 4 5 2 3 6 3 3
4 4 3 5 2 5 7 1 5 5 4 5
5 3 5 6 4 5 5 3 5 5 4 4
6 3 6 6 3 6 6 1 6 6 3 5
5 5 4 6 5 4 5 4 6 6 4 6
5 4 5 5 2 7 5 1 5 7 4 5
5 2 4 6 3 5 6 4 5 6 5 5
3 2 3 5 4 4 4 4 5 5 6 4
5 2 3 2 3 2 3 2 3 5 4 3
6 2 6 5 5 5 5 4 5 6 5 5
4 2 3 6 4 3 4 3 4 5 4 4
4 3 5 3 3 6 5 3 5 6 3 5
5 4 5 5 4 5 5 5 4 6 4 6
5 5 5 5 6 5 5 6 6 5 6 5
6 4 4 1 2 5 6 3 5 6 5 6
5 3 4 5 2 4 4 2 5 5 3 6
4 4 5 4 4 5 4 4 4 4 4 5
4 3 4 5 2 4 4 2 5 5 4 5
5 4 4 4 4 5 7 4 6 7 4 5
5 5 5 6 5 5 5 4 5 6 5 6
5 4 5 6 4 6 6 4 6 6 5 5
4 3 4 5 3 6 6 3 4 5 3 5
7 5 6 6 3 6 5 3 6 6 6 5
6 4 4 7 4 3 6 2 3 6 1 6
7 4 5 6 5 6 6 5 6 6 5 6
5 4 3 5 3 5 5 2 5 5 3 5
5 2 5 6 2 6 6 2 6 6 2 6
5 5 5 6 4 5 5 3 5 5 5 5
6 5 5 5 4 5 5 2 5 7 2 5
7 6 6 7 4 7 4 2 5 5 5 5
3 4 2 3 1 7 2 1 2 3 1 2
5 6 3 5 6 6 5 5 6 6 6 6
7 6 7 7 4 7 6 4 7 7 4 5
5 4 2 5 3 6 3 2 5 6 4 5
4 5 4 6 3 6 5 4 5 6 5 4
4 3 5 4 3 6 4 3 3 4 4 4
5 2 3 4 2 6 6 3 6 6 1 3
5 4 6 5 4 6 6 3 3 6 5 4
4 5 4 3 3 3 3 3 3 6 3 3
3 3 2 5 2 3 5 6 2 5 2 3
7 5 5 4 5 6 5 5 4 7 4 6
6 3 7 5 4 6 7 3 6 6 5 5
5 5 6 5 3 5 4 3 4 6 4 6
5 1 6 5 2 6 6 2 6 4 3 5
4 5 3 5 3 5 5 2 5 4 4 5
1 3 2 1 1 3 1 2 5 1 1 1
3 2 4 3 2 5 4 4 4 4 3 3
5 5 6 5 3 4 6 4 4 6 5 6
4 4 4 4 5 5 4 4 5 5 6 6
3 6 6 7 4 7 7 3 7 6 4 6
6 3 3 3 2 6 5 2 7 6 5 5
4 3 2 4 3 4 5 3 2 5 3 4
5 4 3 5 5 5 3 4 3 6 5 4
5 4 3 4 4 4 5 4 6 6 4 5
5 6 4 3 3 3 3 3 3 4 3 4
6 5 4 6 5 5 6 5 4 6 5 5
5 5 2 5 4 5 5 3 4 6 4 5
5 3 6 4 4 5 5 3 5 5 2 3
5 4 2 5 3 3 4 1 1 5 2 1
7 7 6 6 7 1 7 7 7 7 7 7
5 4 6 6 4 6 6 5 6 6 6 6
6 5 5 5 4 5 6 4 6 6 5 5
5 4 5 5 5 5 5 5 5 5 5 5
5 2 3 3 1 7 5 1 5 4 4 7
5 3 4 5 4 5 6 5 4 5 5 4
6 5 4 6 4 5 6 3 6 5 4 4
6 5 5 5 4 5 5 4 5 6 5 5
4 3 3 4 4 5 3 2 4 5 3 4
5 4 5 5 4 5 5 4 5 5 5 5
3 2 3 4 5 5 5 4 4 4 2 4
5 5 4 4 4 5 7 4 5 5 5 5
5 0 5 5 4 5 4 5 4 5 4 5
7 1 6 6 1 5 7 2 7 7 2 6
2 2 1 2 5 4 2 1 1 7 2 1
5 5 5 3 2 5 5 2 4 4 3 3
6 2 5 5 4 6 2 2 6 5 2 6
5 6 5 5 3 7 5 2 6 5 2 5
5 3 5 5 4 5 6 4 5 6 4 6
4 2 5 4 3 4 5 1 3 5 2 6
3 1 1 1 1 1 1 1 1 1 1 1
6 1 7 5 1 7 6 1 7 5 1 6
3 2 3 4 2 4 5 2 6 3 3 5
6 3 5 5 2 5 4 2 5 4 3 5
4 3 2 4 4 3 4 3 5 5 2 5
6 6 4 4 5 5 6 5 6 6 5 4
2 3 2 3 1 2 2 1 5 3 1 5
5 4 3 5 2 3 4 2 4 7 3 4
5 4 4 5 1 4 6 1 3 7 4 6
5 5 4 4 4 5 4 3 5 5 4 6
5 3 4 5 3 5 5 3 4 5 3 3
6 3 5 7 2 4 7 4 4 7 3 3
5 4 1 1 1 1 1 1 4 6 5 5
5 3 4 5 3 5 5 3 6 5 5 6
5 3 2 4 1 4 5 2 1 6 5 3
6 5 5 6 5 4 5 4 4 5 5 5
4 4 4 5 4 5 6 4 4 5 4 5
6 3 2 5 5 5 6 4 4 6 4 5
5 4 2 2 1 2 6 4 5 6 2 6
4 2 1 5 5 5 6 2 6 7 3 5
7 3 3 6 2 3 6 2 5 5 1 4
