Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_partial_least_squares.wasp
Title produced by softwarePartial Least Squares - Path Modeling
Date of computationTue, 13 Dec 2011 14:46:44 -0500
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/Dec/13/t1323805631ntb1v5rriydbux1.htm/, Retrieved Thu, 02 May 2024 15:32:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154666, Retrieved Thu, 02 May 2024 15:32:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Partial Least Squares - Path Modeling] [] [2011-12-13 19:46:44] [9fcdc23b96f67ca1860b0ed8ec932927] [Current]
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Dataseries X:
1	8	9	5	6	9	9	5	8	9	6	8	2	7	7	7	10	9	6	6	7	6	7	9	9	6	6
9	9	10	9	7	9	8	10	8	9	7	9	7	8	7	8	8	7	8	8	7	8	7	8	9	8	8
9	8	8	8	8	8	8	8	9	9	7	9	6	8	6	8	9	7	8	9	8	8	8	9	8	9	9
8	9	8	9	7	10	6	3	10	10	7	3	2	10	8	6	7	6	4	7	7	7	6	6	6	5	5
10	10	8	10	8	7	9	8	9	8	7	9	8	9	8	9	10	10	8	8	8	8	7	8	9	9	9
7	8	8	8	8	8	8	8	9	10	6	6	7	7	7	8	7	6	5	7	6	6	7	7	8	8	7
5	5	5	5	2	7	6	5	6	6	4	4	2	3	3	5	6	4	5	6	5	6	4	5	6	6	5
9	9	9	9	9	10	10	10	10	10	8	9	9	9	9	8	8	8	8	8	8	8	8	9	8	3	8
9	8	9	8	5	7	8	7	7	5	6	8	7	7	5	9	8	7	7	8	7	6	6	7	7	5	8
10	10	10	10	10	10	10	10	10	10	10	10	8	10	10	10	10	10	10	10	10	9	10	10	8	8	10
2	2	8	5	6	7	8	6	8	7	7	8	6	8	7	7	8	8	8	7	7	7	6	9	3	9	8
7	8	8	7	8	7	8	7	5	7	7	8	7	8	7	8	6	6	7	8	7	8	8	8	8	8	7
8	9	9	8	7	8	10	10	10	10	6	7	7	7	8	10	8	8	7	7	7	8	8	8	8	9	9
7	7	8	8	8	10	10	10	10	9	7	8	8	8	7	8	9	10	7	9	8	7	7	9	4	8	8
3	4	3	8	3	7	7	5	6	6	4	4	3	4	8	4	4	4	3	4	4	3	4	4	3	4	4
7	8	8	5	6	7	9	7	9	9	5	8	6	8	7	8	8	7	6	10	8	8	7	7	7	8	7
8	8	8	8	7	5	0	0	7	8	7	0	1	7	8	6	8	5	1	7	7	8	6	9	7	7	8
7	8	8	8	7	7	7	7	9	7	7	7	7	9	7	8	9	8	7	9	9	9	9	10	10	9	8
8	4	3	8	0	8	5	5	2	8	5	4	4	3	5	3	5	5	3	5	3	4	3	2	10	1	0
8	7	6	10	7	8	7	6	8	9	8	8	7	7	7	8	8	7	6	7	7	7	5	4	4	5	5
9	9	10	8	8	8	9	9	8	9	8	6	8	8	8	8	9	7	7	8	7	8	7	8	9	8	9
6	6	6	6	6	7	7	7	6	7	4	5	6	5	5	4	5	4	5	4	5	4	3	3	9	3	4
8	8	8	7	5	7	10	9	10	10	5	8	8	8	8	7	7	8	6	6	7	8	9	8	7	9	4
9	9	10	9	9	10	10	10	10	10	10	9	8	9	9	10	10	7	10	8	8	8	5	10	10	10	10
8	7	8	8	8	8	9	10	10	9	8	9	8	10	9	9	8	9	8	8	8	8	9	8	8	8	8
8	5	5	4	3	3	3	3	4	5	0	0	0	1	2	2	1	1	0	3	2	4	5	0	1	0	0
6	6	6	6	5	6	7	7	7	7	3	5	5	4	7	5	6	3	5	5	6	3	4	4	4	4	5
7	8	9	8	4	5	5	8	8	6	5	5	8	8	6	8	9	9	6	9	8	7	7	9	8	8	10
8	8	8	7	7	7	7	7	6	7	6	7	6	6	6	7	7	7	6	7	6	6	6	7	8	7	8
8	8	8	8	7	8	8	9	9	8	8	8	8	8	7	9	8	9	8	9	8	7	8	9	8	9	9
7	7	7	6	6	7	5	8	8	6	7	5	8	6	8	7	7	9	7	8	6	7	7	7	7	7	8
9	9	9	9	5	8	9	5	9	10	8	9	5	9	10	6	9	8	7	8	8	8	8	8	8	8	9
10	10	10	10	5	6	1	5	8	5	6	4	6	8	5	9	9	10	5	8	10	8	8	10	2	9	9
9	8	9	9	8	9	9	9	9	8	8	8	7	8	7	8	7	7	8	8	5	9	7	9	9	9	9
8	9	9	9	5	7	6	6	8	7	7	6	6	8	7	8	7	7	6	8	7	8	6	7	8	8	8
3	4	2	5	2	3	1	5	4	1	5	2	3	4	2	4	2	2	4	6	2	3	2	4	4	3	4
8	8	9	9	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8
8	6	7	6	6	6	7	6	6	7	5	6	5	6	7	7	7	8	6	6	7	7	6	7	6	5	5
10	10	10	10	5	9	8	10	9	5	8	9	10	9	5	7	10	10	8	10	8	8	7	10	10	10	10
8	10	9	8	7	10	9	10	10	10	8	6	7	7	7	8	8	7	6	9	8	8	7	7	7	10	8
8	8	8	8	8	7	8	8	8	8	7	8	8	8	8	8	8	7	8	9	9	8	7	7	8	8	8
5	7	7	8	7	3	10	8	3	8	6	7	5	5	5	5	7	7	3	5	6	6	5	1	6	3	10
10	9	9	10	5	5	9	5	10	9	5	9	5	10	9	7	9	9	9	8	9	7	7	9	9	9	8
9	7	7	9	3	8	0	5	8	5	5	0	2	5	4	3	6	4	2	6	6	6	6	1	8	2	1
8	8	10	8	8	5	10	7	5	4	8	10	8	7	5	9	8	7	8	9	6	5	7	8	7	8	8
6	8	8	8	8	8	9	9	9	9	7	8	8	8	8	9	10	10	9	9	8	5	5	10	9	9	10
8	8	8	8	8	8	9	9	10	9	8	9	6	8	9	10	8	7	10	9	7	8	6	8	8	7	10
6	5	5	6	6	10	10	8	8	6	7	7	6	6	7	6	6	4	6	7	5	5	3	3	5	0	3
9	9	9	8	7	5	8	8	8	8	6	8	7	8	8	8	9	8	7	8	8	8	6	6	7	8	10
9	10	8	8	5	7	7	9	8	7	7	9	7	8	9	9	10	10	9	8	8	7	7	7	8	9	7
8	8	8	9	7	5	5	5	5	5	6	7	8	7	6	6	8	7	6	7	7	6	5	8	9	8	8
8	5	6	7	7	6	8	8	8	5	6	8	10	8	7	8	8	7	7	8	8	7	6	4	6	7	6
4	1	4	9	0	4	2	0	7	5	5	2	0	7	5	0	0	7	0	0	0	0	0	0	10	0	10
5	5	7	5	5	9	9	7	10	9	7	7	7	7	7	7	7	8	5	7	7	7	7	7	7	5	5
5	5	6	5	5	7	7	7	8	8	5	4	5	5	6	4	4	4	3	3	4	3	3	3	2	3	3
7	9	9	10	3	5	9	1	6	9	5	9	3	7	10	5	9	9	9	10	7	10	9	9	6	7	6
10	10	10	10	7	7	7	7	7	9	9	6	8	8	7	8	9	9	6	7	7	7	6	7	8	8	10
8	8	8	8	4	8	8	7	7	7	8	8	7	7	7	6	9	8	7	7	7	7	5	7	6	6	2
6	6	7	9	7	8	8	8	8	9	7	7	5	7	7	8	8	7	6	6	7	7	6	1	9	0	4
6	7	7	7	3	4	4	3	5	1	4	4	3	3	3	6	5	5	5	7	6	6	8	5	7	3	5
9	9	9	9	7	5	9	10	10	10	7	7	8	8	8	8	8	9	6	8	7	7	8	8	7	6	10
7	7	7	7	6	6	8	7	7	7	7	8	7	7	7	6	8	8	6	6	6	5	10	7	7	7	8
9	7	8	9	7	6	7	6	7	8	6	7	6	7	8	8	10	8	9	7	7	8	3	8	9	7	9
8	9	9	8	8	9	9	8	8	8	8	8	8	8	8	7	8	7	8	7	8	7	6	8	6	7	8
8	8	8	8	7	7	7	5	6	6	7	7	5	6	6	6	8	8	5	7	7	6	5	8	9	8	7
7	7	7	7	7	4	4	6	10	10	5	5	6	9	10	4	5	6	6	1	5	6	5	3	6	7	7
9	10	9	9	8	7	9	8	10	5	6	8	6	9	7	8	9	8	8	8	7	8	8	9	8	9	9
8	9	9	8	6	8	6	8	6	7	8	6	8	6	7	8	8	8	7	6	6	5	6	8	7	8	9
7	8	8	8	2	6	4	4	7	5	6	4	4	7	5	6	7	7	6	7	6	7	5	8	5	7	7
4	2	2	6	4	6	8	6	7	4	6	5	6	5	5	5	5	4	4	4	6	7	2	6	4	3	2
5	5	7	5	5	5	3	5	5	2	5	3	2	5	2	5	5	5	2	4	5	5	4	2	9	5	5
9	9	10	9	8	8	10	4	9	8	6	10	5	9	6	7	8	7	8	8	8	6	9	8	9	8	8
8	8	8	8	5	9	9	10	10	10	9	9	7	8	8	7	7	7	9	7	7	5	6	8	9	7	8
8	8	9	8	6	8	7	7	8	6	8	9	9	8	8	8	9	5	9	8	8	8	6	8	5	7	6
9	9	9	9	0	4	8	6	6	5	4	8	6	6	5	6	8	8	4	7	7	8	8	7	5	6	6
9	8	8	8	7	8	8	7	9	8	8	8	8	9	8	7	9	9	9	10	9	7	10	10	10	10	10
9	9	9	8	8	8	9	8	9	9	7	9	7	9	9	8	8	8	7	8	8	8	7	7	7	7	7
8	5	7	9	2	4	3	1	3	2	4	3	1	3	2	2	8	8	2	5	5	5	5	9	10	1	9
7	6	9	8	6	7	7	8	8	8	5	7	6	8	5	5	7	7	6	6	7	6	7	5	7	4	4
8	8	9	8	7	6	7	6	8	7	6	7	6	8	7	6	7	7	6	8	7	8	6	8	7	7	10
10	10	10	10	10	10	10	5	10	8	10	8	5	10	5	8	10	10	7	9	9	9	7	8	8	7	8
9	9	6	6	10	10	10	10	10	10	10	10	10	10	10	4	5	5	5	2	3	3	3	2	3	5	4
3	3	3	8	2	1	0	4	0	1	0	0	3	0	1	0	7	0	0	0	0	8	8	0	7	0	10
5	4	4	7	1	5	3	1	7	2	4	3	2	4	3	4	4	4	2	3	3	3	3	0	4	2	8
8	10	10	10	8	8	10	8	10	10	10	10	8	10	10	7	8	7	10	10	9	9	8	10	9	8	10
5	8	8	8	7	8	8	10	10	8	6	8	7	10	10	7	8	8	6	7	8	6	6	7	7	7	10
7	4	5	9	4	8	8	6	8	7	6	6	6	6	5	5	8	7	5	6	6	6	6	5	8	0	7
5	6	5	8	9	7	8	5	9	5	6	8	5	9	6	10	9	0	8	8	8	5	4	8	8	8	5
7	5	7	6	4	2	2	2	8	3	2	3	2	5	4	3	6	6	3	5	5	5	5	5	6	6	5
10	8	9	8	7	6	9	4	9	8	7	9	4	9	8	8	8	8	6	8	7	5	8	8	8	6	6
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	8	10	10	9	9	8	9	10	10	10
6	6	4	5	4	4	7	6	6	4	4	4	6	4	5	0	5	5	5	6	4	5	5	5	8	5	9
10	3	7	9	8	5	1	7	10	0	5	0	5	10	0	0	5	7	7	5	5	5	5	10	8	10	10
5	7	8	7	7	10	10	10	10	9	9	8	8	8	7	8	7	8	7	8	6	7	6	8	6	7	6
9	10	9	9	8	10	8	10	7	10	10	8	10	5	10	10	6	6	7	10	7	7	10	9	8	8	8
10	10	10	10	10	10	9	5	9	9	10	8	5	9	9	9	10	10	9	9	9	9	9	10	10	10	10
10	10	10	10	5	5	9	2	7	8	7	7	2	6	7	10	10	10	8	7	6	8	8	10	10	6	10
7	7	7	8	7	8	7	8	6	8	7	8	8	7	8	7	8	8	6	8	7	8	8	8	8	7	7
9	5	7	9	3	6	7	7	8	4	3	6	7	8	4	6	8	8	4	6	5	5	4	6	7	4	5
9	9	10	8	8	8	8	10	8	9	6	7	8	6	9	8	9	8	7	8	7	5	7	8	8	7	9
6	6	5	4	4	4	5	5	5	4	4	4	5	5	4	4	4	4	3	4	5	5	5	4	3	4	5
7	8	8	9	6	7	7	7	6	6	5	5	5	7	7	7	5	4	7	6	4	4	7	10	8	8	8
8	6	6	5	0	6	7	10	7	10	10	6	3	6	7	7	6	0	8	7	7	6	7	7	9	7	7
6	8	8	9	9	7	9	9	8	9	8	10	8	10	10	9	9	9	5	9	5	5	9	9	8	7	9
9	5	6	7	5	9	6	6	9	7	7	9	8	9	7	10	10	8	7	8	8	7	8	8	9	10	10
8	7	8	5	5	5	4	10	8	3	0	6	2	9	8	8	7	4	5	3	5	3	5	8	5	5	9
7	8	7	4	7	7	8	5	8	0	7	5	6	7	1	8	8	8	7	8	7	7	7	7	6	6	7
8	7	10	10	5	9	10	10	6	10	9	10	10	7	6	10	10	10	10	9	6	4	8	10	10	10	10
5	9	10	5	0	6	10	10	10	10	10	9	4	10	10	9	9	6	8	8	7	9	9	10	10	10	9
9	6	6	7	7	10	10	10	9	10	6	10	10	9	9	10	10	6	6	10	10	10	9	9	10	10	9
0	4	7	5	5	7	10	8	10	10	10	10	10	10	10	10	8	9	9	8	9	9	9	9	7	8	7
4	8	8	6	5	9	9	10	8	7	8	10	10	10	10	9	9	7	2	8	8	8	7	9	9	9	9
8	8	10	8	5	7	5	10	8	7	5	8	8	6	9	7	7	5	5	7	7	6	7	5	10	8	3
9	10	10	7	6	0	5	9	7	3	0	0	0	10	10	5	4	0	7	5	4	5	9	10	10	10	10
10	10	10	10	9	2	7	8	7	10	3	4	8	7	6	7	0	0	8	4	5	3	3	2	8	1	0
10	8	8	8	7	7	8	5	8	8	8	8	8	8	4	5	5	8	8	6	6	5	5	10	10	10	10
7	10	10	8	5	8	7	3	8	10	8	8	6	8	8	10	10	8	8	7	8	10	6	10	9	10	10
10	9	9	6	5	8	8	8	10	9	8	10	0	10	10	9	9	0	9	8	7	10	7	10	8	9	9
8	10	8	5	6	5	7	4	7	6	8	8	8	10	8	7	6	5	7	6	6	7	7	10	10	10	10
10	8	8	7	8	7	8	8	7	8	5	6	4	9	10	8	8	7	5	8	8	7	8	10	8	8	10
8	7	8	8	8	7	9	3	9	7	7	8	0	6	7	10	10	2	1	9	7	9	10	8	10	8	8
8	8	8	8	6	8	8	8	7	7	5	8	6	7	6	6	7	7	4	7	5	6	7	8	8	7	7
7	9	8	8	7	9	8	8	10	8	7	7	8	8	8	9	8	8	7	10	9	7	7	9	9	9	7
8	8	9	9	8	8	8	8	8	7	7	7	7	8	7	7	7	8	7	7	7	8	8	7	8	7	7
8	7	8	7	6	8	8	8	7	8	7	8	7	7	8	8	8	8	7	8	8	8	7	8	8	7	8
8	7	7	8	7	9	7	9	8	8	8	7	7	7	8	7	7	8	7	7	7	8	7	8	8	7	8
8	8	8	8	7	8	8	8	7	8	8	8	8	7	8	7	6	8	6	8	8	6	6	9	8	7	9
8	8	8	9	7	8	9	9	9	8	8	9	9	9	8	9	6	8	9	8	8	8	5	8	9	10	10
7	8	8	6	8	8	9	4	9	10	8	9	5	9	8	8	9	9	6	8	8	6	8	8	8	8	8
7	9	9	8	5	7	7	5	8	6	7	7	5	8	6	6	8	8	5	7	5	7	4	8	7	5	9
10	10	10	8	8	8	10	8	10	10	8	10	10	10	10	9	8	8	8	10	10	9	9	10	10	10	10
6	8	7	8	8	6	8	6	9	9	7	9	6	8	9	9	8	10	8	8	7	7	5	8	9	9	8
8	8	8	8	5	9	10	10	10	9	8	8	7	8	7	8	8	8	8	8	8	8	7	8	8	5	10
10	0	0	10	0	10	3	3	6	3	10	0	0	3	0	0	7	5	0	0	0	0	0	0	3	0	10
8	5	9	4	4	8	5	9	8	6	5	3	9	5	8	6	8	8	5	6	5	5	4	6	6	4	5
9	9	9	9	7	8	8	8	10	9	6	6	7	8	6	8	8	8	7	7	7	8	8	7	3	0	9
8	9	8	8	8	10	8	7	9	7	9	9	9	8	7	8	8	8	8	8	8	9	5	8	9	9	8
7	7	7	7	5	10	10	8	9	10	7	8	5	7	7	7	7	7	5	8	7	5	6	7	7	7	5
9	9	9	9	7	6	8	6	8	7	6	7	6	9	7	7	7	8	8	8	6	6	7	8	7	8	6
5	7	4	10	0	1	1	0	5	5	0	0	0	5	5	0	10	10	0	0	0	0	0	5	5	0	10
9	9	9	9	7	8	9	9	10	10	9	9	9	10	8	9	10	10	9	10	10	5	5	10	10	10	10
8	8	9	7	8	7	7	8	8	8	7	7	6	7	8	7	8	8	7	8	8	6	8	8	7	7	8
9	10	10	7	9	10	9	8	10	9	8	9	5	9	9	9	9	8	4	8	7	9	9	9	9	9	8
10	10	10	10	9	10	10	10	10	10	9	10	7	8	7	10	10	9	7	7	7	5	9	10	10	10	9
8	6	9	9	4	8	8	5	8	6	5	6	5	7	6	7	9	8	5	6	7	5	4	6	6	5	5
7	7	8	5	5	6	6	10	7	9	4	4	7	5	8	8	8	7	6	5	6	6	6	5	5	7	4
9	10	10	10	7	9	9	9	9	9	8	7	8	8	8	9	8	9	8	9	8	6	7	8	8	8	9
7	7	6	6	5	8	8	7	7	3	5	5	4	4	4	4	6	6	4	4	4	4	4	4	6	3	5
8	8	8	8	7	8	9	8	8	5	8	9	7	8	8	9	8	7	9	9	8	7	7	10	3	7	10
8	8	8	8	8	5	8	5	8	5	5	8	5	8	8	7	8	5	8	8	7	7	7	10	10	7	10
10	10	10	10	10	10	10	10	10	9	10	10	10	10	9	10	10	10	10	10	10	10	10	10	10	10	9
10	10	10	10	7	8	9	9	10	9	7	9	8	10	9	9	10	10	9	9	9	9	8	10	10	10	10
9	10	10	10	9	9	10	8	8	9	9	10	8	8	9	8	10	10	8	9	8	8	7	10	10	10	10
4	6	8	8	6	7	7	6	10	9	7	6	6	7	6	7	7	7	5	6	6	7	5	4	7	5	9
8	8	8	7	7	8	9	9	9	9	7	8	7	8	8	7	8	9	7	8	9	9	9	7	5	7	4
8	8	8	8	6	6	6	4	7	7	6	6	2	7	7	6	7	7	4	7	7	7	5	6	10	6	6
8	8	4	8	0	2	5	5	9	0	3	5	3	7	0	5	5	5	1	2	2	2	2	0	5	0	10
7	7	7	7	8	7	8	8	9	8	6	6	6	8	6	7	8	8	4	6	6	6	6	7	7	6	6
8	7	7	8	8	9	8	9	8	9	9	7	7	6	8	10	9	8	9	9	8	7	8	10	9	8	9
9	9	7	8	8	9	8	9	9	9	8	6	8	7	8	9	8	8	8	9	7	8	6	8	8	9	8
5	3	4	3	6	6	1	2	7	2	4	1	2	4	4	4	5	3	5	4	2	4	7	2	3	5	4
6	8	8	8	8	9	8	9	9	7	8	8	8	8	7	9	9	8	7	8	8	7	7	9	8	9	10
9	10	10	10	6	8	9	7	10	6	6	9	5	10	5	6	8	9	8	10	9	6	9	0	10	10	10
4	5	5	7	5	7	8	8	5	6	4	6	5	4	3	4	4	5	4	5	5	3	3	2	4	2	7
8	8	8	8	8	10	4	4	10	7	8	5	5	8	5	10	10	10	10	10	7	7	7	10	10	10	8
9	9	9	9	8	9	8	9	8	9	9	8	9	7	9	10	8	8	8	9	9	8	8	8	8	7	8
10	10	10	10	10	8	8	8	9	8	8	8	8	9	8	8	8	8	8	9	9	8	8	8	8	8	9
10	10	10	9	9	9	9	3	10	10	9	9	2	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	6	8	10	5	10	5	5	10	5	7	5	5	5	8	5	5	10	6	8	8	5
7	8	8	8	3	7	8	1	7	4	6	8	3	7	4	4	5	5	4	7	6	8	4	5	4	5	8
8	8	8	8	5	8	8	8	8	8	5	7	5	8	6	5	8	8	5	6	6	5	5	5	5	5	7
8	6	8	5	2	2	5	5	2	2	2	5	5	5	2	5	5	5	3	5	3	5	3	3	9	3	2
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	9	10	10	9	10	10	10
7	7	7	7	8	8	5	5	7	5	8	5	5	7	5	7	8	8	5	6	7	5	5	6	7	7	9
8	7	7	8	6	7	7	7	7	7	7	7	7	7	7	8	9	5	5	7	7	6	6	6	7	6	5
10	10	10	10	5	10	10	10	10	10	10	10	9	10	10	9	10	10	10	10	9	10	5	9	10	10	10
7	9	9	9	8	9	9	8	8	9	9	9	8	8	9	8	9	8	8	9	10	8	8	9	8	8	7
9	8	8	8	7	9	7	9	8	9	7	7	7	8	8	9	9	8	7	9	8	7	8	10	8	9	10
8	6	6	7	2	8	4	9	8	9	6	4	6	6	6	5	7	5	4	6	5	5	4	5	2	5	5
8	9	9	9	7	8	8	8	8	8	6	5	7	6	5	6	8	7	6	7	6	7	6	6	5	5	7
7	8	9	9	8	8	8	9	7	8	8	8	9	7	8	9	10	10	7	10	10	5	9	10	5	8	8
9	8	8	8	5	6	7	7	7	8	6	7	7	7	8	8	9	7	8	8	8	9	2	0	5	7	10
10	10	10	10	8	7	9	7	9	10	7	9	7	9	10	6	10	7	8	9	9	7	6	10	9	2	8
10	10	9	10	9	6	10	10	10	9	6	10	10	10	9	10	10	10	9	10	10	9	10	10	10	10	10
8	9	9	8	8	8	8	8	9	8	8	8	8	9	8	8	8	8	8	8	8	9	8	8	8	8	8
8	10	10	10	8	9	9	9	9	9	8	8	7	8	8	8	8	8	8	9	8	9	8	9	9	9	8
8	8	8	5	5	7	8	7	8	6	7	8	7	6	6	5	8	8	8	7	7	5	7	6	8	10	5
6	6	7	7	1	6	10	5	7	8	6	7	5	5	6	6	7	5	2	6	10	6	6	5	5	5	0
10	10	10	10	8	7	8	8	8	8	7	8	8	8	8	5	10	10	10	10	10	5	10	10	10	10	10
8	7	8	8	5	8	10	7	10	8	6	8	6	7	7	7	9	9	7	7	7	5	6	6	8	8	8
8	7	8	7	7	8	8	8	8	8	7	8	8	7	8	8	8	8	8	8	7	7	6	9	9	8	9
7	7	7	8	2	8	8	9	8	8	8	8	9	7	8	8	9	8	7	7	7	7	7	8	8	8	9
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	9	6	8	9	8	9	8	7	9	8	9	8	7	9	9	7	8	8	8	8	8	8	8	7
10	10	10	10	6	10	10	10	10	10	10	10	10	10	10	10	10	10	5	9	10	9	9	10	10	10	10
10	10	10	10	10	10	10	7	10	10	9	9	5	8	10	10	10	10	10	10	10	9	9	10	10	10	10
10	10	10	9	8	10	10	10	10	10	10	10	9	10	10	10	10	9	9	10	10	7	9	10	6	10	10
7	9	9	8	3	7	6	7	8	8	7	6	7	8	8	9	10	9	8	8	7	5	7	8	8	8	8
9	9	9	9	7	10	10	10	10	10	10	8	9	9	8	9	9	8	8	9	8	8	8	9	9	8	7
6	5	5	7	0	7	4	1	8	5	7	5	0	5	3	1	5	6	5	3	0	5	5	5	10	5	10
10	10	10	10	6	10	10	8	10	8	10	7	8	10	8	7	10	10	10	10	10	5	10	10	10	10	10
8	9	10	9	7	9	9	8	10	9	9	9	8	10	9	9	10	9	9	10	9	10	9	9	10	10	9
6	8	8	9	5	5	5	6	5	4	5	5	6	5	4	3	8	5	5	5	5	6	6	5	5	7	6
9	9	9	9	9	5	10	5	8	5	5	10	5	8	5	5	8	5	6	8	8	8	8	9	9	8	10
8	9	9	8	7	7	8	7	8	7	7	8	7	8	7	5	8	8	8	7	7	5	7	8	8	7	9
6	7	7	7	5	6	8	8	7	7	3	6	7	5	7	6	6	6	3	5	5	5	5	3	6	0	6
8	8	8	9	7	8	8	7	8	8	9	8	7	8	8	7	8	8	8	7	7	7	6	5	8	7	10
8	8	9	6	7	7	5	9	7	5	7	5	9	7	5	6	7	5	5	7	7	5	5	10	5	0	10
8	9	9	7	8	8	8	7	9	9	6	7	6	7	8	6	7	7	6	7	6	7	7	9	7	7	7
8	4	6	7	2	3	7	6	7	7	3	6	5	6	4	3	6	6	6	6	5	4	6	0	6	0	9
9	9	9	9	9	10	10	10	10	10	8	9	8	8	9	9	8	8	8	8	8	8	8	7	8	8	8
6	7	7	6	6	7	9	7	8	6	5	8	8	7	7	6	6	4	4	2	3	3	4	5	6	8	5
6	7	7	7	5	8	7	5	8	4	7	5	6	5	5	4	6	4	4	6	7	7	5	4	8	4	5
8	7	7	9	6	8	10	7	8	5	7	10	8	8	8	7	10	5	6	6	7	6	7	8	8	6	9
10	10	10	9	7	5	8	7	6	5	5	8	5	8	8	8	7	8	7	5	5	5	4	8	9	9	8
8	7	8	10	5	4	8	8	6	5	4	8	5	5	2	8	9	6	7	8	8	7	8	8	7	8	8
8	9	7	8	5	10	6	10	10	9	8	6	8	7	9	9	8	8	7	6	8	8	8	8	7	5	9
7	7	5	7	9	9	10	9	10	8	7	9	9	10	9	8	8	6	6	7	7	6	7	9	7	8	8
9	9	10	8	7	8	9	8	8	9	6	7	6	6	8	8	8	6	5	7	7	5	6	10	10	10	10
8	6	9	9	6	10	10	10	10	10	10	10	10	10	10	8	9	8	9	9	9	7	8	8	7	7	8
7	7	8	9	7	8	9	9	7	7	8	9	8	7	7	7	7	7	8	6	8	7	7	7	8	9	7
7	7	8	7	7	10	8	7	7	8	10	9	8	9	10	8	7	8	7	7	8	7	7	8	8	6	5
9	10	9	9	5	9	10	10	10	10	9	10	10	9	9	8	7	9	9	7	9	8	8	6	6	6	6
7	7	7	6	8	8	7	5	10	5	7	4	3	8	4	6	6	9	9	6	9	9	8	8	3	5	8
9	10	7	8	7	8	9	7	9	6	9	9	9	9	9	7	5	10	10	6	8	9	7	10	10	7	10
4	6	5	7	8	9	8	6	8	9	9	8	6	8	9	4	4	4	3	4	4	3	4	10	9	10	9
8	8	8	8	10	8	6	6	7	6	8	9	9	8	8	8	8	7	6	10	8	8	7	10	8	10	10
8	6	3	8	8	8	6	5	7	6	7	7	6	9	7	6	8	5	1	7	7	8	6	10	7	10	10
7	7	8	7	9	6	8	8	8	5	8	9	7	8	8	8	8	8	6	7	7	5	6	4	6	5	9
9	10	9	9	7	4	7	6	9	5	5	8	5	7	6	8	7	7	9	5	9	8	6	7	4	7	4
10	10	8	9	8	10	10	10	10	9	10	10	10	10	9	7	6	5	8	7	9	9	5	6	7	6	6
4	6	9	8	8	8	7	8	10	7	7	9	8	10	9	9	9	7	7	8	7	9	4	0	1	0	0
8	8	7	7	6	8	8	8	6	9	9	10	8	8	9	4	6	5	5	3	5	4	3	4	4	4	5
8	8	8	8	7	6	7	6	8	9	7	6	6	7	6	7	7	8	6	6	7	7	9	8	7	8	9
8	8	3	4	8	9	9	8	9	8	10	10	10	10	10	9	8	7	6	7	8	8	5	6	8	6	8
6	8	8	7	8	9	9	7	7	5	8	6	6	8	6	5	7	7	7	7	7	7	7	8	7	8	8
9	10	9	8	8	5	5	7	8	6	7	7	8	8	7	7	8	8	8	8	8	8	8	6	7	6	8
4	5	5	7	10	7	6	7	8	9	8	8	8	8	7	6	9	6	8	8	10	10	7	9	9	8	8
6	8	8	8	7	7	6	5	5	6	8	6	5	6	6	8	8	8	6	9	9	9	8	5	4	6	6
9	9	7	7	8	8	9	7	8	10	7	8	6	7	7	4	6	5	3	4	3	7	5	3	5	3	6
8	9	5	5	5	8	7	7	8	6	6	5	5	5	5	7	8	7	6	7	6	7	4	6	7	6	6
10	10	8	7	8	8	8	7	7	7	5	8	5	8	5	7	8	6	5	5	6	8	8	6	8	5	7
7	9	8	7	6	6	6	10	7	9	4	4	7	5	8	7	8	10	8	9	8	8	7	9	8	7	8
9	9	9	9	5	8	8	7	8	4	5	5	4	5	1	6	3	4	4	6	3	4	4	7	8	6	8
6	5	5	7	7	10	10	10	10	10	10	10	9	10	8	6	7	6	4	7	7	7	6	9	8	8	9
9	9	9	9	8	6	7	5	8	5	5	8	5	8	8	9	10	10	8	8	8	8	7	7	6	8	8
8	9	9	7	8	9	8	7	7	9	7	7	8	8	6	9	9	8	8	7	6	7	8	3	5	3	6
8	8	8	8	8	8	7	7	7	3	5	5	4	5	4	4	5	5	8	8	7	8	6	8	2	8	7
8	9	9	4	8	5	10	7	7	7	9	4	8	8	9	8	9	9	7	9	7	8	7	6	7	8	6
10	9	7	7	9	8	6	4	7	7	5	7	2	7	7	7	8	8	8	7	7	7	6	8	7	5	7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Servervre.aston.ac.uk @ vre.aston.ac.uk

