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Author

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R Software Module

rwasp_surveyscores.wasp

Title produced by software

Survey Scores

Date of computation

Fri, 12 Dec 2014 13:29:33 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418391021mdo7earngwvkvtv.htm/, Retrieved Thu, 16 May 2024 10:03:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266671, Retrieved Thu, 16 May 2024 10:03:02 +0000

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Estimated Impact

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

-       [Survey Scores] [] [2014-12-12 13:29:33] [921bde233b8ec180d9061abb09deed53] [Current]

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1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
1 1 1 0 1 1 1 1 1
1 1 0 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 0 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
0 1 1 1 1 0 1 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 0 1 1
1 1 0 1 1 1 1 1 1
1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 0 1 0 1 1 1 0 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 0
1 1 1 1 1 1 0 1 0
1 1 0 1 1 1 1 1 1
0 1 1 1 1 1 0 1 1
1 1 1 1 0 1 1 1 1
1 1 1 1 0 1 1 1 1
1 1 0 0 0 0 0 1 0
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
1 1 1 1 1 0 1 1 1
1 0 1 1 1 0 0 1 1
1 0 0 0 1 0 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 0 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 0 1 1 1
1 0 1 1 1 1 1 1 0
0 0 0 0 0 0 1 1 1
1 1 1 1 1 1 1 0 0
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
0 1 1 1 0 1 1 1 1
1 1 1 1 0 1 1 1 1
1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1
1 0 0 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 1 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
0 1 1 1 1 1 0 1 1
1 1 1 1 1 0 1 1 1
1 1 0 1 0 1 1 1 1
1 1 1 1 1 1 0 0 1
1 1 1 1 1 1 1 1 1
1 0 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 0 1 0 1 1
1 0 0 1 1 1 0 1 1
1 0 0 0 1 0 0 1 0
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 0
0 1 1 0 1 1 1 1 0
1 1 1 1 1 1 1 1 1
1 0 1 1 1 1 1 1 1
1 1 0 1 1 1 1 1 1
0 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 0 0 1 1 0 1 1
1 1 0 0 1 1 1 1 0
1 1 1 1 0 1 1 1 1
1 1 1 1 0 1 1 1 1
1 1 1 1 1 1 1 1 0
1 0 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 0
1 1 1 1 1 0 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
0 1 0 0 0 1 1 1 0
1 1 0 1 1 1 0 1 1
1 1 1 1 1 1 0 1 1
1 0 0 1 0 1 1 1 1
1 1 0 1 1 1 1 1 0
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1 1 1 1 1 0 1 1 1
1 1 1 1 1 1 0 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 1 0 1 0 0 1 1 0
0 1 0 1 1 1 0 1 1
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0 1 1 1 1 0 1 1 1
1 0 1 1 1 0 1 1 1
0 1 1 1 1 0 1 1 1
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1 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 1 1 1 1 0 1 1 1
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1 1 1 1 1 0 1 1 0
1 1 1 1 1 1 0 0 0
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0 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1
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0 1 1 1 1 1 0 1 0
1 1 1 1 1 1 0 1 1
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1 0 1 1 0 1 1 1 1
1 1 0 1 1 0 1 1 0
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1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1