5 3 5 5 2 2 6 2 3 5 2 2
5 4 5 4 4 5 5 3 5 5 3 5
5 2 6 4 3 4 5 3 5 6 5 5
2 1 3 2 1 4 6 2 6 6 3 5
4 6 7 1 5 2 6 1 6 6 5 6
6 4 5 6 1 6 6 2 6 7 1 5
5 3 5 3 2 5 3 2 5 5 2 6
6 2 5 5 5 7 6 4 7 6 4 6
6 2 2 4 3 3 5 3 5 5 3 2
6 2 6 6 7 4 6 6 7 5 5 5
6 3 7 5 2 6 5 4 5 6 4 6
3 1 6 4 2 5 5 2 3 3 2 3
4 2 5 4 2 6 4 1 5 4 3 4
5 5 5 5 5 5 5 5 5 5 5 5
5 2 5 5 4 5 6 4 5 7 5 4
6 5 6 6 4 6 6 5 6 6 5 6
5 6 2 2 1 5 3 5 5 7 5 6
4 5 3 6 6 5 4 3 3 5 4 4
6 4 7 6 5 6 7 5 6 6 5 6
2 2 6 3 1 7 5 1 7 4 1 7
5 2 5 4 2 3 4 3 5 5 4 4
6 4 5 5 3 4 5 2 5 6 4 4
5 3 4 4 1 5 3 2 5 3 2 3
7 4 5 6 6 6 7 5 6 7 4 7
5 3 4 5 2 4 7 4 4 5 5 6
7 4 7 7 7 7 6 7 7 6 6 6
4 3 4 5 3 4 3 2 5 4 3 5
7 3 6 6 1 6 7 4 6 5 4 5
4 4 5 4 1 3 6 1 6 6 3 4
5 2 6 5 3 5 5 3 6 4 2 4
2 7 7 6 5 6 5 5 6 6 5 6
6 5 4 5 4 4 7 4 6 6 5 4
5 3 6 3 2 5 3 2 1 3 1 7
4 3 5 4 4 5 5 4 5 5 4 5
4 4 4 2 2 2 2 2 3 5 2 2
5 2 6 5 2 6 4 2 5 5 4 5
6 5 5 6 5 6 7 5 7 6 5 6
5 4 4 5 4 6 5 4 6 5 5 7
5 3 4 4 3 3 4 3 4 4 3 5
5 4 5 4 5 7 4 4 6 4 4 6
3 3 4 4 3 4 2 2 4 4 2 4
5 6 4 5 3 6 5 2 3 5 4 3
5 3 4 5 3 4 4 4 5 6 4 5
6 1 5 4 3 4 7 2 4 7 2 4
7 4 5 5 4 6 6 4 6 7 5 4
5 1 2 2 5 6 5 2 1 7 2 6
5 6 4 5 3 4 5 3 4 5 4 3
5 3 5 4 3 6 5 3 4 6 4 4
5 4 5 5 4 5 6 5 6 6 4 5
5 4 4 4 4 5 4 4 5 5 4 5
5 3 5 5 4 5 5 3 5 6 3 4
5 3 5 5 4 5 5 3 4 5 4 4
2 2 4 2 1 2 3 1 4 4 2 4
4 4 4 5 4 4 4 4 5 5 4 4
5 6 6 6 3 6 5 3 7 6 6 6
4 3 4 5 4 5 5 5 4 7 4 1
3 5 5 3 4 4 5 3 5 4 3 5
4 4 5 5 4 4 7 5 5 7 5 6
4 5 3 5 4 5 6 6 7 3 5 5
5 5 4 5 4 6 6 5 5 7 5 5
5 4 5 4 5 5 5 3 5 5 4 4
5 3 5 5 3 4 7 2 5 6 3 4
7 7 6 7 6 6 6 6 5 6 5 5
4 3 5 3 3 3 6 3 5 6 4 5
6 5 6 6 4 5 7 3 5 7 5 6
4 4 5 4 4 5 6 3 5 6 3 4
5 2 5 5 4 5 5 5 5 5 5 5
5 3 5 6 5 5 6 5 6 6 5 6
6 4 6 6 5 6 6 3 4 6 4 6
7 4 5 6 3 5 6 4 7 7 5 7
5 4 5 5 1 4 7 2 4 6 2 5
5 2 5 5 1 4 4 1 5 5 1 7
7 4 4 7 3 6 6 2 7 5 2 5
5 2 5 6 2 5 6 4 4 6 2 5
5 4 3 3 4 4 5 4 6 6 4 5
6 4 6 7 3 6 7 4 6 7 6 6
7 4 6 5 4 4 6 3 5 5 4 6
6 3 6 6 3 5 6 1 5 6 6 5
5 4 2 5 3 6 5 2 6 5 3 6
4 2 3 4 4 5 3 4 5 5 2 4
6 4 5 5 4 5 5 4 5 6 4 5
6 4 7 7 6 5 6 6 4 7 6 6
6 5 7 6 6 6 7 3 6 7 5 5
6 6 6 6 6 6 6 6 6 6 5 6
5 1 4 5 1 5 5 2 7 5 2 4
5 6 4 5 4 5 5 5 5 5 4 5
4 1 4 4 3 4 2 1 6 2 2 6
5 2 5 5 3 4 5 2 3 6 2 4
7 5 5 6 4 5 7 3 6 7 4 5
6 6 4 7 5 5 6 4 6 7 4 7
5 3 5 3 1 5 5 1 5 4 3 4
5 4 5 5 4 6 5 3 4 5 4 4
5 3 2 5 2 4 5 2 3 3 3 4
7 4 6 7 5 5 7 4 5 7 5 6
6 4 5 5 4 5 6 5 5 7 6 5
6 3 5 6 3 5 6 4 5 6 2 6
6 4 4 6 5 5 6 4 6 6 3 6
3 3 4 3 3 4 3 3 4 3 3 3
7 4 6 6 5 6 6 5 5 7 6 6