\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 & 13 seconds \tabularnewline
R Server & vre.aston.ac.uk @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]vre.aston.ac.uk @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Servervre.aston.ac.uk @ vre.aston.ac.uk







PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
MODEL SPECIFICATION
Number of Cases250
Latent Variables6
Manifest Variables27
Scaled?TRUE
Weighting Schemecentroid
Bootstrapping?TRUE
Bootstrap samples100

\begin{tabular}{lllllllll}
\hline
PARTIAL LEAST SQUARES PATH MODELING (PLS-PM) \tabularnewline
MODEL SPECIFICATION \tabularnewline
Number of Cases & 250 \tabularnewline
Latent Variables & 6 \tabularnewline
Manifest Variables & 27 \tabularnewline
Scaled? & TRUE \tabularnewline
Weighting Scheme & centroid \tabularnewline
Bootstrapping? & TRUE \tabularnewline
Bootstrap samples & 100 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=1

[TABLE]
[ROW][C]PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)[/C][/ROW]
[ROW][C]MODEL SPECIFICATION[/C][/ROW]
[ROW][C]Number of Cases[/C][C]250[/C][/ROW]
[ROW][C]Latent Variables[/C][C]6[/C][/ROW]
[ROW][C]Manifest Variables[/C][C]27[/C][/ROW]
[ROW][C]Scaled?[/C][C]TRUE[/C][/ROW]
[ROW][C]Weighting Scheme[/C][C]centroid[/C][/ROW]
[ROW][C]Bootstrapping?[/C][C]TRUE[/C][/ROW]
[ROW][C]Bootstrap samples[/C][C]100[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=1

As an alternative you can also use a QR Code:  

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

PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
MODEL SPECIFICATION
Number of Cases250
Latent Variables6
Manifest Variables27
Scaled?TRUE
Weighting Schemecentroid
Bootstrapping?TRUE
Bootstrap samples100







