FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

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 computationSun, 05 Sep 2010 15:14:51 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Sep/05/t128369985538nplzee4d983vr.htm/, Retrieved Fri, 03 May 2024 07:15:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=79513, Retrieved Fri, 03 May 2024 07:15:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact261

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Kernel Density Estimation] [new release test] [2010-09-05 10:36:46] [b98453cac15ba1066b407e146608df68]
- RMPD  [Partial Least Squares - Path Modeling] [] [2010-09-05 14:06:57] [b98453cac15ba1066b407e146608df68]
- R         [Partial Least Squares - Path Modeling] [PLS-PM Paper] [2010-09-05 15:14:51] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
56	34	5	155	124.9	65.55	44.07	4	-2	-2	-2	5	8	11	3
70	40	16	137	550.95	191.95	113.6	4	4	4	-2	8	2	11	3
47	28	15	72	431.64	165.86	112.46	4	4	-2	4	4	4	7	6
12	10	0	0	0	0	0	-2	-2	-2	-2	8	0	12	8
31	20	3	75	111.97	62.78	72.72	0	-2	4	4	6	10	7	6
46	41	2	160	222.41	94.36	70.55	4	0	-2	4	10	8	12	12
38	37	15	165	192.3	94.45	68.92	4	4	4	4	6	7	4	7
33	30	9	211	238.98	106.73	71.66	4	4	4	4	9	7	13	13
63	36	9	83	306.95	95.4	72.51	4	4	-2	-2	10	6	10	5
28	15	17	105	377.49	133.03	93.05	4	4	4	4	3	0	8	8
45	31	13	138	433.17	139.95	145.32	4	-2	-2	4	10	8	11	9
24	17	8	63	142.63	75.71	74.55	4	4	4	4	2	6	6	5
36	25	6	139	158.74	73.83	53.7	4	0	4	4	6	8	8	10
30	25	38	98	199.55	79.16	65.84	4	0	-2	4	5	1	6	2
58	52	8	142	224.65	106.73	80.73	4	0	4	4	2	6	13	11
51	34	12	143	270.93	120.9	87.7	4	4	4	4	7	8	14	11
45	42	21	167	241.92	92.99	61.24	0	4	4	4	6	6	5	10
65	40	7	134	224.99	93.1	94.19	4	-2	4	4	4	4	10	10
64	49	8	135	172.84	76.53	84.26	4	4	4	0	4	7	6	7
24	24	3	83	99.61	68.4	49.84	4	0	-2	4	4	10	7	4
51	33	32	180	395.78	167.04	124.37	4	4	4	4	2	2	10	9
32	26	18	122	443.16	133.9	77.13	4	0	4	-2	8	6	10	6
16	10	2	79	98	61.52	66.85	4	4	4	4	5	8	7	3
38	31	12	147	196.75	86.12	57.65	4	-2	-2	4	8	4	7	8
75	52	10	178	301.11	125.24	76.37	4	4	4	4	7	6	11	14
56	31	15	190	281.28	113.75	85.74	4	4	4	4	4	4	8	0
38	34	17	152	310.81	114.51	74.23	4	0	4	4	2	1	6	0
46	43	18	177	370.86	143.62	74.06	4	4	-2	4	8	7	12	6
32	31	14	142	446.36	165.41	90.25	4	-2	-2	4	10	4	14	8
62	60	18	127	336.62	115.19	75.11	4	-2	4	4	6	7	12	7
46	36	6	125	134.66	75.43	69.89	4	-2	-2	-2	8	2	8	5
54	53	17	180	333.21	126.88	71.85	4	0	-2	4	4	10	12	9
43	33	17	108	326.98	113.95	70.19	4	0	4	4	10	9	12	8
74	51	26	175	520.95	215.17	202.68	4	4	4	4	10	9	14	12
24	24	3	137	136.04	72.16	52.71	4	4	4	4	4	10	4	12
42	29	15	168	176.99	87.94	53.83	4	0	4	4	4	2	7	7
33	31	13	178	448.54	173.34	97.73	4	-2	-2	4	6	8	12	10
44	41	13	111	171.14	81.27	62.99	4	0	-2	4	5	4	3	7
53	37	8	163	459.07	170.49	132.84	4	4	4	4	6	8	1	4
31	31	9	159	158.74	80.87	68.29	4	-2	4	4	10	2	8	0
41	17	17	160	447.5	168.71	104.5	4	-2	4	4	9	7	7	5
51	37	28	140	581.06	200.45	110.77	4	0	4	4	6	4	8	6
35	31	17	111	386.25	133.57	87.19	4	0	4	4	3	2	8	10
28	25	17	140	359.99	126.07	78	4	0	0	4	6	0	12	12