6 4 5 6 5 6 6 5 5 5 5 6
3 4 5 2 2 4 3 1 3 5 3 3
6 4 5 6 4 5 5 4 5 6 4 5
3 3 6 3 3 6 4 3 5 6 3 6
6 4 6 5 3 6 6 5 5 6 3 4
3 5 5 5 3 4 5 5 5 5 2 4
7 4 5 6 5 6 6 4 6 6 4 6
5 3 5 4 3 3 4 3 3 5 4 5
5 4 5 3 3 3 4 4 4 6 3 6
5 4 4 5 4 5 5 5 4 6 5 5
1 1 1 1 1 1 1 1 4 4 1 4
7 4 4 5 3 5 3 2 4 4 2 4
5 2 5 6 5 5 5 2 5 5 1 5
3 2 1 3 1 3 6 2 5 5 2 4
5 5 7 5 4 6 6 5 6 6 6 6
2 2 4 4 3 2 5 4 7 4 1 5
5 3 5 5 5 5 5 5 5 5 5 5
3 4 5 5 2 4 6 3 4 5 5 5
3 4 4 3 3 5 2 2 6 2 2 4
5 3 3 5 4 2 3 2 3 5 3 3
6 4 4 6 4 5 6 4 5 7 4 3
5 5 5 6 5 5 6 4 6 6 5 6
7 5 6 6 5 6 6 3 4 6 5 5
6 4 5 5 5 5 6 4 5 6 3 4
4 2 5 5 4 5 5 2 3 7 1 7
6 5 6 6 3 4 6 3 5 5 4 6
5 4 5 4 3 5 5 3 6 5 4 6
5 4 5 6 5 6 6 5 5 5 3 4
6 6 5 6 2 5 6 3 6 5 3 5
4 2 4 3 3 4 4 3 4 4 3 3
5 6 5 6 2 6 5 2 5 7 1 6
6 4 5 6 5 5 5 5 5 6 5 6
6 6 7 6 6 7 6 6 7 6 7 7
6 6 5 4 4 5 5 5 5 5 5 5
6 5 5 6 2 5 5 4 5 6 4 5
5 4 6 6 5 6 6 4 7 7 4 4
5 4 3 5 3 5 6 1 3 5 2 4
6 7 6 5 6 4 4 6 5 4 5 4
6 4 4 6 6 6 6 4 5 5 2 6
7 5 5 2 1 6 7 1 4 6 2 4
3 4 4 3 3 3 4 4 5 4 5 4
4 2 3 3 1 5 6 2 4 6 3 5
2 5 7 5 1 7 7 4 7 7 1 5
5 5 4 6 3 5 6 3 6 6 2 5
3 5 4 3 3 3 5 3 3 5 3 3
5 5 5 5 2 4 6 1 5 7 4 5
4 3 4 5 4 5 6 3 6 4 4 6
5 4 4 4 4 6 4 4 5 6 4 6
5 4 5 5 5 6 5 4 6 7 6 7
6 5 5 7 3 6 6 3 6 7 6 6
7 5 4 5 4 5 5 5 6 5 6 5
7 3 6 5 3 6 6 2 6 6 3 5
5 5 5 5 5 5 7 4 7 7 5 7
3 2 2 3 1 2 3 1 2 2 2 2
5 4 5 5 4 5 5 4 5 6 4 4
6 5 3 4 3 4 5 3 6 6 3 5
5 4 6 5 4 5 6 2 5 6 4 4
5 4 5 6 3 6 5 4 5 6 5 5
5 3 3 4 3 4 4 2 5 6 3 4
4 5 4 5 4 5 4 2 4 5 3 4
5 4 4 4 4 6 4 4 5 6 4 6
7 6 7 7 5 6 6 5 7 7 6 7
3 2 3 3 2 3 4 1 3 4 2 3
3 4 5 2 5 3 5 4 3 5 4 6
4 4 3 5 5 7 5 3 3 6 4 4
6 4 5 5 5 4 5 4 4 6 5 5
6 6 5 6 5 6 7 5 6 7 6 7
5 2 4 5 4 4 5 4 5 5 3 5
4 4 4 4 4 4 1 2 4 4 1 6
6 5 5 6 5 7 7 4 6 7 5 6
5 5 4 4 3 5 5 3 5 5 3 4
5 4 5 5 3 4 5 3 5 5 5 5
3 4 4 5 4 5 5 4 5 5 3 5
4 1 4 4 5 7 5 4 7 7 5 6
4 5 4 6 3 5 6 2 3 5 5 3
7 5 5 7 5 6 5 4 5 7 5 6
6 5 4 6 6 4 6 5 5 7 6 4
4 3 5 6 5 5 4 5 5 6 5 5
5 4 6 5 2 6 5 2 5 6 2 5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264968&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264968&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264968&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	8 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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	0.94	532	74	0.76	337	53	0.73
2	-0.25	200	321	-0.23	144	199	-0.16
3	0.4	365	170	0.36	252	108	0.4
4	0.78	487	109	0.63	323	72	0.64
