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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareSurvey Scores
Date of computationThu, 06 Oct 2011 16:55:02 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Oct/06/t1317934562gzhtqu4tbty03h1.htm/, Retrieved Fri, 01 Nov 2024 01:04:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=127118, Retrieved Fri, 01 Nov 2024 01:04:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Survey Scores] [E-Learn 2008 table 1] [2008-09-07 11:33:54] [b98453cac15ba1066b407e146608df68]
F RM D  [Survey Scores] [ATTLES] [2010-04-05 12:47:13] [b98453cac15ba1066b407e146608df68]
F    D    [Survey Scores] [ATTLES Scores] [2010-10-03 16:55:42] [b98453cac15ba1066b407e146608df68]
- RM          [Survey Scores] [] [2011-10-06 20:55:02] [7dc03dd48c8acabd98b217fada4a6bc0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5	3	5	3	4	4	4	4	4	5	5	4	4	3	4	3	4	4	4	3
4	4	4	3	5	5	4	4	3	3	3	3	2	4	4	3	3	3	4	3
3	4	4	2	4	2	4	4	1	2	3	3	4	4	4	2	3	4	4	4
4	2	4	2	2	4	4	4	2	3	4	2	4	3	4	3	3	4	3	3
4	5	3	1	4	2	4	4	2	5	2	4	2	5	4	3	5	4	4	4
3	5	4	4	4	4	5	3	2	1	1	4	1	5	4	1	2	3	3	5
4	5	5	2	4	4	3	4	5	3	2	4	2	4	3	4	1	4	4	3
4	2	4	2	3	4	4	5	2	4	4	4	3	4	3	3	4	4	4	3
4	4	3	4	3	5	4	3	2	4	5	4	3	3	4	4	3	2	3	4
4	3	4	5	4	5	4	3	3	2	5	4	4	3	3	4	5	2	4	4
4	4	5	3	5	5	5	3	1	3	5	3	4	5	2	1	3	3	4	1
5	4	4	3	3	3	3	4	3	4	3	4	3	4	4	4	4	3	2	3
4	3	5	2	4	4	5	4	3	4	4	3	3	4	4	3	4	4	4	2
4	5	4	3	4	4	5	4	2	4	2	4	4	4	4	5	4	4	3	4
4	2	4	2	2	5	2	4	3	5	4	3	5	4	4	3	4	4	4	2
4	2	3	3	4	5	3	4	1	3	5	3	5	2	3	3	3	4	3	2
4	3	4	4	4	2	5	4	2	4	4	4	2	2	2	4	4	4	2	4
5	2	4	3	4	4	3	5	3	5	5	2	4	4	4	4	5	4	4	2
5	2	5	2	3	5	4	5	3	5	4	3	3	5	5	3	2	5	5	3
3	4	2	3	4	4	4	3	2	3	2	4	4	3	2	3	4	4	3	3
4	2	4	2	4	5	3	4	2	2	5	2	2	3	4	3	4	4	4	2
4	5	2	2	3	2	4	2	3	4	2	3	2	5	3	2	4	4	2	4
4	5	4	3	4	4	4	3	4	4	2	4	2	5	4	4	4	5	4	4
5	2	4	3	4	4	5	4	2	4	4	4	4	4	4	4	4	3	4	4
5	5	4	2	3	3	5	5	2	5	4	2	4	3	4	2	3	4	4	2
2	5	4	4	4	5	5	4	4	4	1	2	4	2	2	4	4	5	4	4
4	4	4	2	4	5	3	4	1	5	4	3	4	4	3	4	4	4	3	2
4	4	3	3	2	3	4	4	3	3	3	4	3	4	2	4	5	4	4	4
2	4	4	3	4	4	4	2	2	4	3	4	4	3	4	4	4	3	2	2
4	3	4	3	4	4	2	4	2	4	4	3	4	3	4	2	3	4	4	2
5	4	4	1	2	3	3	3	1	5	NA	4	4	4	4	3	4	2	2	1
3	2	5	1	1	2	4	4	2	3	4	3	3	2	4	3	2	4	4	3
4	4	3	4	4	4	4	2	4	4	2	3	2	2	4	4	4	4	3	3
4	3	4	3	2	4	4	3	3	4	3	4	4	4	4	2	4	4	4	4
4	2	4	4	3	4	5	2	2	4	2	1	4	2	4	4	4	3	2	4
4	2	4	3	3	4	2	3	2	5	4	2	4	4	2	4	3	4	4	2
3	1	4	3	3	4	3	4	1	3	4	3	4	1	4	4	2	4	4	1
5	2	4	3	4	4	4	3	3	4	3	4	4	2	4	3	4	3	4	2
4	4	3	2	3	3	3	2	1	4	3	3	4	3	3	4	4	3	2	2
4	2	4	2	4	4	4	4	3	4	4	3	4	3	4	3	3	3	4	2