BLOCKS DEFINITION
BlockTypeNMVsMode
IMAGExogenous5Reflective
EXPEEndogenous5Reflective
QUALEndogenous5Reflective
VALEndogenous4Reflective
SATEndogenous4Reflective
LOYEndogenous4Reflective

\begin{tabular}{lllllllll}
\hline
BLOCKS DEFINITION \tabularnewline
Block & Type & NMVs & Mode \tabularnewline
IMAG & Exogenous & 5 & Reflective \tabularnewline
EXPE & Endogenous & 5 & Reflective \tabularnewline
QUAL & Endogenous & 5 & Reflective \tabularnewline
VAL & Endogenous & 4 & Reflective \tabularnewline
SAT & Endogenous & 4 & Reflective \tabularnewline
LOY & Endogenous & 4 & Reflective \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=2

[TABLE]
[ROW][C]BLOCKS DEFINITION[/C][/ROW]
[ROW][C]Block[/C][C]Type[/C][C]NMVs[/C][C]Mode[/C][/ROW]
[ROW][C]IMAG[/C][C]Exogenous[/C][C]5[/C][C]Reflective[/C][/ROW]
[ROW][C]EXPE[/C][C]Endogenous[/C][C]5[/C][C]Reflective[/C][/ROW]
[ROW][C]QUAL[/C][C]Endogenous[/C][C]5[/C][C]Reflective[/C][/ROW]
[ROW][C]VAL[/C][C]Endogenous[/C][C]4[/C][C]Reflective[/C][/ROW]
[ROW][C]SAT[/C][C]Endogenous[/C][C]4[/C][C]Reflective[/C][/ROW]
[ROW][C]LOY[/C][C]Endogenous[/C][C]4[/C][C]Reflective[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=2

As an alternative you can also use a QR Code:  

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

BLOCKS DEFINITION
BlockTypeNMVsMode
IMAGExogenous5Reflective
EXPEEndogenous5Reflective
QUALEndogenous5Reflective
VALEndogenous4Reflective
SATEndogenous4Reflective
LOYEndogenous4Reflective







BLOCKS UNIDIMENSIONALITY
BlockType.measureMVseig.1steig.2ndC.alphaDG.rho
IMAGReflective52.998748662766730.7900477849172230.8279167394263080.880649849276187
EXPEReflective53.102313747522910.610546737862660.846584078925260.890855432982731
QUALReflective53.305987356624970.5677772814503220.8713249547696430.906899737529554
VALReflective42.685833232370570.6058713785654320.8365170370928150.890918439371882
SATReflective43.039998787267970.4220000895661180.8940109126765590.926700636965068
LOYReflective42.604720758649650.5734548894363630.8194216410299610.88137109321981

\begin{tabular}{lllllllll}
\hline
BLOCKS UNIDIMENSIONALITY \tabularnewline
Block & Type.measure & MVs & eig.1st & eig.2nd & C.alpha & DG.rho \tabularnewline
IMAG & Reflective & 5 & 2.99874866276673 & 0.790047784917223 & 0.827916739426308 & 0.880649849276187 \tabularnewline
EXPE & Reflective & 5 & 3.10231374752291 & 0.61054673786266 & 0.84658407892526 & 0.890855432982731 \tabularnewline
QUAL & Reflective & 5 & 3.30598735662497 & 0.567777281450322 & 0.871324954769643 & 0.906899737529554 \tabularnewline
VAL & Reflective & 4 & 2.68583323237057 & 0.605871378565432 & 0.836517037092815 & 0.890918439371882 \tabularnewline
SAT & Reflective & 4 & 3.03999878726797 & 0.422000089566118 & 0.894010912676559 & 0.926700636965068 \tabularnewline
LOY & Reflective & 4 & 2.60472075864965 & 0.573454889436363 & 0.819421641029961 & 0.88137109321981 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=3

[TABLE]
[ROW][C]BLOCKS UNIDIMENSIONALITY[/C][/ROW]
[ROW][C]Block[/C][C]Type.measure[/C][C]MVs[/C][C]eig.1st[/C][C]eig.2nd[/C][C]C.alpha[/C][C]DG.rho[/C][/ROW]
[ROW][C]IMAG[/C][C]Reflective[/C][C]5[/C][C]2.99874866276673[/C][C]0.790047784917223[/C][C]0.827916739426308[/C][C]0.880649849276187[/C][/ROW]
[ROW][C]EXPE[/C][C]Reflective[/C][C]5[/C][C]3.10231374752291[/C][C]0.61054673786266[/C][C]0.84658407892526[/C][C]0.890855432982731[/C][/ROW]
[ROW][C]QUAL[/C][C]Reflective[/C][C]5[/C][C]3.30598735662497[/C][C]0.567777281450322[/C][C]0.871324954769643[/C][C]0.906899737529554[/C][/ROW]
[ROW][C]VAL[/C][C]Reflective[/C][C]4[/C][C]2.68583323237057[/C][C]0.605871378565432[/C][C]0.836517037092815[/C][C]0.890918439371882[/C][/ROW]
[ROW][C]SAT[/C][C]Reflective[/C][C]4[/C][C]3.03999878726797[/C][C]0.422000089566118[/C][C]0.894010912676559[/C][C]0.926700636965068[/C][/ROW]
[ROW][C]LOY[/C][C]Reflective[/C][C]4[/C][C]2.60472075864965[/C][C]0.573454889436363[/C][C]0.819421641029961[/C][C]0.88137109321981[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=3

As an alternative you can also use a QR Code:  

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

BLOCKS UNIDIMENSIONALITY
BlockType.measureMVseig.1steig.2ndC.alphaDG.rho
IMAGReflective52.998748662766730.7900477849172230.8279167394263080.880649849276187
EXPEReflective53.102313747522910.610546737862660.846584078925260.890855432982731
QUALReflective53.305987356624970.5677772814503220.8713249547696430.906899737529554
VALReflective42.685833232370570.6058713785654320.8365170370928150.890918439371882
SATReflective43.039998787267970.4220000895661180.8940109126765590.926700636965068
LOYReflective42.604720758649650.5734548894363630.8194216410299610.88137109321981







OUTER MODEL
Blockweightsstd.loadscommunalredundan
IMAG
imag10.20040.73920.54640
imag20.29970.89130.79450
imag30.30940.86260.7440
imag40.18290.63960.40910
imag50.28870.69610.48460
EXPE
expe10.23560.78760.62040.194
expe20.28160.82380.67860.2122
expe30.22370.7320.53580.1675
expe40.26110.77280.59730.1868
expe50.26530.81780.66880.2092
QUAL
qual10.24170.7930.62890.4496
qual20.26910.86980.75650.5409
qual30.22470.76380.58340.4171
qual40.24590.82280.67690.484
qual50.24650.81250.66010.472
VAL
val10.35460.85880.73750.4284
val20.28560.83220.69250.4023
val30.24950.75960.5770.3352
val40.32730.81970.6720.3903
SAT
sat10.31710.91460.83650.5883
sat20.31720.91450.83620.5881
sat30.24830.83430.69610.4895
sat40.26010.81830.66950.4708
LOY
loy10.37360.88780.78820.3864
loy20.25080.72410.52440.2571
loy30.37180.88070.77570.3803
loy40.22380.71150.50620.2482

\begin{tabular}{lllllllll}
\hline
OUTER MODEL \tabularnewline
Block & weights & std.loads & communal & redundan \tabularnewline
IMAG \tabularnewline
imag1 & 0.2004 & 0.7392 & 0.5464 & 0 \tabularnewline
imag2 & 0.2997 & 0.8913 & 0.7945 & 0 \tabularnewline
imag3 & 0.3094 & 0.8626 & 0.744 & 0 \tabularnewline
imag4 & 0.1829 & 0.6396 & 0.4091 & 0 \tabularnewline
imag5 & 0.2887 & 0.6961 & 0.4846 & 0 \tabularnewline
EXPE \tabularnewline
expe1 & 0.2356 & 0.7876 & 0.6204 & 0.194 \tabularnewline
expe2 & 0.2816 & 0.8238 & 0.6786 & 0.2122 \tabularnewline
expe3 & 0.2237 & 0.732 & 0.5358 & 0.1675 \tabularnewline
expe4 & 0.2611 & 0.7728 & 0.5973 & 0.1868 \tabularnewline
expe5 & 0.2653 & 0.8178 & 0.6688 & 0.2092 \tabularnewline
QUAL \tabularnewline
qual1 & 0.2417 & 0.793 & 0.6289 & 0.4496 \tabularnewline
qual2 & 0.2691 & 0.8698 & 0.7565 & 0.5409 \tabularnewline
qual3 & 0.2247 & 0.7638 & 0.5834 & 0.4171 \tabularnewline
qual4 & 0.2459 & 0.8228 & 0.6769 & 0.484 \tabularnewline
qual5 & 0.2465 & 0.8125 & 0.6601 & 0.472 \tabularnewline
VAL \tabularnewline
val1 & 0.3546 & 0.8588 & 0.7375 & 0.4284 \tabularnewline
val2 & 0.2856 & 0.8322 & 0.6925 & 0.4023 \tabularnewline
val3 & 0.2495 & 0.7596 & 0.577 & 0.3352 \tabularnewline
val4 & 0.3273 & 0.8197 & 0.672 & 0.3903 \tabularnewline
SAT \tabularnewline
sat1 & 0.3171 & 0.9146 & 0.8365 & 0.5883 \tabularnewline
sat2 & 0.3172 & 0.9145 & 0.8362 & 0.5881 \tabularnewline
sat3 & 0.2483 & 0.8343 & 0.6961 & 0.4895 \tabularnewline
sat4 & 0.2601 & 0.8183 & 0.6695 & 0.4708 \tabularnewline
LOY \tabularnewline
loy1 & 0.3736 & 0.8878 & 0.7882 & 0.3864 \tabularnewline
loy2 & 0.2508 & 0.7241 & 0.5244 & 0.2571 \tabularnewline
loy3 & 0.3718 & 0.8807 & 0.7757 & 0.3803 \tabularnewline
loy4 & 0.2238 & 0.7115 & 0.5062 & 0.2482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=4

[TABLE]
[ROW][C]OUTER MODEL[/C][/ROW]
[ROW][C]Block[/C][C]weights[/C][C]std.loads[/C][C]communal[/C][C]redundan[/C][/ROW]
[ROW][C]IMAG[/C][/ROW]
[ROW][C]imag1[/C][C]0.2004[/C][C]0.7392[/C][C]0.5464[/C][C]0[/C][/ROW]
[ROW][C]imag2[/C][C]0.2997[/C][C]0.8913[/C][C]0.7945[/C][C]0[/C][/ROW]
[ROW][C]imag3[/C][C]0.3094[/C][C]0.8626[/C][C]0.744[/C][C]0[/C][/ROW]
[ROW][C]imag4[/C][C]0.1829[/C][C]0.6396[/C][C]0.4091[/C][C]0[/C][/ROW]
[ROW][C]imag5[/C][C]0.2887[/C][C]0.6961[/C][C]0.4846[/C][C]0[/C][/ROW]
[ROW][C]EXPE[/C][/ROW]
[ROW][C]expe1[/C][C]0.2356[/C][C]0.7876[/C][C]0.6204[/C][C]0.194[/C][/ROW]
[ROW][C]expe2[/C][C]0.2816[/C][C]0.8238[/C][C]0.6786[/C][C]0.2122[/C][/ROW]
[ROW][C]expe3[/C][C]0.2237[/C][C]0.732[/C][C]0.5358[/C][C]0.1675[/C][/ROW]
[ROW][C]expe4[/C][C]0.2611[/C][C]0.7728[/C][C]0.5973[/C][C]0.1868[/C][/ROW]
[ROW][C]expe5[/C][C]0.2653[/C][C]0.8178[/C][C]0.6688[/C][C]0.2092[/C][/ROW]
[ROW][C]QUAL[/C][/ROW]
[ROW][C]qual1[/C][C]0.2417[/C][C]0.793[/C][C]0.6289[/C][C]0.4496[/C][/ROW]
[ROW][C]qual2[/C][C]0.2691[/C][C]0.8698[/C][C]0.7565[/C][C]0.5409[/C][/ROW]
[ROW][C]qual3[/C][C]0.2247[/C][C]0.7638[/C][C]0.5834[/C][C]0.4171[/C][/ROW]
[ROW][C]qual4[/C][C]0.2459[/C][C]0.8228[/C][C]0.6769[/C][C]0.484[/C][/ROW]
[ROW][C]qual5[/C][C]0.2465[/C][C]0.8125[/C][C]0.6601[/C][C]0.472[/C][/ROW]
[ROW][C]VAL[/C][/ROW]
[ROW][C]val1[/C][C]0.3546[/C][C]0.8588[/C][C]0.7375[/C][C]0.4284[/C][/ROW]
[ROW][C]val2[/C][C]0.2856[/C][C]0.8322[/C][C]0.6925[/C][C]0.4023[/C][/ROW]
[ROW][C]val3[/C][C]0.2495[/C][C]0.7596[/C][C]0.577[/C][C]0.3352[/C][/ROW]
[ROW][C]val4[/C][C]0.3273[/C][C]0.8197[/C][C]0.672[/C][C]0.3903[/C][/ROW]
[ROW][C]SAT[/C][/ROW]
[ROW][C]sat1[/C][C]0.3171[/C][C]0.9146[/C][C]0.8365[/C][C]0.5883[/C][/ROW]
[ROW][C]sat2[/C][C]0.3172[/C][C]0.9145[/C][C]0.8362[/C][C]0.5881[/C][/ROW]
[ROW][C]sat3[/C][C]0.2483[/C][C]0.8343[/C][C]0.6961[/C][C]0.4895[/C][/ROW]
[ROW][C]sat4[/C][C]0.2601[/C][C]0.8183[/C][C]0.6695[/C][C]0.4708[/C][/ROW]
[ROW][C]LOY[/C][/ROW]
[ROW][C]loy1[/C][C]0.3736[/C][C]0.8878[/C][C]0.7882[/C][C]0.3864[/C][/ROW]
[ROW][C]loy2[/C][C]0.2508[/C][C]0.7241[/C][C]0.5244[/C][C]0.2571[/C][/ROW]
[ROW][C]loy3[/C][C]0.3718[/C][C]0.8807[/C][C]0.7757[/C][C]0.3803[/C][/ROW]
[ROW][C]loy4[/C][C]0.2238[/C][C]0.7115[/C][C]0.5062[/C][C]0.2482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=4

As an alternative you can also use a QR Code:  

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

OUTER MODEL
Blockweightsstd.loadscommunalredundan
IMAG
imag10.20040.73920.54640
imag20.29970.89130.79450
imag30.30940.86260.7440
imag40.18290.63960.40910
imag50.28870.69610.48460
EXPE
expe10.23560.78760.62040.194
expe20.28160.82380.67860.2122
expe30.22370.7320.53580.1675
expe40.26110.77280.59730.1868
expe50.26530.81780.66880.2092
QUAL
qual10.24170.7930.62890.4496
qual20.26910.86980.75650.5409
qual30.22470.76380.58340.4171
qual40.24590.82280.67690.484
qual50.24650.81250.66010.472
VAL
val10.35460.85880.73750.4284
val20.28560.83220.69250.4023
val30.24950.75960.5770.3352
val40.32730.81970.6720.3903
SAT
sat10.31710.91460.83650.5883
sat20.31720.91450.83620.5881
sat30.24830.83430.69610.4895
sat40.26010.81830.66950.4708
LOY
loy10.37360.88780.78820.3864
loy20.25080.72410.52440.2571
loy30.37180.88070.77570.3803
loy40.22380.71150.50620.2482







CORRELATIONS BETWEEN MVs AND LVs
BlockIMAGEXPEQUALVALSATLOY
IMAG
imag10.73920.30690.32250.40310.38430.389
imag20.89130.48040.54190.6020.59690.5381
imag30.86260.47620.52340.65830.64390.5478
imag40.63960.27660.32170.40750.34380.3654
imag50.69610.53990.59620.52810.5550.4613
EXPE
expe10.37390.78760.63410.50030.4610.3504
expe20.4880.82380.73750.58420.5450.4134
expe30.38760.7320.60690.46390.41210.3113
expe40.47520.77280.64580.55320.50840.4343
expe50.46340.81780.69410.55350.50660.3822
QUAL
qual10.44190.69750.7930.59110.55310.4988
qual20.53410.73860.86980.68420.62810.522
qual30.42340.64660.76380.56640.49910.366
qual40.61480.65390.82280.62890.59120.5972
qual50.4990.69810.81250.60990.57070.4972
VAL
val10.60780.66290.71240.85880.74840.5752
val20.51530.47820.55720.83220.67480.5701
val30.49590.44510.51240.75960.53650.4843
val40.62760.59480.67120.81970.69390.5954
SAT
sat10.64110.59010.65540.80160.91460.6578
sat20.62730.62990.71270.79480.91450.5823
sat30.50930.45980.54820.62210.83430.4804
sat40.57470.46280.50350.6090.81830.5945
LOY
loy10.55820.46490.56720.65290.66150.8878
loy20.42120.30490.38740.4050.39730.7241
loy30.55880.48640.60310.6420.65490.8807
loy40.38740.2340.3550.43910.34310.7115