5	-0.48	156	388	-0.43	117	231	-0.33
6	0.8	493	103	0.65	324	69	0.65
7	1.05	584	74	0.78	358	46	0.77
8	-0.69	125	463	-0.57	98	265	-0.46
9	0.85	524	109	0.66	333	71	0.65
10	1.41	724	38	0.9	398	28	0.87
11	-0.25	215	337	-0.22	160	203	-0.12
12	0.79	489	106	0.64	321	69	0.65

\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 & 0.94 & 532 & 74 & 0.76 & 337 & 53 & 0.73 \tabularnewline
2 & -0.25 & 200 & 321 & -0.23 & 144 & 199 & -0.16 \tabularnewline
3 & 0.4 & 365 & 170 & 0.36 & 252 & 108 & 0.4 \tabularnewline
4 & 0.78 & 487 & 109 & 0.63 & 323 & 72 & 0.64 \tabularnewline
5 & -0.48 & 156 & 388 & -0.43 & 117 & 231 & -0.33 \tabularnewline
6 & 0.8 & 493 & 103 & 0.65 & 324 & 69 & 0.65 \tabularnewline
7 & 1.05 & 584 & 74 & 0.78 & 358 & 46 & 0.77 \tabularnewline
8 & -0.69 & 125 & 463 & -0.57 & 98 & 265 & -0.46 \tabularnewline
9 & 0.85 & 524 & 109 & 0.66 & 333 & 71 & 0.65 \tabularnewline
10 & 1.41 & 724 & 38 & 0.9 & 398 & 28 & 0.87 \tabularnewline
11 & -0.25 & 215 & 337 & -0.22 & 160 & 203 & -0.12 \tabularnewline
12 & 0.79 & 489 & 106 & 0.64 & 321 & 69 & 0.65 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264968&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]0.94[/C][C]532[/C][C]74[/C][C]0.76[/C][C]337[/C][C]53[/C][C]0.73[/C][/ROW]
[ROW][C]2[/C][C]-0.25[/C][C]200[/C][C]321[/C][C]-0.23[/C][C]144[/C][C]199[/C][C]-0.16[/C][/ROW]
[ROW][C]3[/C][C]0.4[/C][C]365[/C][C]170[/C][C]0.36[/C][C]252[/C][C]108[/C][C]0.4[/C][/ROW]
[ROW][C]4[/C][C]0.78[/C][C]487[/C][C]109[/C][C]0.63[/C][C]323[/C][C]72[/C][C]0.64[/C][/ROW]
[ROW][C]5[/C][C]-0.48[/C][C]156[/C][C]388[/C][C]-0.43[/C][C]117[/C][C]231[/C][C]-0.33[/C][/ROW]
[ROW][C]6[/C][C]0.8[/C][C]493[/C][C]103[/C][C]0.65[/C][C]324[/C][C]69[/C][C]0.65[/C][/ROW]
[ROW][C]7[/C][C]1.05[/C][C]584[/C][C]74[/C][C]0.78[/C][C]358[/C][C]46[/C][C]0.77[/C][/ROW]
[ROW][C]8[/C][C]-0.69[/C][C]125[/C][C]463[/C][C]-0.57[/C][C]98[/C][C]265[/C][C]-0.46[/C][/ROW]
[ROW][C]9[/C][C]0.85[/C][C]524[/C][C]109[/C][C]0.66[/C][C]333[/C][C]71[/C][C]0.65[/C][/ROW]
[ROW][C]10[/C][C]1.41[/C][C]724[/C][C]38[/C][C]0.9[/C][C]398[/C][C]28[/C][C]0.87[/C][/ROW]
[ROW][C]11[/C][C]-0.25[/C][C]215[/C][C]337[/C][C]-0.22[/C][C]160[/C][C]203[/C][C]-0.12[/C][/ROW]
[ROW][C]12[/C][C]0.79[/C][C]489[/C][C]106[/C][C]0.64[/C][C]321[/C][C]69[/C][C]0.65[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264968&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264968&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	0.94	532	74	0.76	337	53	0.73