5	4	5	3	3	4	4	4	2	3	4	3	4	3	4	2	4	4	2	2
4	2	4	3	4	4	3	4	2	4	4	4	2	4	2	3	4	4	2	4
5	3	4	4	4	4	4	4	2	4	4	4	3	3	3	1	3	4	4	3
4	4	2	5	3	3	5	3	2	4	3	3	3	4	3	4	3	3	4	3
2	4	4	5	4	4	4	4	2	5	4	4	2	4	1	2	4	1	2	4
5	4	3	3	2	4	4	4	4	4	4	3	4	4	2	3	4	4	4	3
4	2	4	4	5	4	4	4	2	5	4	4	4	4	5	2	4	4	4	4
4	3	4	3	3	3	4	3	2	4	4	4	4	3	3	3	4	3	4	2
4	3	4	3	3	4	4	4	3	4	5	4	4	3	4	3	4	4	4	3
4	3	5	3	4	4	3	4	3	5	4	3	4	4	3	2	4	4	2	2
4	3	2	2	4	4	4	4	2	3	4	3	4	4	4	3	4	4	4	4
4	2	4	3	3	4	3	3	2	4	3	3	4	2	3	3	3	4	3	2
4	3	5	2	5	2	4	4	1	2	4	2	3	2	4	2	4	4	4	4
4	4	4	3	2	2	3	5	3	4	4	4	4	3	4	4	4	4	4	3
2	5	3	2	3	4	4	4	2	3	3	4	4	4	2	4	4	3	2	2
4	5	4	4	5	3	2	4	1	5	5	3	4	4	5	NA	3	3	3	2
5	4	4	2	4	3	4	4	4	5	3	4	1	4	3	3	4	4	4	4
4	3	4	2	2	2	4	4	1	3	4	2	4	4	4	3	4	4	2	3
4	3	4	3	4	4	3	4	4	4	4	4	4	3	3	4	4	3	3	4
4	2	4	2	4	4	4	4	2	5	4	3	3	4	3	4	4	3	4	2
4	1	3	3	4	4	3	4	1	3	2	4	3	1	4	2	4	3	2	3
4	4	3	4	4	4	5	3	4	3	3	4	4	2	2	5	4	4	3	3
5	1	3	2	4	4	3	5	2	5	4	2	5	2	4	3	4	4	4	3
4	3	2	2	2	4	5	3	1	5	4	4	2	3	4	3	4	4	3	4
4	2	3	2	3	3	5	4	3	5	3	2	3	4	4	3	4	1	3	4
5	4	3	2	3	4	4	4	1	5	4	4	4	4	4	3	4	4	4	2
4	3	4	2	4	4	3	4	4	4	4	4	3	4	3	3	3	4	3	4
4	2	4	4	2	4	3	4	1	2	4	1	4	3	3	3	3	3	2	1
4	3	4	4	5	4	4	4	3	4	4	4	3	4	5	4	4	4	4	4
4	2	4	4	3	4	4	4	2	4	4	4	4	3	4	3	4	4	4	3
4	2	4	5	4	5	4	3	3	4	4	4	4	4	3	4	3	4	3	3
4	4	3	1	3	3	3	4	2	4	4	4	4	4	4	3	4	4	3	4
2	2	4	3	4	4	4	4	2	5	5	4	3	5	5	3	3	4	3	4
5	3	5	2	3	5	4	5	1	5	3	3	5	4	5	4	4	5	5	3
4	2	4	3	3	4	4	4	2	4	4	2	4	2	2	3	3	3	2	2
3	4	5	5	5	5	4	4	2	2	3	4	2	4	4	2	3	1	3	4
4	2	4	2	5	5	4	3	4	4	5	4	2	4	4	4	3	3	4	4
3	2	4	4	4	4	3	4	2	4	3	3	3	3	3	3	4	3	3	3
2	4	2	2	3	4	4	2	3	2	4	2	2	4	4	4	2	3	4	2
4	2	4	4	5	5	3	4	2	4	3	3	4	2	3	2	2	4	2	2
3	2	4	3	2	4	4	4	3	4	4	4	4	3	4	3	2	4	4	4
5	3	4	3	5	4	4	4	2	3	4	4	5	3	4	4	3	5	4	2
4	2	4	4	4	4	3	4	2	4	4	4	4	4	4	2	4	4	4	3
4	2	4	3	4	4	5	4	3	4	4	3	5	2	4	2	3	4	4	2
4	2	3	2	4	4	3	4	2	4	4	3	4	4	2	4	4	4	3	2
4	2	3	2	3	4	5	4	2	4	4	3	4	2	2	4	4	4	2	2
4	3	4	4	5	4	4	4	2	4	3	4	4	4	4	4	4	4	4	4
4	3	4	2	4	3	4	4	2	3	4	4	4	4	3	3	4	3	3	2
4	3	3	3	3	3	3	2	1	4	4	3	4	3	4	3	3	3	2	3
4	3	4	3	4	4	2	4	1	4	4	3	5	2	2	4	3	4	4	2
5	1	4	1	3	4	3	4	1	5	4	3	3	5	4	3	4	4	3	3
5	4	5	5	1	3	1	5	3	4	3	3	3	4	2	4	4	3	3	3
4	3	5	2	3	4	4	4	2	4	4	4	5	4	5	4	4	4	4	3
4	4	3	2	3	2	4	4	2	4	2	3	3	4	2	4	3	2	2	3
2	2	4	2	4	4	4	2	1	4	4	4	2	3	3	2	4	4	2	2
4	1	5	3	4	5	4	5	3	5	5	4	4	3	4	3	4	4	4	1
3	4	4	3	4	4	4	4	3	4	3	4	2	4	3	4	4	4	3	4
4	4	3	2	3	4	5	3	3	4	4	4	2	4	3	3	4	3	2	2
5	3	4	3	4	4	3	4	3	4	3	3	4	4	3	3	4	3	3	4
4	2	5	3	4	4	2	3	2	3	5	4	4	4	4	2	3	4	4	2
4	4	4	2	4	3	4	4	4	5	3	4	2	2	4	4	5	4	4	4