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN MVs AND LVs \tabularnewline
Block & IMAG & EXPE & QUAL & VAL & SAT & LOY \tabularnewline
IMAG \tabularnewline
imag1 & 0.7392 & 0.3069 & 0.3225 & 0.4031 & 0.3843 & 0.389 \tabularnewline
imag2 & 0.8913 & 0.4804 & 0.5419 & 0.602 & 0.5969 & 0.5381 \tabularnewline
imag3 & 0.8626 & 0.4762 & 0.5234 & 0.6583 & 0.6439 & 0.5478 \tabularnewline
imag4 & 0.6396 & 0.2766 & 0.3217 & 0.4075 & 0.3438 & 0.3654 \tabularnewline
imag5 & 0.6961 & 0.5399 & 0.5962 & 0.5281 & 0.555 & 0.4613 \tabularnewline
EXPE \tabularnewline
expe1 & 0.3739 & 0.7876 & 0.6341 & 0.5003 & 0.461 & 0.3504 \tabularnewline
expe2 & 0.488 & 0.8238 & 0.7375 & 0.5842 & 0.545 & 0.4134 \tabularnewline
expe3 & 0.3876 & 0.732 & 0.6069 & 0.4639 & 0.4121 & 0.3113 \tabularnewline
expe4 & 0.4752 & 0.7728 & 0.6458 & 0.5532 & 0.5084 & 0.4343 \tabularnewline
expe5 & 0.4634 & 0.8178 & 0.6941 & 0.5535 & 0.5066 & 0.3822 \tabularnewline
QUAL \tabularnewline
qual1 & 0.4419 & 0.6975 & 0.793 & 0.5911 & 0.5531 & 0.4988 \tabularnewline
qual2 & 0.5341 & 0.7386 & 0.8698 & 0.6842 & 0.6281 & 0.522 \tabularnewline
qual3 & 0.4234 & 0.6466 & 0.7638 & 0.5664 & 0.4991 & 0.366 \tabularnewline
qual4 & 0.6148 & 0.6539 & 0.8228 & 0.6289 & 0.5912 & 0.5972 \tabularnewline
qual5 & 0.499 & 0.6981 & 0.8125 & 0.6099 & 0.5707 & 0.4972 \tabularnewline
VAL \tabularnewline
val1 & 0.6078 & 0.6629 & 0.7124 & 0.8588 & 0.7484 & 0.5752 \tabularnewline
val2 & 0.5153 & 0.4782 & 0.5572 & 0.8322 & 0.6748 & 0.5701 \tabularnewline
val3 & 0.4959 & 0.4451 & 0.5124 & 0.7596 & 0.5365 & 0.4843 \tabularnewline
val4 & 0.6276 & 0.5948 & 0.6712 & 0.8197 & 0.6939 & 0.5954 \tabularnewline
SAT \tabularnewline
sat1 & 0.6411 & 0.5901 & 0.6554 & 0.8016 & 0.9146 & 0.6578 \tabularnewline
sat2 & 0.6273 & 0.6299 & 0.7127 & 0.7948 & 0.9145 & 0.5823 \tabularnewline
sat3 & 0.5093 & 0.4598 & 0.5482 & 0.6221 & 0.8343 & 0.4804 \tabularnewline
sat4 & 0.5747 & 0.4628 & 0.5035 & 0.609 & 0.8183 & 0.5945 \tabularnewline
LOY \tabularnewline
loy1 & 0.5582 & 0.4649 & 0.5672 & 0.6529 & 0.6615 & 0.8878 \tabularnewline
loy2 & 0.4212 & 0.3049 & 0.3874 & 0.405 & 0.3973 & 0.7241 \tabularnewline
loy3 & 0.5588 & 0.4864 & 0.6031 & 0.642 & 0.6549 & 0.8807 \tabularnewline
loy4 & 0.3874 & 0.234 & 0.355 & 0.4391 & 0.3431 & 0.7115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=5

[TABLE]
[ROW][C]CORRELATIONS BETWEEN MVs AND LVs[/C][/ROW]
[ROW][C]Block[/C][C]IMAG[/C][C]EXPE[/C][C]QUAL[/C][C]VAL[/C][C]SAT[/C][C]LOY[/C][/ROW]
[ROW][C]IMAG[/C][/ROW]
[ROW][C]imag1[/C][C]0.7392[/C][C]0.3069[/C][C]0.3225[/C][C]0.4031[/C][C]0.3843[/C][C]0.389[/C][/ROW]
[ROW][C]imag2[/C][C]0.8913[/C][C]0.4804[/C][C]0.5419[/C][C]0.602[/C][C]0.5969[/C][C]0.5381[/C][/ROW]
[ROW][C]imag3[/C][C]0.8626[/C][C]0.4762[/C][C]0.5234[/C][C]0.6583[/C][C]0.6439[/C][C]0.5478[/C][/ROW]
[ROW][C]imag4[/C][C]0.6396[/C][C]0.2766[/C][C]0.3217[/C][C]0.4075[/C][C]0.3438[/C][C]0.3654[/C][/ROW]
[ROW][C]imag5[/C][C]0.6961[/C][C]0.5399[/C][C]0.5962[/C][C]0.5281[/C][C]0.555[/C][C]0.4613[/C][/ROW]
[ROW][C]EXPE[/C][/ROW]
[ROW][C]expe1[/C][C]0.3739[/C][C]0.7876[/C][C]0.6341[/C][C]0.5003[/C][C]0.461[/C][C]0.3504[/C][/ROW]
[ROW][C]expe2[/C][C]0.488[/C][C]0.8238[/C][C]0.7375[/C][C]0.5842[/C][C]0.545[/C][C]0.4134[/C][/ROW]
[ROW][C]expe3[/C][C]0.3876[/C][C]0.732[/C][C]0.6069[/C][C]0.4639[/C][C]0.4121[/C][C]0.3113[/C][/ROW]
[ROW][C]expe4[/C][C]0.4752[/C][C]0.7728[/C][C]0.6458[/C][C]0.5532[/C][C]0.5084[/C][C]0.4343[/C][/ROW]
[ROW][C]expe5[/C][C]0.4634[/C][C]0.8178[/C][C]0.6941[/C][C]0.5535[/C][C]0.5066[/C][C]0.3822[/C][/ROW]
[ROW][C]QUAL[/C][/ROW]
[ROW][C]qual1[/C][C]0.4419[/C][C]0.6975[/C][C]0.793[/C][C]0.5911[/C][C]0.5531[/C][C]0.4988[/C][/ROW]
[ROW][C]qual2[/C][C]0.5341[/C][C]0.7386[/C][C]0.8698[/C][C]0.6842[/C][C]0.6281[/C][C]0.522[/C][/ROW]
[ROW][C]qual3[/C][C]0.4234[/C][C]0.6466[/C][C]0.7638[/C][C]0.5664[/C][C]0.4991[/C][C]0.366[/C][/ROW]
[ROW][C]qual4[/C][C]0.6148[/C][C]0.6539[/C][C]0.8228[/C][C]0.6289[/C][C]0.5912[/C][C]0.5972[/C][/ROW]
[ROW][C]qual5[/C][C]0.499[/C][C]0.6981[/C][C]0.8125[/C][C]0.6099[/C][C]0.5707[/C][C]0.4972[/C][/ROW]
[ROW][C]VAL[/C][/ROW]
[ROW][C]val1[/C][C]0.6078[/C][C]0.6629[/C][C]0.7124[/C][C]0.8588[/C][C]0.7484[/C][C]0.5752[/C][/ROW]
[ROW][C]val2[/C][C]0.5153[/C][C]0.4782[/C][C]0.5572[/C][C]0.8322[/C][C]0.6748[/C][C]0.5701[/C][/ROW]
[ROW][C]val3[/C][C]0.4959[/C][C]0.4451[/C][C]0.5124[/C][C]0.7596[/C][C]0.5365[/C][C]0.4843[/C][/ROW]
[ROW][C]val4[/C][C]0.6276[/C][C]0.5948[/C][C]0.6712[/C][C]0.8197[/C][C]0.6939[/C][C]0.5954[/C][/ROW]
[ROW][C]SAT[/C][/ROW]
[ROW][C]sat1[/C][C]0.6411[/C][C]0.5901[/C][C]0.6554[/C][C]0.8016[/C][C]0.9146[/C][C]0.6578[/C][/ROW]
[ROW][C]sat2[/C][C]0.6273[/C][C]0.6299[/C][C]0.7127[/C][C]0.7948[/C][C]0.9145[/C][C]0.5823[/C][/ROW]
[ROW][C]sat3[/C][C]0.5093[/C][C]0.4598[/C][C]0.5482[/C][C]0.6221[/C][C]0.8343[/C][C]0.4804[/C][/ROW]
[ROW][C]sat4[/C][C]0.5747[/C][C]0.4628[/C][C]0.5035[/C][C]0.609[/C][C]0.8183[/C][C]0.5945[/C][/ROW]
[ROW][C]LOY[/C][/ROW]
[ROW][C]loy1[/C][C]0.5582[/C][C]0.4649[/C][C]0.5672[/C][C]0.6529[/C][C]0.6615[/C][C]0.8878[/C][/ROW]
[ROW][C]loy2[/C][C]0.4212[/C][C]0.3049[/C][C]0.3874[/C][C]0.405[/C][C]0.3973[/C][C]0.7241[/C][/ROW]
[ROW][C]loy3[/C][C]0.5588[/C][C]0.4864[/C][C]0.6031[/C][C]0.642[/C][C]0.6549[/C][C]0.8807[/C][/ROW]
[ROW][C]loy4[/C][C]0.3874[/C][C]0.234[/C][C]0.355[/C][C]0.4391[/C][C]0.3431[/C][C]0.7115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=5

As an alternative you can also use a QR Code:  

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

CORRELATIONS BETWEEN MVs AND LVs
BlockIMAGEXPEQUALVALSATLOY
IMAG
imag10.73920.30690.32250.40310.38430.389
imag20.89130.48040.54190.6020.59690.5381
imag30.86260.47620.52340.65830.64390.5478
imag40.63960.27660.32170.40750.34380.3654
imag50.69610.53990.59620.52810.5550.4613
EXPE
expe10.37390.78760.63410.50030.4610.3504
expe20.4880.82380.73750.58420.5450.4134
expe30.38760.7320.60690.46390.41210.3113
expe40.47520.77280.64580.55320.50840.4343
expe50.46340.81780.69410.55350.50660.3822
QUAL
qual10.44190.69750.7930.59110.55310.4988
qual20.53410.73860.86980.68420.62810.522
qual30.42340.64660.76380.56640.49910.366
qual40.61480.65390.82280.62890.59120.5972
qual50.4990.69810.81250.60990.57070.4972
VAL
val10.60780.66290.71240.85880.74840.5752
val20.51530.47820.55720.83220.67480.5701
val30.49590.44510.51240.75960.53650.4843
val40.62760.59480.67120.81970.69390.5954
SAT
sat10.64110.59010.65540.80160.91460.6578
sat20.62730.62990.71270.79480.91450.5823
sat30.50930.45980.54820.62210.83430.4804
sat40.57470.46280.50350.6090.81830.5945
LOY
loy10.55820.46490.56720.65290.66150.8878
loy20.42120.30490.38740.4050.39730.7241
loy30.55880.48640.60310.6420.65490.8807
loy40.38740.2340.3550.43910.34310.7115







INNER MODEL
BlockConceptValue
S2
1R20.3127
2Intercept0
3path_S10.5592
S3
1R20.715
2Intercept0
3path_S20.8456
S4
1R20.5809
2Intercept0
3path_S20.124
4path_S30.6544
S5
1R20.7032
2Intercept0
3path_S10.1874
4path_S20.0059
5path_S30.1401
6path_S40.579
S6
1R20.4903
2Intercept0
3path_S10.2893
4path_S50.4709

\begin{tabular}{lllllllll}
\hline
INNER MODEL \tabularnewline
Block & Concept & Value \tabularnewline
S2 \tabularnewline
1 & R2 & 0.3127 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S1 & 0.5592 \tabularnewline
S3 \tabularnewline
1 & R2 & 0.715 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.8456 \tabularnewline
S4 \tabularnewline
1 & R2 & 0.5809 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.124 \tabularnewline
4 & path_S3 & 0.6544 \tabularnewline
S5 \tabularnewline
1 & R2 & 0.7032 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S1 & 0.1874 \tabularnewline
4 & path_S2 & 0.0059 \tabularnewline
5 & path_S3 & 0.1401 \tabularnewline
6 & path_S4 & 0.579 \tabularnewline
S6 \tabularnewline
1 & R2 & 0.4903 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S1 & 0.2893 \tabularnewline
4 & path_S5 & 0.4709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=6

[TABLE]
[ROW][C]INNER MODEL[/C][/ROW]
[ROW][C]Block[/C][C]Concept[/C][C]Value[/C][/ROW]
[ROW][C]S2[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.3127[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S1[/C][C]0.5592[/C][/ROW]
[ROW][C]S3[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.715[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.8456[/C][/ROW]
[ROW][C]S4[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.5809[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.124[/C][/ROW]
[ROW][C]4[/C][C]path_S3[/C][C]0.6544[/C][/ROW]
[ROW][C]S5[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.7032[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S1[/C][C]0.1874[/C][/ROW]
[ROW][C]4[/C][C]path_S2[/C][C]0.0059[/C][/ROW]
[ROW][C]5[/C][C]path_S3[/C][C]0.1401[/C][/ROW]
[ROW][C]6[/C][C]path_S4[/C][C]0.579[/C][/ROW]
[ROW][C]S6[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.4903[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S1[/C][C]0.2893[/C][/ROW]
[ROW][C]4[/C][C]path_S5[/C][C]0.4709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=6

As an alternative you can also use a QR Code:  

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

INNER MODEL
BlockConceptValue
S2
1R20.3127
2Intercept0
3path_S10.5592
S3
1R20.715
2Intercept0
3path_S20.8456
S4
1R20.5809
2Intercept0
3path_S20.124
4path_S30.6544
S5
1R20.7032
2Intercept0
3path_S10.1874
4path_S20.0059
5path_S30.1401
6path_S40.579
S6
1R20.4903
2Intercept0
3path_S10.2893
4path_S50.4709







CORRELATIONS BETWEEN LVs
IMAGEXPEQUALVALSATLOY
IMAG10.55920.61990.69180.67820.6087
EXPE0.559210.84560.67740.62140.4834
QUAL0.61990.845610.75930.70090.6127
VAL0.69180.67740.759310.81910.6825
SAT0.67820.62140.70090.819110.6671
LOY0.60870.48340.61270.68250.66711

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN LVs \tabularnewline
 & IMAG & EXPE & QUAL & VAL & SAT & LOY \tabularnewline
IMAG & 1 & 0.5592 & 0.6199 & 0.6918 & 0.6782 & 0.6087 \tabularnewline
EXPE & 0.5592 & 1 & 0.8456 & 0.6774 & 0.6214 & 0.4834 \tabularnewline
QUAL & 0.6199 & 0.8456 & 1 & 0.7593 & 0.7009 & 0.6127 \tabularnewline
VAL & 0.6918 & 0.6774 & 0.7593 & 1 & 0.8191 & 0.6825 \tabularnewline
SAT & 0.6782 & 0.6214 & 0.7009 & 0.8191 & 1 & 0.6671 \tabularnewline
LOY & 0.6087 & 0.4834 & 0.6127 & 0.6825 & 0.6671 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=7

[TABLE]
[ROW][C]CORRELATIONS BETWEEN LVs[/C][/ROW]
[ROW][C][/C][C]IMAG[/C][C]EXPE[/C][C]QUAL[/C][C]VAL[/C][C]SAT[/C][C]LOY[/C][/ROW]
[ROW][C]IMAG[/C][C]1[/C][C]0.5592[/C][C]0.6199[/C][C]0.6918[/C][C]0.6782[/C][C]0.6087[/C][/ROW]
[ROW][C]EXPE[/C][C]0.5592[/C][C]1[/C][C]0.8456[/C][C]0.6774[/C][C]0.6214[/C][C]0.4834[/C][/ROW]
[ROW][C]QUAL[/C][C]0.6199[/C][C]0.8456[/C][C]1[/C][C]0.7593[/C][C]0.7009[/C][C]0.6127[/C][/ROW]
[ROW][C]VAL[/C][C]0.6918[/C][C]0.6774[/C][C]0.7593[/C][C]1[/C][C]0.8191[/C][C]0.6825[/C][/ROW]
[ROW][C]SAT[/C][C]0.6782[/C][C]0.6214[/C][C]0.7009[/C][C]0.8191[/C][C]1[/C][C]0.6671[/C][/ROW]
[ROW][C]LOY[/C][C]0.6087[/C][C]0.4834[/C][C]0.6127[/C][C]0.6825[/C][C]0.6671[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=7

As an alternative you can also use a QR Code:  

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

CORRELATIONS BETWEEN LVs
IMAGEXPEQUALVALSATLOY
IMAG10.55920.61990.69180.67820.6087
EXPE0.559210.84560.67740.62140.4834
QUAL0.61990.845610.75930.70090.6127
VAL0.69180.67740.759310.81910.6825
SAT0.67820.62140.70090.819110.6671
LOY0.60870.48340.61270.68250.66711