2	-0.25	200	321	-0.23	144	199	-0.16
3	0.4	365	170	0.36	252	108	0.4
4	0.78	487	109	0.63	323	72	0.64
5	-0.48	156	388	-0.43	117	231	-0.33
6	0.8	493	103	0.65	324	69	0.65
7	1.05	584	74	0.78	358	46	0.77
8	-0.69	125	463	-0.57	98	265	-0.46
9	0.85	524	109	0.66	333	71	0.65
10	1.41	724	38	0.9	398	28	0.87
11	-0.25	215	337	-0.22	160	203	-0.12
12	0.79	489	106	0.64	321	69	0.65

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	0.992 (0)	0.992 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	0.992 (0)	1 (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) & 0.992 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.992 (0) & 1 (0) & 1 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.992 (0) & 1 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264968&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]0.992 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.992 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.992 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264968&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264968&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)	0.992 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	0.992 (0)	1 (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)	0.992 (0)	0.969 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	0.977 (0)
(Pc-Nc)/(Pc+Nc)	0.969 (0)	0.977 (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) & 0.992 (0) & 0.969 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.992 (0) & 1 (0) & 0.977 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.969 (0) & 0.977 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264968&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]0.992 (0)[/C][C]0.969 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.992 (0)[/C][C]1 (0)[/C][C]0.977 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.969 (0)[/C][C]0.977 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264968&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264968&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)	0.992 (0)	0.969 (0)
(Ps-Ns)/(Ps+Ns)	0.992 (0)	1 (0)	0.977 (0)
(Pc-Nc)/(Pc+Nc)	0.969 (0)	0.977 (0)	1 (0)

Parameters (Session):

par1 = 1 2 3 4 5 6 7 ;

Parameters (R input):

par1 = 1 2 3 4 5 6 7 ;

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