4	4	5	3	1	4	3	5	3	5	4	5	3	1	4	5	4	3	2	4
4	4	4	2	3	5	4	5	3	2	4	5	2	5	4	2	5	2	4	4
4	2	2	3	3	4	4	4	2	4	3	2	4	4	3	2	4	2	2	2
4	3	3	2	3	4	4	4	2	4	5	4	4	4	3	4	4	3	4	4
3	4	5	4	5	5	4	5	1	4	3	4	4	2	2	2	4	4	4	3
5	2	4	4	5	4	4	4	2	4	4	5	4	2	4	4	4	4	2	2
4	5	5	5	4	5	2	4	2	5	4	4	4	3	4	4	2	5	5	4
4	2	4	4	4	4	4	2	3	5	4	2	4	2	4	4	5	4	4	2
5	5	5	3	1	4	5	5	5	5	3	5	3	5	4	5	5	3	4	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=127118&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=127118&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=127118&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Summary of survey scores (median of Likert score was subtracted)
QuestionmeanSum ofpositives (Ps)Sum ofnegatives (Ns)(Ps-Ns)/(Ps+Ns)Count ofpositives (Pc)Count ofnegatives (Nc)(Pc-Nc)/(Pc+Nc)
10.9711470.889370.86
20.0855460.0943410.02
30.8610270.878370.84
4-0.123447-0.162742-0.22
50.5276190.663150.62
60.8710590.848690.81
70.759290.827580.81
80.789590.838290.8
9-0.661588-0.711368-0.68
100.92111100.838390.8
110.6585140.7271120.71
120.4163180.5659160.57
130.4775230.5367210.52
140.470260.4660230.45
150.4872190.5865180.57
160.2149260.3145230.32
170.6582100.787490.78
180.5977120.737190.78
190.3462250.4359250.4
20-0.054146-0.063941-0.02

\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 & 0.97 & 114 & 7 & 0.88 & 93 & 7 & 0.86 \tabularnewline
2 & 0.08 & 55 & 46 & 0.09 & 43 & 41 & 0.02 \tabularnewline
3 & 0.86 & 102 & 7 & 0.87 & 83 & 7 & 0.84 \tabularnewline
4 & -0.12 & 34 & 47 & -0.16 & 27 & 42 & -0.22 \tabularnewline
5 & 0.52 & 76 & 19 & 0.6 & 63 & 15 & 0.62 \tabularnewline
6 & 0.87 & 105 & 9 & 0.84 & 86 & 9 & 0.81 \tabularnewline
7 & 0.75 & 92 & 9 & 0.82 & 75 & 8 & 0.81 \tabularnewline
8 & 0.78 & 95 & 9 & 0.83 & 82 & 9 & 0.8 \tabularnewline
9 & -0.66 & 15 & 88 & -0.71 & 13 & 68 & -0.68 \tabularnewline
10 & 0.92 & 111 & 10 & 0.83 & 83 & 9 & 0.8 \tabularnewline
11 & 0.65 & 85 & 14 & 0.72 & 71 & 12 & 0.71 \tabularnewline
12 & 0.41 & 63 & 18 & 0.56 & 59 & 16 & 0.57 \tabularnewline
13 & 0.47 & 75 & 23 & 0.53 & 67 & 21 & 0.52 \tabularnewline
14 & 0.4 & 70 & 26 & 0.46 & 60 & 23 & 0.45 \tabularnewline
15 & 0.48 & 72 & 19 & 0.58 & 65 & 18 & 0.57 \tabularnewline
16 & 0.21 & 49 & 26 & 0.31 & 45 & 23 & 0.32 \tabularnewline
17 & 0.65 & 82 & 10 & 0.78 & 74 & 9 & 0.78 \tabularnewline
18 & 0.59 & 77 & 12 & 0.73 & 71 & 9 & 0.78 \tabularnewline
19 & 0.34 & 62 & 25 & 0.43 & 59 & 25 & 0.4 \tabularnewline