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1 1 1 1 1 0 1 1 1
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1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
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0 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 0
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1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1
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0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
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1 1 0 0 1 1 1 1 1
0 1 1 0 1 1 1 1 1
1 1 0 1 0 1 0 1 1
1 0 1 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1
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1 1 0 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 0 1 0 1 1 1 1 1
1 1 1 1 0 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 1 1 1 1 0 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 1 1 1 0 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 0 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 0 1 1 1 0 1 1
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1 1 1 1 1 0 1 1 1
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1 1 1 1 1 1 1 1 0
0 0 1 1 1 0 1 1 1
1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 1
1 0 1 0 1 1 1 1 1
0 1 1 1 1 0 0 0 0
0 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 1 1 0 1 1 1 1 1
0 1 0 0 0 1 1 0 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 1 0 0 0 1 1 1 1
0 0 1 1 1 0 0 0 0
1 1 0 1 1 0 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 0 1 0 1 1
0 1 1 1 1 1 1 1 0
1 1 1 1 1 1 0 1 1
0 1 1 1 1 1 1 0 1
1 1 0 1 1 1 1 1 0
1 0 0 1 1 0 1 1 1
1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 0 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 0 1 1 1 1 0 1 1
1 1 1 0 1 1 0 1 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 0
1 1 1 0 0 1 0 1 1
1 1 1 1 1 1 1 1 1
0 1 1 0 1 1 1 1 0
0 0 1 1 1 1 1 0 0
1 1 0 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 0 1 1 1 1 1 0
0 1 1 1 1 1 1 1 0
0 0 0 1 1 1 1 1 0
1 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
0 1 1 0 0 0 0 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
0 1 1 1 1 0 1 1 1
1 1 0 1 1 1 1 1 1
1 0 0 0 0 0 0 0 0
1 1 0 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1
0 1 1 0 1 1 0 1 1
1 1 1 0 1 1 0 1 1
1 1 1 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
0 0 0 0 0 0 0 1 1
1 0 0 0 0 0 1 1 1
0 1 0 0 1 1 1 1 0
1 1 1 0 1 1 1 1 1
0 0 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 1 1 1 1 1 1
0 0 0 0 1 1 0 1 1
1 1 0 0 1 1 1 1 1
0 1 1 0 1 1 1 1 1
0 1 1 1 1 1 1 1 1
0 1 0 0 1 0 1 1 1
0 1 0 0 1 0 1 1 1
1 1 1 0 1 1 1 1 0
0 1 1 0 1 1 1 1 1
1 1 1 1 1 1 0 1 1
0 0 1 0 1 1 1 1 1
1 1 0 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
0 1 1 1 1 1 1 1 1
0 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 0 1 0 1 1 0
1 1 1 0 1 1 1 1 1
0 1 0 1 1 0 1 1 0
0 1 0 0 0 1 0 1 0
1 1 1 0 0 1 1 0 1
1 0 0 0 1 0 1 1 0
0 0 0 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 0 1 0 0 1 1
0 0 1 0 1 1 1 1 1
1 0 1 1 1 1 1 1 0
1 1 1 0 0 0 1 1 1
1 1 1 1 1 1 1 1 1
0 1 0 1 1 0 1 1 1
1 1 1 0 0 1 1 1 1
1 1 1 0 1 1 1 1 1
0 0 1 1 0 0 1 1 1
0 1 0 0 0 0 1 1 1
0 1 0 0 0 1 1 0 1
0 0 1 0 1 1 0 1 0
0 1 0 0 1 1 0 1 1
1 0 0 0 1 0 0 1 1
0 1 1 0 1 1 1 1 0
0 0 1 1 1 0 1 1 0
0 1 0 0 0 1 1 1 1
1 1 0 1 0 0 1 1 1
0 1 0 0 1 0 1 1 1
1 1 0 0 1 1 1 1 1
0 1 1 0 0 1 1 1 1
0 1 1 0 1 1 1 1 0
0 1 1 1 1 1 1 1 1
