Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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 13:06:57 +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/t1418216836vrr6qsftwy73cdi.htm/, Retrieved Thu, 12 Feb 2026 17:08:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265094, Retrieved Thu, 12 Feb 2026 17:08:39 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

311

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

-       [Survey Scores] [Kendall Tau NUMALG] [2014-12-10 13:06:57] [ddb851b9ced255c1d64c58a7ca49fb28] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265094&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]11 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=265094&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265094&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	11 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	-0.03	167	185.5	-0.05	334	371	-0.05
2	0.1	210	142.5	0.19	420	285	0.19
3	-0.04	161	191.5	-0.09	322	383	-0.09
4	-0.32	62	290.5	-0.65	124	581	-0.65
5	-0.31	66	286.5	-0.63	132	573	-0.63
6	-0.06	154.5	198	-0.12	309	396	-0.12
7	0.4	319	33.5	0.81	638	67	0.81
8	0.22	254.5	98	0.44	509	196	0.44
9	-0.28	79	273.5	-0.55	158	547	-0.55
10	-0.35	54	298.5	-0.69	108	597	-0.69

\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 & -0.03 & 167 & 185.5 & -0.05 & 334 & 371 & -0.05 \tabularnewline
2 & 0.1 & 210 & 142.5 & 0.19 & 420 & 285 & 0.19 \tabularnewline
3 & -0.04 & 161 & 191.5 & -0.09 & 322 & 383 & -0.09 \tabularnewline
4 & -0.32 & 62 & 290.5 & -0.65 & 124 & 581 & -0.65 \tabularnewline
5 & -0.31 & 66 & 286.5 & -0.63 & 132 & 573 & -0.63 \tabularnewline
6 & -0.06 & 154.5 & 198 & -0.12 & 309 & 396 & -0.12 \tabularnewline
7 & 0.4 & 319 & 33.5 & 0.81 & 638 & 67 & 0.81 \tabularnewline
8 & 0.22 & 254.5 & 98 & 0.44 & 509 & 196 & 0.44 \tabularnewline
9 & -0.28 & 79 & 273.5 & -0.55 & 158 & 547 & -0.55 \tabularnewline
10 & -0.35 & 54 & 298.5 & -0.69 & 108 & 597 & -0.69 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265094&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]-0.03[/C][C]167[/C][C]185.5[/C][C]-0.05[/C][C]334[/C][C]371[/C][C]-0.05[/C][/ROW]
[ROW][C]2[/C][C]0.1[/C][C]210[/C][C]142.5[/C][C]0.19[/C][C]420[/C][C]285[/C][C]0.19[/C][/ROW]
[ROW][C]3[/C][C]-0.04[/C][C]161[/C][C]191.5[/C][C]-0.09[/C][C]322[/C][C]383[/C][C]-0.09[/C][/ROW]
[ROW][C]4[/C][C]-0.32[/C][C]62[/C][C]290.5[/C][C]-0.65[/C][C]124[/C][C]581[/C][C]-0.65[/C][/ROW]
[ROW][C]5[/C][C]-0.31[/C][C]66[/C][C]286.5[/C][C]-0.63[/C][C]132[/C][C]573[/C][C]-0.63[/C][/ROW]
[ROW][C]6[/C][C]-0.06[/C][C]154.5[/C][C]198[/C][C]-0.12[/C][C]309[/C][C]396[/C][C]-0.12[/C][/ROW]
[ROW][C]7[/C][C]0.4[/C][C]319[/C][C]33.5[/C][C]0.81[/C][C]638[/C][C]67[/C][C]0.81[/C][/ROW]
[ROW][C]8[/C][C]0.22[/C][C]254.5[/C][C]98[/C][C]0.44[/C][C]509[/C][C]196[/C][C]0.44[/C][/ROW]
[ROW][C]9[/C][C]-0.28[/C][C]79[/C][C]273.5[/C][C]-0.55[/C][C]158[/C][C]547[/C][C]-0.55[/C][/ROW]
[ROW][C]10[/C][C]-0.35[/C][C]54[/C][C]298.5[/C][C]-0.69[/C][C]108[/C][C]597[/C][C]-0.69[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265094&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265094&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	-0.03	167	185.5	-0.05	334	371	-0.05
2	0.1	210	142.5	0.19	420	285	0.19
3	-0.04	161	191.5	-0.09	322	383	-0.09
4	-0.32	62	290.5	-0.65	124	581	-0.65
5	-0.31	66	286.5	-0.63	132	573	-0.63
6	-0.06	154.5	198	-0.12	309	396	-0.12
7	0.4	319	33.5	0.81	638	67	0.81
8	0.22	254.5	98	0.44	509	196	0.44
9	-0.28	79	273.5	-0.55	158	547	-0.55
10	-0.35	54	298.5	-0.69	108	597	-0.69

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=265094&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=265094&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265094&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)	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
Kendall tau rank 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=265094&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]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=265094&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265094&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)	1 (0)	1 (0)
(Ps-Ns)/(Ps+Ns)	1 (0)	1 (0)	1 (0)
(Pc-Nc)/(Pc+Nc)	1 (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