20 & -0.05 & 41 & 46 & -0.06 & 39 & 41 & -0.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=127118&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]0.97[/C][C]114[/C][C]7[/C][C]0.88[/C][C]93[/C][C]7[/C][C]0.86[/C][/ROW]
[ROW][C]2[/C][C]0.08[/C][C]55[/C][C]46[/C][C]0.09[/C][C]43[/C][C]41[/C][C]0.02[/C][/ROW]
[ROW][C]3[/C][C]0.86[/C][C]102[/C][C]7[/C][C]0.87[/C][C]83[/C][C]7[/C][C]0.84[/C][/ROW]
[ROW][C]4[/C][C]-0.12[/C][C]34[/C][C]47[/C][C]-0.16[/C][C]27[/C][C]42[/C][C]-0.22[/C][/ROW]
[ROW][C]5[/C][C]0.52[/C][C]76[/C][C]19[/C][C]0.6[/C][C]63[/C][C]15[/C][C]0.62[/C][/ROW]
[ROW][C]6[/C][C]0.87[/C][C]105[/C][C]9[/C][C]0.84[/C][C]86[/C][C]9[/C][C]0.81[/C][/ROW]
[ROW][C]7[/C][C]0.75[/C][C]92[/C][C]9[/C][C]0.82[/C][C]75[/C][C]8[/C][C]0.81[/C][/ROW]
[ROW][C]8[/C][C]0.78[/C][C]95[/C][C]9[/C][C]0.83[/C][C]82[/C][C]9[/C][C]0.8[/C][/ROW]
[ROW][C]9[/C][C]-0.66[/C][C]15[/C][C]88[/C][C]-0.71[/C][C]13[/C][C]68[/C][C]-0.68[/C][/ROW]
[ROW][C]10[/C][C]0.92[/C][C]111[/C][C]10[/C][C]0.83[/C][C]83[/C][C]9[/C][C]0.8[/C][/ROW]
[ROW][C]11[/C][C]0.65[/C][C]85[/C][C]14[/C][C]0.72[/C][C]71[/C][C]12[/C][C]0.71[/C][/ROW]
[ROW][C]12[/C][C]0.41[/C][C]63[/C][C]18[/C][C]0.56[/C][C]59[/C][C]16[/C][C]0.57[/C][/ROW]
[ROW][C]13[/C][C]0.47[/C][C]75[/C][C]23[/C][C]0.53[/C][C]67[/C][C]21[/C][C]0.52[/C][/ROW]
[ROW][C]14[/C][C]0.4[/C][C]70[/C][C]26[/C][C]0.46[/C][C]60[/C][C]23[/C][C]0.45[/C][/ROW]
[ROW][C]15[/C][C]0.48[/C][C]72[/C][C]19[/C][C]0.58[/C][C]65[/C][C]18[/C][C]0.57[/C][/ROW]
[ROW][C]16[/C][C]0.21[/C][C]49[/C][C]26[/C][C]0.31[/C][C]45[/C][C]23[/C][C]0.32[/C][/ROW]
[ROW][C]17[/C][C]0.65[/C][C]82[/C][C]10[/C][C]0.78[/C][C]74[/C][C]9[/C][C]0.78[/C][/ROW]
[ROW][C]18[/C][C]0.59[/C][C]77[/C][C]12[/C][C]0.73[/C][C]71[/C][C]9[/C][C]0.78[/C][/ROW]
[ROW][C]19[/C][C]0.34[/C][C]62[/C][C]25[/C][C]0.43[/C][C]59[/C][C]25[/C][C]0.4[/C][/ROW]
[ROW][C]20[/C][C]-0.05[/C][C]41[/C][C]46[/C][C]-0.06[/C][C]39[/C][C]41[/C][C]-0.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=127118&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=127118&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Summary of survey scores (median of Likert score was subtracted)
QuestionmeanSum ofpositives (Ps)Sum ofnegatives (Ns)(Ps-Ns)/(Ps+Ns)Count ofpositives (Pc)Count ofnegatives (Nc)(Pc-Nc)/(Pc+Nc)
10.9711470.889370.86
20.0855460.0943410.02
30.8610270.878370.84
4-0.123447-0.162742-0.22
50.5276190.663150.62
60.8710590.848690.81
70.759290.827580.81
80.789590.838290.8
9-0.661588-0.711368-0.68
100.92111100.838390.8
110.6585140.7271120.71
120.4163180.5659160.57