SUMMARY INNER MODEL
LV.TypeMeasureMVsR.squareAv.CommuAv.RedunAVE
IMAGExogenRflct500.595700.596
EXPEEndogenRflct50.31270.62020.19390.62
QUALEndogenRflct50.7150.66120.47270.661
VALEndogenRflct40.58090.66970.3890.67
SATEndogenRflct40.70320.75960.53420.76
LOYEndogenRflct40.49030.64860.3180.649

\begin{tabular}{lllllllll}
\hline
SUMMARY INNER MODEL \tabularnewline
 & LV.Type & Measure & MVs & R.square & Av.Commu & Av.Redun & AVE \tabularnewline
IMAG & Exogen & Rflct & 5 & 0 & 0.5957 & 0 & 0.596 \tabularnewline
EXPE & Endogen & Rflct & 5 & 0.3127 & 0.6202 & 0.1939 & 0.62 \tabularnewline
QUAL & Endogen & Rflct & 5 & 0.715 & 0.6612 & 0.4727 & 0.661 \tabularnewline
VAL & Endogen & Rflct & 4 & 0.5809 & 0.6697 & 0.389 & 0.67 \tabularnewline
SAT & Endogen & Rflct & 4 & 0.7032 & 0.7596 & 0.5342 & 0.76 \tabularnewline
LOY & Endogen & Rflct & 4 & 0.4903 & 0.6486 & 0.318 & 0.649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=8

[TABLE]
[ROW][C]SUMMARY INNER MODEL[/C][/ROW]
[ROW][C][/C][C]LV.Type[/C][C]Measure[/C][C]MVs[/C][C]R.square[/C][C]Av.Commu[/C][C]Av.Redun[/C][C]AVE[/C][/ROW]
[ROW][C]IMAG[/C][C]Exogen[/C][C]Rflct[/C][C]5[/C][C]0[/C][C]0.5957[/C][C]0[/C][C]0.596[/C][/ROW]
[ROW][C]EXPE[/C][C]Endogen[/C][C]Rflct[/C][C]5[/C][C]0.3127[/C][C]0.6202[/C][C]0.1939[/C][C]0.62[/C][/ROW]
[ROW][C]QUAL[/C][C]Endogen[/C][C]Rflct[/C][C]5[/C][C]0.715[/C][C]0.6612[/C][C]0.4727[/C][C]0.661[/C][/ROW]
[ROW][C]VAL[/C][C]Endogen[/C][C]Rflct[/C][C]4[/C][C]0.5809[/C][C]0.6697[/C][C]0.389[/C][C]0.67[/C][/ROW]
[ROW][C]SAT[/C][C]Endogen[/C][C]Rflct[/C][C]4[/C][C]0.7032[/C][C]0.7596[/C][C]0.5342[/C][C]0.76[/C][/ROW]
[ROW][C]LOY[/C][C]Endogen[/C][C]Rflct[/C][C]4[/C][C]0.4903[/C][C]0.6486[/C][C]0.318[/C][C]0.649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=8

As an alternative you can also use a QR Code:  

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

SUMMARY INNER MODEL
LV.TypeMeasureMVsR.squareAv.CommuAv.RedunAVE
IMAGExogenRflct500.595700.596
EXPEEndogenRflct50.31270.62020.19390.62
QUALEndogenRflct50.7150.66120.47270.661
VALEndogenRflct40.58090.66970.3890.67
SATEndogenRflct40.70320.75960.53420.76
LOYEndogenRflct40.49030.64860.3180.649







GOODNESS-OF-FIT
GoFValue
Absolute0.60607085098299
Relative0.951528216653255
Outer.mod0.99880851972517
Inner.mod0.952663296179207

\begin{tabular}{lllllllll}
\hline
GOODNESS-OF-FIT \tabularnewline
GoF & Value \tabularnewline
Absolute & 0.60607085098299 \tabularnewline
Relative & 0.951528216653255 \tabularnewline
Outer.mod & 0.99880851972517 \tabularnewline
Inner.mod & 0.952663296179207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=9

[TABLE]
[ROW][C]GOODNESS-OF-FIT[/C][/ROW]
[ROW][C]GoF[/C][C]Value[/C][/ROW]
[ROW][C]Absolute[/C][C]0.60607085098299[/C][/ROW]
[ROW][C]Relative[/C][C]0.951528216653255[/C][/ROW]
[ROW][C]Outer.mod[/C][C]0.99880851972517[/C][/ROW]
[ROW][C]Inner.mod[/C][C]0.952663296179207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=9

As an alternative you can also use a QR Code:  

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

GOODNESS-OF-FIT
GoFValue
Absolute0.60607085098299
Relative0.951528216653255
Outer.mod0.99880851972517
Inner.mod0.952663296179207







TOTAL EFFECTS
relationshipsdir.effectind.effecttot.effect
S1->S20.55920860225877400.559208602258774
S1->S300.4728432143477330.472843214347733
S1->S400.3787916081737920.378791608173792
S1->S50.1873663171745290.2889095968867950.476275914061324
S1->S60.2893150689729770.2242867372792640.513601806252242
S2->S30.84555783376330300.845557833763303
S2->S40.1240422591040860.5533285943551840.67737085345927
S2->S50.005929005117052520.5107111104316380.516640115548691
S2->S600.2432949524486730.243294952448673
S3->S40.65439473476639600.654394734766396
S3->S50.1401265052438480.3789219128102240.519048418054072
S3->S600.2444290646205590.244429064620559
S4->S50.57904181173690300.579041811736903
S4->S600.2726810129769070.272681012976907
S5->S60.47091765646243800.470917656462438

\begin{tabular}{lllllllll}
\hline
TOTAL EFFECTS \tabularnewline
relationships & dir.effect & ind.effect & tot.effect \tabularnewline
S1->S2 & 0.559208602258774 & 0 & 0.559208602258774 \tabularnewline
S1->S3 & 0 & 0.472843214347733 & 0.472843214347733 \tabularnewline
S1->S4 & 0 & 0.378791608173792 & 0.378791608173792 \tabularnewline
S1->S5 & 0.187366317174529 & 0.288909596886795 & 0.476275914061324 \tabularnewline
S1->S6 & 0.289315068972977 & 0.224286737279264 & 0.513601806252242 \tabularnewline
S2->S3 & 0.845557833763303 & 0 & 0.845557833763303 \tabularnewline
S2->S4 & 0.124042259104086 & 0.553328594355184 & 0.67737085345927 \tabularnewline
S2->S5 & 0.00592900511705252 & 0.510711110431638 & 0.516640115548691 \tabularnewline
S2->S6 & 0 & 0.243294952448673 & 0.243294952448673 \tabularnewline
S3->S4 & 0.654394734766396 & 0 & 0.654394734766396 \tabularnewline
S3->S5 & 0.140126505243848 & 0.378921912810224 & 0.519048418054072 \tabularnewline
S3->S6 & 0 & 0.244429064620559 & 0.244429064620559 \tabularnewline
S4->S5 & 0.579041811736903 & 0 & 0.579041811736903 \tabularnewline
S4->S6 & 0 & 0.272681012976907 & 0.272681012976907 \tabularnewline
S5->S6 & 0.470917656462438 & 0 & 0.470917656462438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=10

[TABLE]
[ROW][C]TOTAL EFFECTS[/C][/ROW]
[ROW][C]relationships[/C][C]dir.effect[/C][C]ind.effect[/C][C]tot.effect[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.559208602258774[/C][C]0[/C][C]0.559208602258774[/C][/ROW]
[ROW][C]S1->S3[/C][C]0[/C][C]0.472843214347733[/C][C]0.472843214347733[/C][/ROW]
[ROW][C]S1->S4[/C][C]0[/C][C]0.378791608173792[/C][C]0.378791608173792[/C][/ROW]
[ROW][C]S1->S5[/C][C]0.187366317174529[/C][C]0.288909596886795[/C][C]0.476275914061324[/C][/ROW]
[ROW][C]S1->S6[/C][C]0.289315068972977[/C][C]0.224286737279264[/C][C]0.513601806252242[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.845557833763303[/C][C]0[/C][C]0.845557833763303[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.124042259104086[/C][C]0.553328594355184[/C][C]0.67737085345927[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.00592900511705252[/C][C]0.510711110431638[/C][C]0.516640115548691[/C][/ROW]
[ROW][C]S2->S6[/C][C]0[/C][C]0.243294952448673[/C][C]0.243294952448673[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.654394734766396[/C][C]0[/C][C]0.654394734766396[/C][/ROW]
[ROW][C]S3->S5[/C][C]0.140126505243848[/C][C]0.378921912810224[/C][C]0.519048418054072[/C][/ROW]
[ROW][C]S3->S6[/C][C]0[/C][C]0.244429064620559[/C][C]0.244429064620559[/C][/ROW]
[ROW][C]S4->S5[/C][C]0.579041811736903[/C][C]0[/C][C]0.579041811736903[/C][/ROW]
[ROW][C]S4->S6[/C][C]0[/C][C]0.272681012976907[/C][C]0.272681012976907[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.470917656462438[/C][C]0[/C][C]0.470917656462438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=10

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=10

As an alternative you can also use a QR Code:  

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

TOTAL EFFECTS
relationshipsdir.effectind.effecttot.effect
S1->S20.55920860225877400.559208602258774
S1->S300.4728432143477330.472843214347733
S1->S400.3787916081737920.378791608173792
S1->S50.1873663171745290.2889095968867950.476275914061324
S1->S60.2893150689729770.2242867372792640.513601806252242
S2->S30.84555783376330300.845557833763303
S2->S40.1240422591040860.5533285943551840.67737085345927
S2->S50.005929005117052520.5107111104316380.516640115548691
S2->S600.2432949524486730.243294952448673
S3->S40.65439473476639600.654394734766396
S3->S50.1401265052438480.3789219128102240.519048418054072
S3->S600.2444290646205590.244429064620559
S4->S50.57904181173690300.579041811736903
S4->S600.2726810129769070.272681012976907
S5->S60.47091765646243800.470917656462438







BOOTSTRAP VALIDATION - WEIGHTS
OriginalMean.BootStd.Errorperc.05perc.95
imag10.2003718087804350.2010253776022740.02426888552578340.1611607600410980.234465042899789
imag20.299668890562040.2992904078112190.01830320592378040.2736721377667970.332171108065418
imag30.3093877235871260.3077065026552150.02018328706710950.2733438598410090.339206527919999
imag40.1828696602355610.1875848965291980.0272250319854650.1436080353953720.229338187890433
imag50.2886787377931950.2831091683638860.0232949932581260.2498266671359740.318597392420184
expe10.235551657157690.2346696408189420.01982497579076170.2069778393855990.262908955616103
expe20.2816431584851620.2807158158829740.0152763349169690.2592257514334740.310250933530914
expe30.223726676806350.2232588430914410.01770634797169980.1932041452561440.249366555084841
expe40.2610635383972910.2624578177466050.01844656202195330.2384866370721230.298467027836289
expe50.2652562858606330.2678038645784680.0166762674164930.2438631616907820.294851828966131
qual10.2416878164207670.242803239640.01920209156783280.2143852367886190.270925907847301
qual20.2691250660831340.2690730371499660.01165267296773120.2519025064259880.293643704355846
qual30.2246813621595990.2237163954931770.01575155321325240.1997110759786560.24967430153878
qual40.245917530810370.2480832779607570.01144540078222460.2321123102786970.265774783167419
qual50.2465417431561340.2460747291773420.01239141636552270.2300883174558810.268537485746637
val10.3546418454187340.3514551709900190.02150478979422090.3171114291728260.387293029885891
val20.2855781737629840.2863667452502930.01841890146046910.2556626213339970.317327510195694
val30.2494883696640680.2511833028526250.01940769145660270.2143658610634940.279111495554649
val40.3272878598595570.3255388469865690.02016008188207530.296071490501480.359403989006462
sat10.3170896381019140.3194439412974140.01649837867508590.2983893262175840.349405827131457
sat20.317179042775730.3188664006046210.0158337161488130.2960009577077780.343004710037015
sat30.248263731714610.2462506641789510.01078291518326330.2261563576370050.261975505896215
sat40.2600778302031410.2632090477500350.01454974106834560.2417671915291950.290477243706804
loy10.373635483535550.3689365093883890.02318468512014320.3306217269026190.405790428948839
loy20.250768989248710.2494970705908320.02518260811105940.2063757453412710.288446486211379
loy30.3718062366710190.3673234221623180.02755922265645960.3294262039882940.419410314848178
loy40.2238075786491250.2283712416019120.0370776110345720.1727840977102130.287039176214107

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - WEIGHTS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
imag1 & 0.200371808780435 & 0.201025377602274 & 0.0242688855257834 & 0.161160760041098 & 0.234465042899789 \tabularnewline
imag2 & 0.29966889056204 & 0.299290407811219 & 0.0183032059237804 & 0.273672137766797 & 0.332171108065418 \tabularnewline
imag3 & 0.309387723587126 & 0.307706502655215 & 0.0201832870671095 & 0.273343859841009 & 0.339206527919999 \tabularnewline
imag4 & 0.182869660235561 & 0.187584896529198 & 0.027225031985465 & 0.143608035395372 & 0.229338187890433 \tabularnewline
imag5 & 0.288678737793195 & 0.283109168363886 & 0.023294993258126 & 0.249826667135974 & 0.318597392420184 \tabularnewline
expe1 & 0.23555165715769 & 0.234669640818942 & 0.0198249757907617 & 0.206977839385599 & 0.262908955616103 \tabularnewline
expe2 & 0.281643158485162 & 0.280715815882974 & 0.015276334916969 & 0.259225751433474 & 0.310250933530914 \tabularnewline
expe3 & 0.22372667680635 & 0.223258843091441 & 0.0177063479716998 & 0.193204145256144 & 0.249366555084841 \tabularnewline
expe4 & 0.261063538397291 & 0.262457817746605 & 0.0184465620219533 & 0.238486637072123 & 0.298467027836289 \tabularnewline
expe5 & 0.265256285860633 & 0.267803864578468 & 0.016676267416493 & 0.243863161690782 & 0.294851828966131 \tabularnewline
qual1 & 0.241687816420767 & 0.24280323964 & 0.0192020915678328 & 0.214385236788619 & 0.270925907847301 \tabularnewline
qual2 & 0.269125066083134 & 0.269073037149966 & 0.0116526729677312 & 0.251902506425988 & 0.293643704355846 \tabularnewline
qual3 & 0.224681362159599 & 0.223716395493177 & 0.0157515532132524 & 0.199711075978656 & 0.24967430153878 \tabularnewline
qual4 & 0.24591753081037 & 0.248083277960757 & 0.0114454007822246 & 0.232112310278697 & 0.265774783167419 \tabularnewline
qual5 & 0.246541743156134 & 0.246074729177342 & 0.0123914163655227 & 0.230088317455881 & 0.268537485746637 \tabularnewline
val1 & 0.354641845418734 & 0.351455170990019 & 0.0215047897942209 & 0.317111429172826 & 0.387293029885891 \tabularnewline
val2 & 0.285578173762984 & 0.286366745250293 & 0.0184189014604691 & 0.255662621333997 & 0.317327510195694 \tabularnewline
val3 & 0.249488369664068 & 0.251183302852625 & 0.0194076914566027 & 0.214365861063494 & 0.279111495554649 \tabularnewline
val4 & 0.327287859859557 & 0.325538846986569 & 0.0201600818820753 & 0.29607149050148 & 0.359403989006462 \tabularnewline
sat1 & 0.317089638101914 & 0.319443941297414 & 0.0164983786750859 & 0.298389326217584 & 0.349405827131457 \tabularnewline
sat2 & 0.31717904277573 & 0.318866400604621 & 0.015833716148813 & 0.296000957707778 & 0.343004710037015 \tabularnewline
sat3 & 0.24826373171461 & 0.246250664178951 & 0.0107829151832633 & 0.226156357637005 & 0.261975505896215 \tabularnewline
sat4 & 0.260077830203141 & 0.263209047750035 & 0.0145497410683456 & 0.241767191529195 & 0.290477243706804 \tabularnewline
loy1 & 0.37363548353555 & 0.368936509388389 & 0.0231846851201432 & 0.330621726902619 & 0.405790428948839 \tabularnewline
loy2 & 0.25076898924871 & 0.249497070590832 & 0.0251826081110594 & 0.206375745341271 & 0.288446486211379 \tabularnewline
loy3 & 0.371806236671019 & 0.367323422162318 & 0.0275592226564596 & 0.329426203988294 & 0.419410314848178 \tabularnewline
loy4 & 0.223807578649125 & 0.228371241601912 & 0.037077611034572 & 0.172784097710213 & 0.287039176214107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=11