1 1 0 1 0 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 0 0 0 1 1 1 0
1 1 0 0 1 1 1 1 1
1 1 0 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
0 1 1 0 1 1 1 1 1
0 1 1 1 1 1 1 0 1
1 1 1 1 1 1 1 1 1
0 1 1 0 1 1 1 1 0
1 0 0 1 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 0 0 0 1 0 1 1 1
0 1 1 0 0 0 1 1 1
1 1 0 0 1 1 0 1 0
1 1 1 0 1 1 1 1 1
1 1 0 0 1 0 0 1 1
0 1 0 1 1 1 1 1 1
1 1 0 0 1 0 1 1 1
1 1 0 0 1 0 0 1 1
0 1 1 0 1 1 1 1 1
0 1 1 0 0 1 1 1 1
1 1 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 0 1 1
0 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 1 0 1
1 0 1 0 1 1 1 1 1
1 1 0 0 1 1 0 1 1
1 1 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0
0 1 0 1 1 1 1 1 1
0 1 0 0 1 0 1 1 1
1 1 0 0 1 1 1 1 0
1 1 1 1 1 1 1 0 1
1 0 1 1 1 1 0 1 1
0 0 1 0 1 1 1 1 1
0 0 0 1 0 1 1 1 0
1 1 0 1 0 1 0 1 1
1 1 0 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 0 1 1 1 0 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 0 1 0 1 1 1 1 1
0 1 0 0 0 0 1 1 1
1 1 0 0 1 0 1 1 1
1 0 1 0 1 1 1 1 1
1 0 1 1 1 1 1 1 1
0 1 0 0 1 1 1 1 1
1 1 1 0 1 0 1 1 1
1 1 1 0 1 1 1 1 1
1 0 1 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 0 1 1
1 1 1 0 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 0 1 0 1 1 1 1 0
1 1 1 0 1 1 1 1 1
1 1 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 0 1 1 1 1 1
0 1 1 0 1 1 1 1 0
0 1 0 1 1 0 0 1 1
1 1 1 0 1 1 1 1 1
1 0 1 1 1 1 1 1 1
1 1 1 0 0 0 1 1 1
0 0 0 0 1 1 1 1 0
1 1 1 1 1 1 1 1 1
0 0 1 1 1 1 0 1 1
0 1 1 0 1 0 0 1 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 0 1 0 1 1 0 1 0
1 0 0 1 1 1 1 1 1
0 1 0 0 0 1 1 1 1
1 1 1 0 1 1 1 1 1
0 1 0 1 1 1 1 1 1
1 1 1 0 1 1 1 1 1
1 1 1 0 1 1 0 1 0
1 1 1 1 1 1 1 1 1
1 0 0 0 1 1 0 1 1
1 1 1 0 1 0 0 1 1
0 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 0 1 0 1 1 1
1 1 1 0 1 0 1 1 0
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 0 0 1 0 1 1 1
0 1 1 0 1 1 0 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 0 0 1 1 0 1 1 1
1 1 0 0 1 1 0 1 1
0 1 1 0 1 1 1 1 1
1 1 1 0 1 0 1 1 1
1 0 0 0 1 0 0 0 0
1 1 0 0 1 0 1 1 1
1 1 1 1 1 1 1 1 1
0 0 1 0 1 1 1 1 1
0 1 0 0 0 1 1 1 1
0 0 1 0 1 1 1 1 1
1 1 1 0 1 1 1 1 1
0 0 0 1 0 0 0 1 1
1 0 1 0 1 1 0 1 1
1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 1 0 1 1 1 1 1
1 0 0 0 1 1 1 1 0
1 0 1 1 0 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 1 1 0 1 1 1 1 1
0 1 1 0 1 0 1 1 1
1 1 1 1 1 1 1 1 1
1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1
1 1 0 0 1 1 1 1 1
1 1 1 1 1 0 1 1 1
1 0 1 0 1 1 1 1 1
0 0 1 1 1 0 0 1 1
0 1 1 0 1 1 1 1 1
1 1 1 0 0 1 1 1 1
1 0 0 1 0 1 1 1 1
0 1 1 1 1 1 1 1 0
1 1 0 0 1 1 1 0 1
1 1 1 1 1 1 1 1 1
1 0 0 1 1 0 1 1 1
1 1 1 0 1 0 1 1 1
0 0 0 0 1 1 1 1 0
0 1 1 1 1 1 1 1 1
1 1 0 0 1 1 0 1 1
1 0 1 0 0 0 1 1 1
1 0 1 0 1 1 1 1 1
1 0 1 1 1 1 1 1 1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266671&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]5 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=266671&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266671&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	5 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	0.2	170	73.5	0.4	340	147	0.4
2	0.29	192	51.5	0.58	384	103	0.58
3	0.24	179.5	64	0.47	359	128	0.47
4	0.13	152.5	91	0.25	305	182	0.25
5	0.36	210.5	33	0.73	421	66	0.73
6	0.29	193	50.5	0.59	386	101	0.59
7	0.29	192	51.5	0.58	384	103	0.58
8	0.45	232.5	11	0.91	465	22	0.91
9	0.31	198	45.5	0.63	396	91	0.63