130.4775230.5367210.52
140.470260.4660230.45
150.4872190.5865180.57
160.2149260.3145230.32
170.6582100.787490.78
180.5977120.737190.78
190.3462250.4359250.4
20-0.054146-0.063941-0.02






Pearson correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.985 (0)0.977 (0)
(Ps-Ns)/(Ps+Ns)0.985 (0)1 (0)0.997 (0)
(Pc-Nc)/(Pc+Nc)0.977 (0)0.997 (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) & 0.985 (0) & 0.977 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.985 (0) & 1 (0) & 0.997 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.977 (0) & 0.997 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=127118&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]0.985 (0)[/C][C]0.977 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.985 (0)[/C][C]1 (0)[/C][C]0.997 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.977 (0)[/C][C]0.997 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=127118&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=127118&T=2

As an alternative you can also use a QR Code:  
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)
mean1 (0)0.985 (0)0.977 (0)
(Ps-Ns)/(Ps+Ns)0.985 (0)1 (0)0.997 (0)
(Pc-Nc)/(Pc+Nc)0.977 (0)0.997 (0)1 (0)






Kendall tau rank correlations of survey scores (and p-values)
mean(Ps-Ns)/(Ps+Ns)(Pc-Nc)/(Pc+Nc)
mean1 (0)0.942 (0)0.912 (0)
(Ps-Ns)/(Ps+Ns)0.942 (0)1 (0)0.971 (0)
(Pc-Nc)/(Pc+Nc)0.912 (0)0.971 (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) & 0.942 (0) & 0.912 (0) \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.942 (0) & 1 (0) & 0.971 (0) \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.912 (0) & 0.971 (0) & 1 (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=127118&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]0.942 (0)[/C][C]0.912 (0)[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.942 (0)[/C][C]1 (0)[/C][C]0.971 (0)[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.912 (0)[/C][C]0.971 (0)[/C][C]1 (0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=127118&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=127118&T=3

As an alternative you can also use a QR Code:  
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)
mean1 (0)0.942 (0)0.912 (0)
(Ps-Ns)/(Ps+Ns)0.942 (0)1 (0)0.971 (0)
(Pc-Nc)/(Pc+Nc)0.912 (0)0.971 (0)1 (0)


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 2 3 4 5 ;
Parameters (R input):
par1 = 1 2 3 4 5 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')