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - WEIGHTS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Error[/C][C]perc.05[/C][C]perc.95[/C][/ROW]
[ROW][C]imag1[/C][C]0.200371808780435[/C][C]0.201025377602274[/C][C]0.0242688855257834[/C][C]0.161160760041098[/C][C]0.234465042899789[/C][/ROW]
[ROW][C]imag2[/C][C]0.29966889056204[/C][C]0.299290407811219[/C][C]0.0183032059237804[/C][C]0.273672137766797[/C][C]0.332171108065418[/C][/ROW]
[ROW][C]imag3[/C][C]0.309387723587126[/C][C]0.307706502655215[/C][C]0.0201832870671095[/C][C]0.273343859841009[/C][C]0.339206527919999[/C][/ROW]
[ROW][C]imag4[/C][C]0.182869660235561[/C][C]0.187584896529198[/C][C]0.027225031985465[/C][C]0.143608035395372[/C][C]0.229338187890433[/C][/ROW]
[ROW][C]imag5[/C][C]0.288678737793195[/C][C]0.283109168363886[/C][C]0.023294993258126[/C][C]0.249826667135974[/C][C]0.318597392420184[/C][/ROW]
[ROW][C]expe1[/C][C]0.23555165715769[/C][C]0.234669640818942[/C][C]0.0198249757907617[/C][C]0.206977839385599[/C][C]0.262908955616103[/C][/ROW]
[ROW][C]expe2[/C][C]0.281643158485162[/C][C]0.280715815882974[/C][C]0.015276334916969[/C][C]0.259225751433474[/C][C]0.310250933530914[/C][/ROW]
[ROW][C]expe3[/C][C]0.22372667680635[/C][C]0.223258843091441[/C][C]0.0177063479716998[/C][C]0.193204145256144[/C][C]0.249366555084841[/C][/ROW]
[ROW][C]expe4[/C][C]0.261063538397291[/C][C]0.262457817746605[/C][C]0.0184465620219533[/C][C]0.238486637072123[/C][C]0.298467027836289[/C][/ROW]
[ROW][C]expe5[/C][C]0.265256285860633[/C][C]0.267803864578468[/C][C]0.016676267416493[/C][C]0.243863161690782[/C][C]0.294851828966131[/C][/ROW]
[ROW][C]qual1[/C][C]0.241687816420767[/C][C]0.24280323964[/C][C]0.0192020915678328[/C][C]0.214385236788619[/C][C]0.270925907847301[/C][/ROW]
[ROW][C]qual2[/C][C]0.269125066083134[/C][C]0.269073037149966[/C][C]0.0116526729677312[/C][C]0.251902506425988[/C][C]0.293643704355846[/C][/ROW]
[ROW][C]qual3[/C][C]0.224681362159599[/C][C]0.223716395493177[/C][C]0.0157515532132524[/C][C]0.199711075978656[/C][C]0.24967430153878[/C][/ROW]
[ROW][C]qual4[/C][C]0.24591753081037[/C][C]0.248083277960757[/C][C]0.0114454007822246[/C][C]0.232112310278697[/C][C]0.265774783167419[/C][/ROW]
[ROW][C]qual5[/C][C]0.246541743156134[/C][C]0.246074729177342[/C][C]0.0123914163655227[/C][C]0.230088317455881[/C][C]0.268537485746637[/C][/ROW]
[ROW][C]val1[/C][C]0.354641845418734[/C][C]0.351455170990019[/C][C]0.0215047897942209[/C][C]0.317111429172826[/C][C]0.387293029885891[/C][/ROW]
[ROW][C]val2[/C][C]0.285578173762984[/C][C]0.286366745250293[/C][C]0.0184189014604691[/C][C]0.255662621333997[/C][C]0.317327510195694[/C][/ROW]
[ROW][C]val3[/C][C]0.249488369664068[/C][C]0.251183302852625[/C][C]0.0194076914566027[/C][C]0.214365861063494[/C][C]0.279111495554649[/C][/ROW]
[ROW][C]val4[/C][C]0.327287859859557[/C][C]0.325538846986569[/C][C]0.0201600818820753[/C][C]0.29607149050148[/C][C]0.359403989006462[/C][/ROW]
[ROW][C]sat1[/C][C]0.317089638101914[/C][C]0.319443941297414[/C][C]0.0164983786750859[/C][C]0.298389326217584[/C][C]0.349405827131457[/C][/ROW]
[ROW][C]sat2[/C][C]0.31717904277573[/C][C]0.318866400604621[/C][C]0.015833716148813[/C][C]0.296000957707778[/C][C]0.343004710037015[/C][/ROW]
[ROW][C]sat3[/C][C]0.24826373171461[/C][C]0.246250664178951[/C][C]0.0107829151832633[/C][C]0.226156357637005[/C][C]0.261975505896215[/C][/ROW]
[ROW][C]sat4[/C][C]0.260077830203141[/C][C]0.263209047750035[/C][C]0.0145497410683456[/C][C]0.241767191529195[/C][C]0.290477243706804[/C][/ROW]
[ROW][C]loy1[/C][C]0.37363548353555[/C][C]0.368936509388389[/C][C]0.0231846851201432[/C][C]0.330621726902619[/C][C]0.405790428948839[/C][/ROW]
[ROW][C]loy2[/C][C]0.25076898924871[/C][C]0.249497070590832[/C][C]0.0251826081110594[/C][C]0.206375745341271[/C][C]0.288446486211379[/C][/ROW]
[ROW][C]loy3[/C][C]0.371806236671019[/C][C]0.367323422162318[/C][C]0.0275592226564596[/C][C]0.329426203988294[/C][C]0.419410314848178[/C][/ROW]
[ROW][C]loy4[/C][C]0.223807578649125[/C][C]0.228371241601912[/C][C]0.037077611034572[/C][C]0.172784097710213[/C][C]0.287039176214107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=11

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=11

As an alternative you can also use a QR Code:  

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

BOOTSTRAP VALIDATION - WEIGHTS
OriginalMean.BootStd.Errorperc.05perc.95
imag10.2003718087804350.2010253776022740.02426888552578340.1611607600410980.234465042899789
imag20.299668890562040.2992904078112190.01830320592378040.2736721377667970.332171108065418
imag30.3093877235871260.3077065026552150.02018328706710950.2733438598410090.339206527919999
imag40.1828696602355610.1875848965291980.0272250319854650.1436080353953720.229338187890433
imag50.2886787377931950.2831091683638860.0232949932581260.2498266671359740.318597392420184
expe10.235551657157690.2346696408189420.01982497579076170.2069778393855990.262908955616103
expe20.2816431584851620.2807158158829740.0152763349169690.2592257514334740.310250933530914
expe30.223726676806350.2232588430914410.01770634797169980.1932041452561440.249366555084841
expe40.2610635383972910.2624578177466050.01844656202195330.2384866370721230.298467027836289
expe50.2652562858606330.2678038645784680.0166762674164930.2438631616907820.294851828966131
qual10.2416878164207670.242803239640.01920209156783280.2143852367886190.270925907847301
qual20.2691250660831340.2690730371499660.01165267296773120.2519025064259880.293643704355846
qual30.2246813621595990.2237163954931770.01575155321325240.1997110759786560.24967430153878
qual40.245917530810370.2480832779607570.01144540078222460.2321123102786970.265774783167419
qual50.2465417431561340.2460747291773420.01239141636552270.2300883174558810.268537485746637
val10.3546418454187340.3514551709900190.02150478979422090.3171114291728260.387293029885891
val20.2855781737629840.2863667452502930.01841890146046910.2556626213339970.317327510195694
val30.2494883696640680.2511833028526250.01940769145660270.2143658610634940.279111495554649
val40.3272878598595570.3255388469865690.02016008188207530.296071490501480.359403989006462
sat10.3170896381019140.3194439412974140.01649837867508590.2983893262175840.349405827131457
sat20.317179042775730.3188664006046210.0158337161488130.2960009577077780.343004710037015
sat30.248263731714610.2462506641789510.01078291518326330.2261563576370050.261975505896215
sat40.2600778302031410.2632090477500350.01454974106834560.2417671915291950.290477243706804
loy10.373635483535550.3689365093883890.02318468512014320.3306217269026190.405790428948839
loy20.250768989248710.2494970705908320.02518260811105940.2063757453412710.288446486211379
loy30.3718062366710190.3673234221623180.02755922265645960.3294262039882940.419410314848178
loy40.2238075786491250.2283712416019120.0370776110345720.1727840977102130.287039176214107







BOOTSTRAP VALIDATION - LOADINGS
OriginalMean.BootStd.Errorperc.05perc.95
imag10.7391666369038130.739657480063570.05451557361428390.6635002352804580.812296281483652
imag20.8913229823792570.890816575758340.01515609043893590.8633344130638840.912966157558843
imag30.862580781070710.8635611857384420.02119118877324410.8236777180741060.895849737173237
imag40.6395702568008990.646613609148660.06753506114585050.541381267388510.737284056376186
imag50.6961377773237060.6874907584398890.04284396858221590.6210312396727220.757857218875288
expe10.787643932696340.7872690120804250.0366724277399320.729894631147740.839339472338372
expe20.8237870749734150.8195570766268890.02904227769422790.7727982427597430.860140355723929
expe30.7319698394952610.7278652801027460.04047952263529160.6520875198230420.78405998868187
expe40.7728446342093660.7721409331873890.03101494248658470.7234528330434430.819646791516113
expe50.8178220449470480.8176272194820860.02712166004885470.7741844650570980.853983540386557
qual10.793031225236110.7928811119844730.0455065291228110.7174004278311450.843439536362381
qual20.86978359190350.8657593490577540.02036907564304680.8346710843075870.898692667375788
qual30.7638003540756440.760063559970610.04013922309360530.6825452966880.814785710775779
qual40.8227541998867430.8223495861201520.03345236559647320.7635165744584470.863764494101688
qual50.8124874122090520.809953375745820.03089620281221040.7652457955502940.857731035335497
val10.8587666514283780.8579582075120340.01808357744966440.8276565125346930.888492481654893
val20.83216388369480.834143638243760.03406063630246770.7757218761756630.885094320134291
val30.7595816282084020.7641240645214550.0517744504003280.6706977534780.840542225538648
val40.8197394973432080.8184557528196370.02786934928543920.7687477156207730.857716174052245
sat10.914618059150250.9123182174099520.01218189868395410.8885938376213490.931715204841578
sat20.9144517835548660.9129510359611760.01137706967963530.8945665692062070.92670319955588
sat30.8343152317318540.8247849574573770.03118813764002580.7701937652441490.866394974321402
sat40.818250968288110.8127897060442030.03583411605728810.7518722135366520.8636676212444
loy10.8878331820145780.8876913124528180.02244793952104560.8456112712215930.920660756513064
loy20.7241240382842360.7271677244205740.05695550882431710.6385611331079010.811845982870742
loy30.8807109910764550.8812878309132130.02022978111253960.8488210571886570.914193057968874
loy40.7114697739103020.717776384943740.06841359756433260.5996415999810310.807116177595747

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - LOADINGS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
imag1 & 0.739166636903813 & 0.73965748006357 & 0.0545155736142839 & 0.663500235280458 & 0.812296281483652 \tabularnewline
imag2 & 0.891322982379257 & 0.89081657575834 & 0.0151560904389359 & 0.863334413063884 & 0.912966157558843 \tabularnewline
imag3 & 0.86258078107071 & 0.863561185738442 & 0.0211911887732441 & 0.823677718074106 & 0.895849737173237 \tabularnewline
imag4 & 0.639570256800899 & 0.64661360914866 & 0.0675350611458505 & 0.54138126738851 & 0.737284056376186 \tabularnewline
imag5 & 0.696137777323706 & 0.687490758439889 & 0.0428439685822159 & 0.621031239672722 & 0.757857218875288 \tabularnewline
expe1 & 0.78764393269634 & 0.787269012080425 & 0.036672427739932 & 0.72989463114774 & 0.839339472338372 \tabularnewline
expe2 & 0.823787074973415 & 0.819557076626889 & 0.0290422776942279 & 0.772798242759743 & 0.860140355723929 \tabularnewline
expe3 & 0.731969839495261 & 0.727865280102746 & 0.0404795226352916 & 0.652087519823042 & 0.78405998868187 \tabularnewline
expe4 & 0.772844634209366 & 0.772140933187389 & 0.0310149424865847 & 0.723452833043443 & 0.819646791516113 \tabularnewline
expe5 & 0.817822044947048 & 0.817627219482086 & 0.0271216600488547 & 0.774184465057098 & 0.853983540386557 \tabularnewline
qual1 & 0.79303122523611 & 0.792881111984473 & 0.045506529122811 & 0.717400427831145 & 0.843439536362381 \tabularnewline
qual2 & 0.8697835919035 & 0.865759349057754 & 0.0203690756430468 & 0.834671084307587 & 0.898692667375788 \tabularnewline
qual3 & 0.763800354075644 & 0.76006355997061 & 0.0401392230936053 & 0.682545296688 & 0.814785710775779 \tabularnewline
qual4 & 0.822754199886743 & 0.822349586120152 & 0.0334523655964732 & 0.763516574458447 & 0.863764494101688 \tabularnewline
qual5 & 0.812487412209052 & 0.80995337574582 & 0.0308962028122104 & 0.765245795550294 & 0.857731035335497 \tabularnewline
val1 & 0.858766651428378 & 0.857958207512034 & 0.0180835774496644 & 0.827656512534693 & 0.888492481654893 \tabularnewline
val2 & 0.8321638836948 & 0.83414363824376 & 0.0340606363024677 & 0.775721876175663 & 0.885094320134291 \tabularnewline
val3 & 0.759581628208402 & 0.764124064521455 & 0.051774450400328 & 0.670697753478 & 0.840542225538648 \tabularnewline
val4 & 0.819739497343208 & 0.818455752819637 & 0.0278693492854392 & 0.768747715620773 & 0.857716174052245 \tabularnewline
sat1 & 0.91461805915025 & 0.912318217409952 & 0.0121818986839541 & 0.888593837621349 & 0.931715204841578 \tabularnewline
sat2 & 0.914451783554866 & 0.912951035961176 & 0.0113770696796353 & 0.894566569206207 & 0.92670319955588 \tabularnewline
sat3 & 0.834315231731854 & 0.824784957457377 & 0.0311881376400258 & 0.770193765244149 & 0.866394974321402 \tabularnewline
sat4 & 0.81825096828811 & 0.812789706044203 & 0.0358341160572881 & 0.751872213536652 & 0.8636676212444 \tabularnewline
loy1 & 0.887833182014578 & 0.887691312452818 & 0.0224479395210456 & 0.845611271221593 & 0.920660756513064 \tabularnewline
loy2 & 0.724124038284236 & 0.727167724420574 & 0.0569555088243171 & 0.638561133107901 & 0.811845982870742 \tabularnewline
loy3 & 0.880710991076455 & 0.881287830913213 & 0.0202297811125396 & 0.848821057188657 & 0.914193057968874 \tabularnewline
loy4 & 0.711469773910302 & 0.71777638494374 & 0.0684135975643326 & 0.599641599981031 & 0.807116177595747 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=12

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - LOADINGS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Error[/C][C]perc.05[/C][C]perc.95[/C][/ROW]
[ROW][C]imag1[/C][C]0.739166636903813[/C][C]0.73965748006357[/C][C]0.0545155736142839[/C][C]0.663500235280458[/C][C]0.812296281483652[/C][/ROW]
[ROW][C]imag2[/C][C]0.891322982379257[/C][C]0.89081657575834[/C][C]0.0151560904389359[/C][C]0.863334413063884[/C][C]0.912966157558843[/C][/ROW]
[ROW][C]imag3[/C][C]0.86258078107071[/C][C]0.863561185738442[/C][C]0.0211911887732441[/C][C]0.823677718074106[/C][C]0.895849737173237[/C][/ROW]
[ROW][C]imag4[/C][C]0.639570256800899[/C][C]0.64661360914866[/C][C]0.0675350611458505[/C][C]0.54138126738851[/C][C]0.737284056376186[/C][/ROW]
[ROW][C]imag5[/C][C]0.696137777323706[/C][C]0.687490758439889[/C][C]0.0428439685822159[/C][C]0.621031239672722[/C][C]0.757857218875288[/C][/ROW]
[ROW][C]expe1[/C][C]0.78764393269634[/C][C]0.787269012080425[/C][C]0.036672427739932[/C][C]0.72989463114774[/C][C]0.839339472338372[/C][/ROW]
[ROW][C]expe2[/C][C]0.823787074973415[/C][C]0.819557076626889[/C][C]0.0290422776942279[/C][C]0.772798242759743[/C][C]0.860140355723929[/C][/ROW]
[ROW][C]expe3[/C][C]0.731969839495261[/C][C]0.727865280102746[/C][C]0.0404795226352916[/C][C]0.652087519823042[/C][C]0.78405998868187[/C][/ROW]
[ROW][C]expe4[/C][C]0.772844634209366[/C][C]0.772140933187389[/C][C]0.0310149424865847[/C][C]0.723452833043443[/C][C]0.819646791516113[/C][/ROW]
[ROW][C]expe5[/C][C]0.817822044947048[/C][C]0.817627219482086[/C][C]0.0271216600488547[/C][C]0.774184465057098[/C][C]0.853983540386557[/C][/ROW]
[ROW][C]qual1[/C][C]0.79303122523611[/C][C]0.792881111984473[/C][C]0.045506529122811[/C][C]0.717400427831145[/C][C]0.843439536362381[/C][/ROW]
[ROW][C]qual2[/C][C]0.8697835919035[/C][C]0.865759349057754[/C][C]0.0203690756430468[/C][C]0.834671084307587[/C][C]0.898692667375788[/C][/ROW]
[ROW][C]qual3[/C][C]0.763800354075644[/C][C]0.76006355997061[/C][C]0.0401392230936053[/C][C]0.682545296688[/C][C]0.814785710775779[/C][/ROW]
[ROW][C]qual4[/C][C]0.822754199886743[/C][C]0.822349586120152[/C][C]0.0334523655964732[/C][C]0.763516574458447[/C][C]0.863764494101688[/C][/ROW]
[ROW][C]qual5[/C][C]0.812487412209052[/C][C]0.80995337574582[/C][C]0.0308962028122104[/C][C]0.765245795550294[/C][C]0.857731035335497[/C][/ROW]
[ROW][C]val1[/C][C]0.858766651428378[/C][C]0.857958207512034[/C][C]0.0180835774496644[/C][C]0.827656512534693[/C][C]0.888492481654893[/C][/ROW]
[ROW][C]val2[/C][C]0.8321638836948[/C][C]0.83414363824376[/C][C]0.0340606363024677[/C][C]0.775721876175663[/C][C]0.885094320134291[/C][/ROW]
[ROW][C]val3[/C][C]0.759581628208402[/C][C]0.764124064521455[/C][C]0.051774450400328[/C][C]0.670697753478[/C][C]0.840542225538648[/C][/ROW]
[ROW][C]val4[/C][C]0.819739497343208[/C][C]0.818455752819637[/C][C]0.0278693492854392[/C][C]0.768747715620773[/C][C]0.857716174052245[/C][/ROW]
[ROW][C]sat1[/C][C]0.91461805915025[/C][C]0.912318217409952[/C][C]0.0121818986839541[/C][C]0.888593837621349[/C][C]0.931715204841578[/C][/ROW]
[ROW][C]sat2[/C][C]0.914451783554866[/C][C]0.912951035961176[/C][C]0.0113770696796353[/C][C]0.894566569206207[/C][C]0.92670319955588[/C][/ROW]
[ROW][C]sat3[/C][C]0.834315231731854[/C][C]0.824784957457377[/C][C]0.0311881376400258[/C][C]0.770193765244149[/C][C]0.866394974321402[/C][/ROW]
[ROW][C]sat4[/C][C]0.81825096828811[/C][C]0.812789706044203[/C][C]0.0358341160572881[/C][C]0.751872213536652[/C][C]0.8636676212444[/C][/ROW]
[ROW][C]loy1[/C][C]0.887833182014578[/C][C]0.887691312452818[/C][C]0.0224479395210456[/C][C]0.845611271221593[/C][C]0.920660756513064[/C][/ROW]
[ROW][C]loy2[/C][C]0.724124038284236[/C][C]0.727167724420574[/C][C]0.0569555088243171[/C][C]0.638561133107901[/C][C]0.811845982870742[/C][/ROW]
[ROW][C]loy3[/C][C]0.880710991076455[/C][C]0.881287830913213[/C][C]0.0202297811125396[/C][C]0.848821057188657[/C][C]0.914193057968874[/C][/ROW]
[ROW][C]loy4[/C][C]0.711469773910302[/C][C]0.71777638494374[/C][C]0.0684135975643326[/C][C]0.599641599981031[/C][C]0.807116177595747[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=12