\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 & 0.2 & 170 & 73.5 & 0.4 & 340 & 147 & 0.4 \tabularnewline
2 & 0.29 & 192 & 51.5 & 0.58 & 384 & 103 & 0.58 \tabularnewline
3 & 0.24 & 179.5 & 64 & 0.47 & 359 & 128 & 0.47 \tabularnewline
4 & 0.13 & 152.5 & 91 & 0.25 & 305 & 182 & 0.25 \tabularnewline
5 & 0.36 & 210.5 & 33 & 0.73 & 421 & 66 & 0.73 \tabularnewline
6 & 0.29 & 193 & 50.5 & 0.59 & 386 & 101 & 0.59 \tabularnewline
7 & 0.29 & 192 & 51.5 & 0.58 & 384 & 103 & 0.58 \tabularnewline
8 & 0.45 & 232.5 & 11 & 0.91 & 465 & 22 & 0.91 \tabularnewline
9 & 0.31 & 198 & 45.5 & 0.63 & 396 & 91 & 0.63 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266671&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]0.2[/C][C]170[/C][C]73.5[/C][C]0.4[/C][C]340[/C][C]147[/C][C]0.4[/C][/ROW]
[ROW][C]2[/C][C]0.29[/C][C]192[/C][C]51.5[/C][C]0.58[/C][C]384[/C][C]103[/C][C]0.58[/C][/ROW]
[ROW][C]3[/C][C]0.24[/C][C]179.5[/C][C]64[/C][C]0.47[/C][C]359[/C][C]128[/C][C]0.47[/C][/ROW]
[ROW][C]4[/C][C]0.13[/C][C]152.5[/C][C]91[/C][C]0.25[/C][C]305[/C][C]182[/C][C]0.25[/C][/ROW]
[ROW][C]5[/C][C]0.36[/C][C]210.5[/C][C]33[/C][C]0.73[/C][C]421[/C][C]66[/C][C]0.73[/C][/ROW]
[ROW][C]6[/C][C]0.29[/C][C]193[/C][C]50.5[/C][C]0.59[/C][C]386[/C][C]101[/C][C]0.59[/C][/ROW]
[ROW][C]7[/C][C]0.29[/C][C]192[/C][C]51.5[/C][C]0.58[/C][C]384[/C][C]103[/C][C]0.58[/C][/ROW]
[ROW][C]8[/C][C]0.45[/C][C]232.5[/C][C]11[/C][C]0.91[/C][C]465[/C][C]22[/C][C]0.91[/C][/ROW]
[ROW][C]9[/C][C]0.31[/C][C]198[/C][C]45.5[/C][C]0.63[/C][C]396[/C][C]91[/C][C]0.63[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266671&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266671&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	0.2	170	73.5	0.4	340	147	0.4
2	0.29	192	51.5	0.58	384	103	0.58
3	0.24	179.5	64	0.47	359	128	0.47
4	0.13	152.5	91	0.25	305	182	0.25
5	0.36	210.5	33	0.73	421	66	0.73
6	0.29	193	50.5	0.59	386	101	0.59
7	0.29	192	51.5	0.58	384	103	0.58
8	0.45	232.5	11	0.91	465	22	0.91
9	0.31	198	45.5	0.63	396	91	0.63

Pearson correlations of survey scores (and p-values)
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1 (0)	1 (0)	1 (0)
(Ps-Ns)/(Ps+Ns)	1 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	1 (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) & 1 (0) & 1 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 1 (0) & 1 (0) & 1 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 1 (0) & 1 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266671&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]1 (0)[/C][C]1 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]1 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]1 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266671&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266671&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)	1 (0)	1 (0)
(Ps-Ns)/(Ps+Ns)	1 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	1 (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)	0.971 (0)	0.971 (0)
(Ps-Ns)/(Ps+Ns)	0.971 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	0.971 (0)	1 (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) & 0.971 (0) & 0.971 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.971 (0) & 1 (0) & 1 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.971 (0) & 1 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266671&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]0.971 (0)[/C][C]0.971 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.971 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.971 (0)[/C][C]1 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266671&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266671&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)	0.971 (0)	0.971 (0)
(Ps-Ns)/(Ps+Ns)	0.971 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	0.971 (0)	1 (0)	1 (0)

Parameters (Session):

par1 = 0 1 ;

Parameters (R input):

par1 = 0 1 ;

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