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=12

As an alternative you can also use a QR Code:  

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

BOOTSTRAP VALIDATION - LOADINGS
OriginalMean.BootStd.Errorperc.05perc.95
imag10.7391666369038130.739657480063570.05451557361428390.6635002352804580.812296281483652
imag20.8913229823792570.890816575758340.01515609043893590.8633344130638840.912966157558843
imag30.862580781070710.8635611857384420.02119118877324410.8236777180741060.895849737173237
imag40.6395702568008990.646613609148660.06753506114585050.541381267388510.737284056376186
imag50.6961377773237060.6874907584398890.04284396858221590.6210312396727220.757857218875288
expe10.787643932696340.7872690120804250.0366724277399320.729894631147740.839339472338372
expe20.8237870749734150.8195570766268890.02904227769422790.7727982427597430.860140355723929
expe30.7319698394952610.7278652801027460.04047952263529160.6520875198230420.78405998868187
expe40.7728446342093660.7721409331873890.03101494248658470.7234528330434430.819646791516113
expe50.8178220449470480.8176272194820860.02712166004885470.7741844650570980.853983540386557
qual10.793031225236110.7928811119844730.0455065291228110.7174004278311450.843439536362381
qual20.86978359190350.8657593490577540.02036907564304680.8346710843075870.898692667375788
qual30.7638003540756440.760063559970610.04013922309360530.6825452966880.814785710775779
qual40.8227541998867430.8223495861201520.03345236559647320.7635165744584470.863764494101688
qual50.8124874122090520.809953375745820.03089620281221040.7652457955502940.857731035335497
val10.8587666514283780.8579582075120340.01808357744966440.8276565125346930.888492481654893
val20.83216388369480.834143638243760.03406063630246770.7757218761756630.885094320134291
val30.7595816282084020.7641240645214550.0517744504003280.6706977534780.840542225538648
val40.8197394973432080.8184557528196370.02786934928543920.7687477156207730.857716174052245
sat10.914618059150250.9123182174099520.01218189868395410.8885938376213490.931715204841578
sat20.9144517835548660.9129510359611760.01137706967963530.8945665692062070.92670319955588
sat30.8343152317318540.8247849574573770.03118813764002580.7701937652441490.866394974321402
sat40.818250968288110.8127897060442030.03583411605728810.7518722135366520.8636676212444
loy10.8878331820145780.8876913124528180.02244793952104560.8456112712215930.920660756513064
loy20.7241240382842360.7271677244205740.05695550882431710.6385611331079010.811845982870742
loy30.8807109910764550.8812878309132130.02022978111253960.8488210571886570.914193057968874
loy40.7114697739103020.717776384943740.06841359756433260.5996415999810310.807116177595747







BOOTSTRAP VALIDATION - PATHS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.5592086022587740.5619189899297820.05283575652639270.4697134314988950.646747387606327
S1->S50.1873663171745290.1966017993811290.05005776483656630.1174603001544440.284057683791327
S1->S60.2893150689729770.3122935073530270.07151495444089230.203808531891270.427021500711318
S2->S30.8455578337633030.8421224739154920.02156242672681630.8053140203723680.870645577859655
S2->S40.1240422591040860.1277730150563870.06940776898043080.02241576625763820.224169904751213
S2->S50.005929005117052520.01634241218395170.0721281439783582-0.0922134692530190.130977793485982
S3->S40.6543947347663960.6472122315604610.07842377638008570.5151376313945690.782149874873193
S3->S50.1401265052438480.1285358549566920.07964987732849030.01487472567995270.258703297509078
S4->S50.5790418117369030.5735828608025480.07798016542836750.4251053529175050.694071154877896
S5->S60.4709176564624380.4498907679296870.07440275591679850.322855630367240.559059633308149

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - PATHS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S1->S2 & 0.559208602258774 & 0.561918989929782 & 0.0528357565263927 & 0.469713431498895 & 0.646747387606327 \tabularnewline
S1->S5 & 0.187366317174529 & 0.196601799381129 & 0.0500577648365663 & 0.117460300154444 & 0.284057683791327 \tabularnewline
S1->S6 & 0.289315068972977 & 0.312293507353027 & 0.0715149544408923 & 0.20380853189127 & 0.427021500711318 \tabularnewline
S2->S3 & 0.845557833763303 & 0.842122473915492 & 0.0215624267268163 & 0.805314020372368 & 0.870645577859655 \tabularnewline
S2->S4 & 0.124042259104086 & 0.127773015056387 & 0.0694077689804308 & 0.0224157662576382 & 0.224169904751213 \tabularnewline
S2->S5 & 0.00592900511705252 & 0.0163424121839517 & 0.0721281439783582 & -0.092213469253019 & 0.130977793485982 \tabularnewline
S3->S4 & 0.654394734766396 & 0.647212231560461 & 0.0784237763800857 & 0.515137631394569 & 0.782149874873193 \tabularnewline
S3->S5 & 0.140126505243848 & 0.128535854956692 & 0.0796498773284903 & 0.0148747256799527 & 0.258703297509078 \tabularnewline
S4->S5 & 0.579041811736903 & 0.573582860802548 & 0.0779801654283675 & 0.425105352917505 & 0.694071154877896 \tabularnewline
S5->S6 & 0.470917656462438 & 0.449890767929687 & 0.0744027559167985 & 0.32285563036724 & 0.559059633308149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=13

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - PATHS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Error[/C][C]perc.05[/C][C]perc.95[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.559208602258774[/C][C]0.561918989929782[/C][C]0.0528357565263927[/C][C]0.469713431498895[/C][C]0.646747387606327[/C][/ROW]
[ROW][C]S1->S5[/C][C]0.187366317174529[/C][C]0.196601799381129[/C][C]0.0500577648365663[/C][C]0.117460300154444[/C][C]0.284057683791327[/C][/ROW]
[ROW][C]S1->S6[/C][C]0.289315068972977[/C][C]0.312293507353027[/C][C]0.0715149544408923[/C][C]0.20380853189127[/C][C]0.427021500711318[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.845557833763303[/C][C]0.842122473915492[/C][C]0.0215624267268163[/C][C]0.805314020372368[/C][C]0.870645577859655[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.124042259104086[/C][C]0.127773015056387[/C][C]0.0694077689804308[/C][C]0.0224157662576382[/C][C]0.224169904751213[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.00592900511705252[/C][C]0.0163424121839517[/C][C]0.0721281439783582[/C][C]-0.092213469253019[/C][C]0.130977793485982[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.654394734766396[/C][C]0.647212231560461[/C][C]0.0784237763800857[/C][C]0.515137631394569[/C][C]0.782149874873193[/C][/ROW]
[ROW][C]S3->S5[/C][C]0.140126505243848[/C][C]0.128535854956692[/C][C]0.0796498773284903[/C][C]0.0148747256799527[/C][C]0.258703297509078[/C][/ROW]
[ROW][C]S4->S5[/C][C]0.579041811736903[/C][C]0.573582860802548[/C][C]0.0779801654283675[/C][C]0.425105352917505[/C][C]0.694071154877896[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.470917656462438[/C][C]0.449890767929687[/C][C]0.0744027559167985[/C][C]0.32285563036724[/C][C]0.559059633308149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=13

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=13

As an alternative you can also use a QR Code:  

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

BOOTSTRAP VALIDATION - PATHS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.5592086022587740.5619189899297820.05283575652639270.4697134314988950.646747387606327
S1->S50.1873663171745290.1966017993811290.05005776483656630.1174603001544440.284057683791327
S1->S60.2893150689729770.3122935073530270.07151495444089230.203808531891270.427021500711318
S2->S30.8455578337633030.8421224739154920.02156242672681630.8053140203723680.870645577859655
S2->S40.1240422591040860.1277730150563870.06940776898043080.02241576625763820.224169904751213
S2->S50.005929005117052520.01634241218395170.0721281439783582-0.0922134692530190.130977793485982
S3->S40.6543947347663960.6472122315604610.07842377638008570.5151376313945690.782149874873193
S3->S50.1401265052438480.1285358549566920.07964987732849030.01487472567995270.258703297509078
S4->S50.5790418117369030.5735828608025480.07798016542836750.4251053529175050.694071154877896
S5->S60.4709176564624380.4498907679296870.07440275591679850.322855630367240.559059633308149







BOOTSTRAP VALIDATION - RSQ
OriginalMean.BootStd.Errorperc.05perc.95
S20.3127142608402120.3185166522397450.05877890862307910.2206313523615760.418282217083706
S30.7149680502384880.7096305499374350.03585604515192110.6485500549643730.758023811841289
S40.5808912086750380.5775402432070770.062331124888710.4781267338432460.682369679516387
S50.7032496152739020.7103283254452240.03157932273360860.6456196415687730.756083321917645
S60.490253987568820.4963984278731890.04646194798679270.4169431854321540.571089500106211

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - RSQ \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S2 & 0.312714260840212 & 0.318516652239745 & 0.0587789086230791 & 0.220631352361576 & 0.418282217083706 \tabularnewline
S3 & 0.714968050238488 & 0.709630549937435 & 0.0358560451519211 & 0.648550054964373 & 0.758023811841289 \tabularnewline
S4 & 0.580891208675038 & 0.577540243207077 & 0.06233112488871 & 0.478126733843246 & 0.682369679516387 \tabularnewline
S5 & 0.703249615273902 & 0.710328325445224 & 0.0315793227336086 & 0.645619641568773 & 0.756083321917645 \tabularnewline
S6 & 0.49025398756882 & 0.496398427873189 & 0.0464619479867927 & 0.416943185432154 & 0.571089500106211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=14

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - RSQ[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Error[/C][C]perc.05[/C][C]perc.95[/C][/ROW]
[ROW][C]S2[/C][C]0.312714260840212[/C][C]0.318516652239745[/C][C]0.0587789086230791[/C][C]0.220631352361576[/C][C]0.418282217083706[/C][/ROW]
[ROW][C]S3[/C][C]0.714968050238488[/C][C]0.709630549937435[/C][C]0.0358560451519211[/C][C]0.648550054964373[/C][C]0.758023811841289[/C][/ROW]
[ROW][C]S4[/C][C]0.580891208675038[/C][C]0.577540243207077[/C][C]0.06233112488871[/C][C]0.478126733843246[/C][C]0.682369679516387[/C][/ROW]
[ROW][C]S5[/C][C]0.703249615273902[/C][C]0.710328325445224[/C][C]0.0315793227336086[/C][C]0.645619641568773[/C][C]0.756083321917645[/C][/ROW]
[ROW][C]S6[/C][C]0.49025398756882[/C][C]0.496398427873189[/C][C]0.0464619479867927[/C][C]0.416943185432154[/C][C]0.571089500106211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=14

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=14

As an alternative you can also use a QR Code:  

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

BOOTSTRAP VALIDATION - RSQ
OriginalMean.BootStd.Errorperc.05perc.95
S20.3127142608402120.3185166522397450.05877890862307910.2206313523615760.418282217083706
S30.7149680502384880.7096305499374350.03585604515192110.6485500549643730.758023811841289
S40.5808912086750380.5775402432070770.062331124888710.4781267338432460.682369679516387
S50.7032496152739020.7103283254452240.03157932273360860.6456196415687730.756083321917645
S60.490253987568820.4963984278731890.04646194798679270.4169431854321540.571089500106211







BOOTSTRAP VALIDATION - TOTAL EFFECTS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.5592086022587740.5619189899297820.05283575652639270.4697134314988950.646747387606327
S1->S30.4728432143477330.4738072381431350.05148878513174470.3884867130937220.558022692043901
S1->S40.3787916081737920.3798542322309070.05479626091989420.2926788537110450.462965686403106
S1->S50.4762759140613240.4839544812558820.05782729467880710.391624236028390.560751645938209
S1->S60.5136018062522420.528545172514970.05833477048386320.4371070640568810.62485929607517
S2->S30.8455578337633030.8421224739154920.02156242672681630.8053140203723680.870645577859655
S2->S40.677370853459270.6732103677226370.04258866252569780.6033131503721210.737167510075347
S2->S50.5166401155486910.5098593632260570.05453602220387890.4305041529077920.599652874344
S2->S60.2432949524486730.229473937143890.04623013541966690.1520769745715130.308788656227898
S3->S40.6543947347663960.6472122315604610.07842377638008570.5151376313945690.782149874873193
S3->S50.5190484180540720.4989411890279370.08443900203826130.3730248665979710.630527605523743
S3->S60.2444290646205590.2248527927413830.055140231541720.1378828344027850.313702039226463
S4->S50.5790418117369030.5735828608025480.07798016542836750.4251053529175050.694071154877896
S4->S60.2726810129769070.2595254876455050.06242199394929770.1630581817438350.355241051410326
S5->S60.4709176564624380.4498907679296870.07440275591679850.322855630367240.559059633308149

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - TOTAL EFFECTS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S1->S2 & 0.559208602258774 & 0.561918989929782 & 0.0528357565263927 & 0.469713431498895 & 0.646747387606327 \tabularnewline
S1->S3 & 0.472843214347733 & 0.473807238143135 & 0.0514887851317447 & 0.388486713093722 & 0.558022692043901 \tabularnewline
S1->S4 & 0.378791608173792 & 0.379854232230907 & 0.0547962609198942 & 0.292678853711045 & 0.462965686403106 \tabularnewline
S1->S5 & 0.476275914061324 & 0.483954481255882 & 0.0578272946788071 & 0.39162423602839 & 0.560751645938209 \tabularnewline
S1->S6 & 0.513601806252242 & 0.52854517251497 & 0.0583347704838632 & 0.437107064056881 & 0.62485929607517 \tabularnewline
S2->S3 & 0.845557833763303 & 0.842122473915492 & 0.0215624267268163 & 0.805314020372368 & 0.870645577859655 \tabularnewline
S2->S4 & 0.67737085345927 & 0.673210367722637 & 0.0425886625256978 & 0.603313150372121 & 0.737167510075347 \tabularnewline
S2->S5 & 0.516640115548691 & 0.509859363226057 & 0.0545360222038789 & 0.430504152907792 & 0.599652874344 \tabularnewline
S2->S6 & 0.243294952448673 & 0.22947393714389 & 0.0462301354196669 & 0.152076974571513 & 0.308788656227898 \tabularnewline
S3->S4 & 0.654394734766396 & 0.647212231560461 & 0.0784237763800857 & 0.515137631394569 & 0.782149874873193 \tabularnewline
S3->S5 & 0.519048418054072 & 0.498941189027937 & 0.0844390020382613 & 0.373024866597971 & 0.630527605523743 \tabularnewline
S3->S6 & 0.244429064620559 & 0.224852792741383 & 0.05514023154172 & 0.137882834402785 & 0.313702039226463 \tabularnewline
S4->S5 & 0.579041811736903 & 0.573582860802548 & 0.0779801654283675 & 0.425105352917505 & 0.694071154877896 \tabularnewline
S4->S6 & 0.272681012976907 & 0.259525487645505 & 0.0624219939492977 & 0.163058181743835 & 0.355241051410326 \tabularnewline
S5->S6 & 0.470917656462438 & 0.449890767929687 & 0.0744027559167985 & 0.32285563036724 & 0.559059633308149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154666&T=15

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - TOTAL EFFECTS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Error[/C][C]perc.05[/C][C]perc.95[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.559208602258774[/C][C]0.561918989929782[/C][C]0.0528357565263927[/C][C]0.469713431498895[/C][C]0.646747387606327[/C][/ROW]
[ROW][C]S1->S3[/C][C]0.472843214347733[/C][C]0.473807238143135[/C][C]0.0514887851317447[/C][C]0.388486713093722[/C][C]0.558022692043901[/C][/ROW]
[ROW][C]S1->S4[/C][C]0.378791608173792[/C][C]0.379854232230907[/C][C]0.0547962609198942[/C][C]0.292678853711045[/C][C]0.462965686403106[/C][/ROW]
[ROW][C]S1->S5[/C][C]0.476275914061324[/C][C]0.483954481255882[/C][C]0.0578272946788071[/C][C]0.39162423602839[/C][C]0.560751645938209[/C][/ROW]
[ROW][C]S1->S6[/C][C]0.513601806252242[/C][C]0.52854517251497[/C][C]0.0583347704838632[/C][C]0.437107064056881[/C][C]0.62485929607517[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.845557833763303[/C][C]0.842122473915492[/C][C]0.0215624267268163[/C][C]0.805314020372368[/C][C]0.870645577859655[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.67737085345927[/C][C]0.673210367722637[/C][C]0.0425886625256978[/C][C]0.603313150372121[/C][C]0.737167510075347[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.516640115548691[/C][C]0.509859363226057[/C][C]0.0545360222038789[/C][C]0.430504152907792[/C][C]0.599652874344[/C][/ROW]
[ROW][C]S2->S6[/C][C]0.243294952448673[/C][C]0.22947393714389[/C][C]0.0462301354196669[/C][C]0.152076974571513[/C][C]0.308788656227898[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.654394734766396[/C][C]0.647212231560461[/C][C]0.0784237763800857[/C][C]0.515137631394569[/C][C]0.782149874873193[/C][/ROW]
[ROW][C]S3->S5[/C][C]0.519048418054072[/C][C]0.498941189027937[/C][C]0.0844390020382613[/C][C]0.373024866597971[/C][C]0.630527605523743[/C][/ROW]
[ROW][C]S3->S6[/C][C]0.244429064620559[/C][C]0.224852792741383[/C][C]0.05514023154172[/C][C]0.137882834402785[/C][C]0.313702039226463[/C][/ROW]
[ROW][C]S4->S5[/C][C]0.579041811736903[/C][C]0.573582860802548[/C][C]0.0779801654283675[/C][C]0.425105352917505[/C][C]0.694071154877896[/C][/ROW]
[ROW][C]S4->S6[/C][C]0.272681012976907[/C][C]0.259525487645505[/C][C]0.0624219939492977[/C][C]0.163058181743835[/C][C]0.355241051410326[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.470917656462438[/C][C]0.449890767929687[/C][C]0.0744027559167985[/C][C]0.32285563036724[/C][C]0.559059633308149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154666&T=15

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154666&T=15

As an alternative you can also use a QR Code:  

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

BOOTSTRAP VALIDATION - TOTAL EFFECTS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.5592086022587740.5619189899297820.05283575652639270.4697134314988950.646747387606327
S1->S30.4728432143477330.4738072381431350.05148878513174470.3884867130937220.558022692043901
S1->S40.3787916081737920.3798542322309070.05479626091989420.2926788537110450.462965686403106
S1->S50.4762759140613240.4839544812558820.05782729467880710.391624236028390.560751645938209
S1->S60.5136018062522420.528545172514970.05833477048386320.4371070640568810.62485929607517
S2->S30.8455578337633030.8421224739154920.02156242672681630.8053140203723680.870645577859655
S2->S40.677370853459270.6732103677226370.04258866252569780.6033131503721210.737167510075347
S2->S50.5166401155486910.5098593632260570.05453602220387890.4305041529077920.599652874344
S2->S60.2432949524486730.229473937143890.04623013541966690.1520769745715130.308788656227898
S3->S40.6543947347663960.6472122315604610.07842377638008570.5151376313945690.782149874873193
S3->S50.5190484180540720.4989411890279370.08443900203826130.3730248665979710.630527605523743
S3->S60.2444290646205590.2248527927413830.055140231541720.1378828344027850.313702039226463
S4->S50.5790418117369030.5735828608025480.07798016542836750.4251053529175050.694071154877896
S4->S60.2726810129769070.2595254876455050.06242199394929770.1630581817438350.355241051410326
S5->S60.4709176564624380.4498907679296870.07440275591679850.322855630367240.559059633308149



Parameters (Session):
par1 = IMAG EXPE QUAL VAL SAT LOY ; par2 = A A A A A A ; par3 = 1 2 3 4 5 ; par4 = 6 7 8 9 10 ; par5 = 11 12 13 14 15 ; par6 = 16 17 18 19 ; par7 = 20 21 22 23 ; par8 = 24 25 26 27 ; par11 = 0 0 0 0 0 0 ; par12 = 1 0 0 0 0 0 ; par13 = 0 1 0 0 0 0 ; par14 = 0 1 1 0 0 0 ; par15 = 1 1 1 1 0 0 ; par16 = 1 0 0 0 1 0 ;
Parameters (R input):
par1 = IMAG EXPE QUAL VAL SAT LOY ; par2 = A A A A A A ; par3 = 1 2 3 4 5 ; par4 = 6 7 8 9 10 ; par5 = 11 12 13 14 15 ; par6 = 16 17 18 19 ; par7 = 20 21 22 23 ; par8 = 24 25 26 27 ; par9 = ; par10 = ; par11 = 0 0 0 0 0 0 ; par12 = 1 0 0 0 0 0 ; par13 = 0 1 0 0 0 0 ; par14 = 0 1 1 0 0 0 ; par15 = 1 1 1 1 0 0 ; par16 = 1 0 0 0 1 0 ; par17 = ; par18 = ;
R code (references can be found in the software module):
library(plspm)
library(diagram)
y <- as.data.frame(t(y))
is.data.frame(y)
head(y)
trim <- function(char) {
return(sub('s+$', '', sub('^s+', '', char)))
}
(latnames <- strsplit(par1,' ')[[1]])
(n <- length(latnames))
(L1 <- as.numeric(strsplit(par3,' ')[[1]]))
(L2 <- as.numeric(strsplit(par4,' ')[[1]]))
(L3 <- as.numeric(strsplit(par5,' ')[[1]]))
(L4 <- as.numeric(strsplit(par6,' ')[[1]]))
(L5 <- as.numeric(strsplit(par7,' ')[[1]]))
(L6 <- as.numeric(strsplit(par8,' ')[[1]]))
(L7 <- as.numeric(strsplit(par9,' ')[[1]]))
(L8 <- as.numeric(strsplit(par10,' ')[[1]]))
(S1 <- as.numeric(strsplit(par11,' ')[[1]]))
(S2 <- as.numeric(strsplit(par12,' ')[[1]]))
(S3 <- as.numeric(strsplit(par13,' ')[[1]]))
(S4 <- as.numeric(strsplit(par14,' ')[[1]]))
(S5 <- as.numeric(strsplit(par15,' ')[[1]]))
(S6 <- as.numeric(strsplit(par16,' ')[[1]]))
(S7 <- as.numeric(strsplit(par17,' ')[[1]]))
(S8 <- as.numeric(strsplit(par18,' ')[[1]]))
if (n==1) sat.mat <- rbind(S1)
if (n==2) sat.mat <- rbind(S1,S2)
if (n==3) sat.mat <- rbind(S1,S2,S3)
if (n==4) sat.mat <- rbind(S1,S2,S3,S4)
if (n==5) sat.mat <- rbind(S1,S2,S3,S4,S5)
if (n==6) sat.mat <- rbind(S1,S2,S3,S4,S5,S6)
if (n==7) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7)
if (n==8) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7,S8)
sat.mat
if (n==1) sat.sets <- list(L1)
if (n==2) sat.sets <- list(L1,L2)
if (n==3) sat.sets <- list(L1,L2,L3)
if (n==4) sat.sets <- list(L1,L2,L3,L4)
if (n==5) sat.sets <- list(L1,L2,L3,L4,L5)
if (n==6) sat.sets <- list(L1,L2,L3,L4,L5,L6)
if (n==7) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7)
if (n==8) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7,L8)
sat.sets
(sat.mod <- strsplit(par2,' ')[[1]])
res <- plspm(x=y, sat.mat, sat.sets, sat.mod, scheme='centroid', scaled=TRUE, boot.val=TRUE)
(r <- summary(res))
myr <- res$path.coefs
myind <- 1
for (j in 1:(length(sat.mat[1,])-1)) {
for (i in 1:length(sat.mat[,1])) {
if (sat.mat[i,j] == 1) {
if ((res$boot$path[myind,'perc.05'] < 0) && (res$boot$path[myind,'perc.95'] > 0)) {
myr[i,j] = 0
}
myind = myind + 1
}
}
}
bitmap(file='test1.png')
plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames,
lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle',
box.prop = 0.5, box.cex = 1, arr.type = 'triangle',
arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue',
arr.col = 'blue', arr.width = 0.2, main = c('Inner Model',
'Path Coefficients'))
dev.off()
myr <- res$path.coefs
myind <- 1
myi <- 1
for (j in 1:(length(sat.mat[1,])-1)) {
for (i in 1:length(sat.mat[,1])) {
if (i > j) {
myr[i,j] = res$boot$total.efs[myi,'Original']
myi = myi + 1
if ((res$boot$total.efs[myind,'perc.05'] < 0) && (res$boot$total.efs[myind,'perc.95'] > 0)) {
myr[i,j] = 0
}
myind = myind + 1
}
}
}
bitmap(file='test2.png')
plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames,
lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle',
box.prop = 0.5, box.cex = 1, arr.type = 'triangle',
arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue',
arr.col = 'blue', arr.width = 0.2, main = c('Inner Model',
'Total Effects'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'MODEL SPECIFICATION',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of Cases',header=TRUE)
a<-table.element(a,r$xxx$obs)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Latent Variables',header=TRUE)
a<-table.element(a,n)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Manifest Variables',header=TRUE)
a<-table.element(a,length(y[1,]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Scaled?',header=TRUE)
a<-table.element(a,r$xxx$scaled)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Weighting Scheme',header=TRUE)
a<-table.element(a,r$xx$scheme)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bootstrapping?',header=TRUE)
a<-table.element(a,r$xx$boot.val)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bootstrap samples',header=TRUE)
a<-table.element(a,r$xx$br)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BLOCKS DEFINITION',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
a<-table.element(a,'Type',header=TRUE)
a<-table.element(a,'NMVs',header=TRUE)
a<-table.element(a,'Mode',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],header=TRUE)
a<-table.element(a,r$input$Type[i])
a<-table.element(a,r$unidim$MVs[i])
a<-table.element(a,r$unidim$Type.measure[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BLOCKS UNIDIMENSIONALITY',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
a<-table.element(a,'Type.measure',header=TRUE)
a<-table.element(a,'MVs',header=TRUE)
a<-table.element(a,'eig.1st',header=TRUE)
a<-table.element(a,'eig.2nd',header=TRUE)
a<-table.element(a,'C.alpha',header=TRUE)
a<-table.element(a,'DG.rho',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],header=TRUE)
a<-table.element(a,r$unidim$Type.measure[i])
a<-table.element(a,r$unidim$MVs[i])
a<-table.element(a,r$unidim$eig.1st[i])
a<-table.element(a,r$unidim$eig.2nd[i])
a<-table.element(a,r$unidim$C.alpha[i])
a<-table.element(a,r$unidim$DG.rho[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'OUTER MODEL',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
a<-table.element(a,'weights',header=TRUE)
a<-table.element(a,'std.loads',header=TRUE)
a<-table.element(a,'communal',header=TRUE)
a<-table.element(a,'redundan',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],5,header=TRUE)
a<-table.row.end(a)
for (j in 1:length(r$outer.mod[[i]][,1])) {
a<-table.row.start(a)
a<-table.element(a,rownames(r$outer.mod[[i]])[j],header=T)
a<-table.element(a,r$outer.mod[[i]][j,1])
a<-table.element(a,r$outer.mod[[i]][j,2])
a<-table.element(a,r$outer.mod[[i]][j,3])
a<-table.element(a,r$outer.mod[[i]][j,4])
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'CORRELATIONS BETWEEN MVs AND LVs',n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
for (iii in 1:n) {
a<-table.element(a,latnames[iii],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],n+1,header=TRUE)
a<-table.row.end(a)
for (j in 1:length(r$outer.cor[[i]][,1])) {
a<-table.row.start(a)
a<-table.element(a,rownames(r$outer.cor[[i]])[j],header=T)
for (iii in 1:n) {
a<-table.element(a,r$outer.cor[[i]][j,iii])
}
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'INNER MODEL',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
a<-table.element(a,'Concept',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
for (i in 1:(length(labels(r$inner.mod)))) {
a<-table.row.start(a)
print (paste('i=',i,sep=''))
a<-table.element(a,labels(r$inner.mod)[i],3,header=TRUE)
a<-table.row.end(a)
for (j in 1:length(r$inner.mod[[i]][,1])) {
print (paste('j=',j,sep=''))
a<-table.row.start(a)
a<-table.element(a,rownames(r$inner.mod[[i]])[j],header=T)
a<-table.element(a,r$inner.mod[[i]][j,1],header=T)
a<-table.element(a,r$inner.mod[[i]][j,2])
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable6.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'CORRELATIONS BETWEEN LVs',n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (iii in 1:n) {
a<-table.element(a,latnames[iii],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],header=T)
for (j in 1:n) {
a<-table.element(a,r$latent.cor[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable7.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'SUMMARY INNER MODEL',8,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
a<-table.element(a,'LV.Type',header=TRUE)
a<-table.element(a,'Measure',header=TRUE)
a<-table.element(a,'MVs',header=TRUE)
a<-table.element(a,'R.square',header=TRUE)
a<-table.element(a,'Av.Commu',header=TRUE)
a<-table.element(a,'Av.Redun',header=TRUE)
a<-table.element(a,'AVE',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],header=T)
a<-table.element(a,r$inner.sum[i,1])
a<-table.element(a,r$inner.sum[i,2])
a<-table.element(a,r$inner.sum[i,3])
a<-table.element(a,r$inner.sum[i,4])
a<-table.element(a,r$inner.sum[i,5])
a<-table.element(a,r$inner.sum[i,6])
a<-table.element(a,r$inner.sum[i,7])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'GOODNESS-OF-FIT',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'GoF',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
for (i in 1:4) {
a<-table.row.start(a)
a<-table.element(a,r$gof[i,1],header=T)
a<-table.element(a,r$gof[i,2])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable9.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TOTAL EFFECTS',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'relationships',header=TRUE)
a<-table.element(a,'dir.effect',header=TRUE)
a<-table.element(a,'ind.effect',header=TRUE)
a<-table.element(a,'tot.effect',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(r$effects[,1])) {
a<-table.row.start(a)
a<-table.element(a,r$effects[i,1],header=T)
a<-table.element(a,r$effects[i,2])
a<-table.element(a,r$effects[i,3])
a<-table.element(a,r$effects[i,4])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable10.tab')
dum <- r$boot$weights
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BOOTSTRAP VALIDATION - WEIGHTS',length(colnames(dum))+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (i in 1:length(colnames(dum))) {
a<-table.element(a,colnames(dum)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:length(rownames(dum))) {
a<-table.row.start(a)
a<-table.element(a,rownames(dum)[i],header=T)
for (j in 1:length(colnames(dum))) {
a<-table.element(a,dum[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable11.tab')
dum <- r$boot$loadings
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BOOTSTRAP VALIDATION - LOADINGS',length(colnames(dum))+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (i in 1:length(colnames(dum))) {
a<-table.element(a,colnames(dum)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:length(rownames(dum))) {
a<-table.row.start(a)
a<-table.element(a,rownames(dum)[i],header=T)
for (j in 1:length(colnames(dum))) {
a<-table.element(a,dum[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable12.tab')
dum <- r$boot$paths
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BOOTSTRAP VALIDATION - PATHS',length(colnames(dum))+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (i in 1:length(colnames(dum))) {
a<-table.element(a,colnames(dum)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:length(rownames(dum))) {
a<-table.row.start(a)
a<-table.element(a,rownames(dum)[i],header=T)
for (j in 1:length(colnames(dum))) {
a<-table.element(a,dum[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable13.tab')
dum <- r$boot$rsq
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BOOTSTRAP VALIDATION - RSQ',length(colnames(dum))+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (i in 1:length(colnames(dum))) {
a<-table.element(a,colnames(dum)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:length(rownames(dum))) {
a<-table.row.start(a)
a<-table.element(a,rownames(dum)[i],header=T)
for (j in 1:length(colnames(dum))) {
a<-table.element(a,dum[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable14.tab')
dum <- r$boot$total.efs
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'BOOTSTRAP VALIDATION - TOTAL EFFECTS',length(colnames(dum))+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (i in 1:length(colnames(dum))) {
a<-table.element(a,colnames(dum)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:length(rownames(dum))) {
a<-table.row.start(a)
a<-table.element(a,rownames(dum)[i],header=T)
for (j in 1:length(colnames(dum))) {
a<-table.element(a,dum[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable15.tab')
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