41	33	8	133	280.26	112.01	110.59	4	4	4	4	8	1	8	4
45	39	10	117	299.28	105.04	56.78	4	4	0	4	9	4	12	8
26	14	12	50	170.84	94.55	86.38	4	-2	-2	4	10	8	8	0
43	29	10	175	327.96	116.07	89.85	4	-2	4	4	6	4	9	7
1	0	6	148	144.41	73.88	51.52	4	4	4	4	7	10	10	11
58	46	28	181	392.62	154.52	80.91	4	4	-2	4	10	8	14	12
21	15	8	153	154.13	73.62	55.9	4	-2	4	4	10	0	8	10
26	20	28	76	261.71	84.64	81.66	4	0	4	0	10	6	0	4
35	34	9	75	187.08	86.4	43.71	4	4	-2	4	9	5	7	6
50	40	12	125	259.49	115.13	57.96	4	0	-2	-2	10	4	11	12
58	57	26	166	296.36	112.81	83.42	4	-2	4	4	4	6	11	8
25	24	18	152	506.74	195.97	111.89	-2	0	4	4	6	2	12	6
76	54	26	178	553.98	174.91	107.6	0	0	4	-2	10	10	14	11
47	42	21	126	257.89	107.21	72.74	4	-2	4	4	0	0	0	0
42	29	15	190	474.79	171.7	122.74	4	4	-2	4	6	8	8	8
3	0	17	129	309.51	128.87	76.59	4	0	4	4	2	2	5	2
80	73	3	107	113.72	66.61	77.73	4	4	-2	4	6	6	12	5
34	33	11	97	257.55	88.36	62.56	4	0	4	4	4	3	9	2
60	43	6	71	303.42	120.15	79.75	-2	0	0	4	5	6	11	10
43	29	2	92	88.7	62.14	69.51	0	0	0	4	4	3	4	4
23	22	7	145	70.08	54.52	59.7	0	4	0	4	5	2	4	0
52	48	5	85	148.39	83.7	45.19	-2	-2	-2	-2	5	2	7	0
14	10	16	159	246.87	114.4	69.51	4	-2	-2	-2	4	0	5	3
49	36	15	197	316.33	114.45	59.29	4	0	4	4	10	3	8	5
53	38	7	171	169.74	97.16	76.27	4	-2	4	4	6	6	9	8
52	49	10	128	283.99	98.38	67.38	4	4	0	4	10	10	12	5
37	28	10	140	150.44	73.91	49.34	4	4	-2	4	4	5	9	5
42	35	13	159	176.16	75.88	67.48	4	4	-2	4	6	2	10	0
49	43	22	180	392.61	158.84	119.72	4	4	4	4	10	2	10	5
40	39	9	145	235.59	105.18	95.47	4	4	4	4	4	2	10	11
50	33	22	162	1632.54	472.94	291.98	4	4	4	4	10	10	15	14
39	37	9	144	226.23	96.33	56.36	4	-2	-2	4	10	8	8	4
39	17	8	99	245.68	95.95	61.84	-2	-2	0	4	6	2	8	0
0	0	14	85	405.24	136.41	119.15	-2	-2	-2	4	2	0	3	5
10	10	6	100	167.91	74.16	56.85	4	4	4	4	6	4	13	0
41	39	16	167	361.29	165.1	120.98	4	-2	-2	4	9	2	6	6
28	22	13	160	275.59	98.39	70.78	4	0	0	4	6	9	6	11
43	22	10	143	287.48	104.35	58.13	4	4	4	4	7	5	11	8
37	34	20	169	216.41	96.09	63.56	4	-2	4	-2	10	4	8	3
15	12	23	97	376.42	133.68	51.49	4	4	-2	-2	2	2	10	3
38	29	29	152	509.97	160.12	111.18	4	4	4	4	9	8	14	12
22	18	13	94	289.48	87.13	75.79	0	0	0	0	10	4	10	7
35	33	15	137	520.97	163.58	118.4	-2	-2	4	4	6	2	9	0
7	7	0	0	0	0	0	4	-2	-2	4	2	5	14	0
43	31	8	183	209.14	91.17	79.62	4	4	4	4	7	0	10	9
29	39	9	106	194.69	89.64	64.99	4	0	-2	4	8	5	12	2
50	44	19	166	323.56	128.29	84.88	4	4	-2	4	10	2	14	2
44	27	28	139	507.98	166.57	70.66	4	0	4	4	7	8	6	9
45	26	12	155	265.14	109.83	35.8	-2	4	4	4	8	0	7	6
49	35	10	123	119.11	72.36	46.4	4	0	4	-2	2	4	9	3
32	13	7	28	94.96	67.4	61.17	4	-2	4	4	8	4	6	3
82	42	23	141	335.78	126.3	118.59	4	4	4	4	7	7	9	8
59	45	19	162	430.35	170.66	113.88	4	0	4	4	10	0	15	6
0	0	5	66	90.55	58.46	61.41	4	-2	4	4	6	2	8	4
22	14	25	95	359.52	119.05	88.14	4	4	4	4	7	0	2	0
6	0	8	109	137.46	81.56	67.17	4	4	4	4	6	7	10	8
16	16	3	26	164.69	85.39	89.45	4	0	-2	4	3	6	5	0
35	23	9	141	263.75	101.27	83.41	4	4	4	4	6	5	13	11
41	39	16	148	353.12	142.92	70.79	4	4	-2	4	7	0	12	13
44	29	8	74	332.74	103.99	70.21	4	4	4	4	5	7	8	4
44	36	10	167	224.63	97.87	60.45	4	4	-2	4	6	5	6	2
19	19	6	68	124.4	71.46	51.17	4	-2	4	4	4	0	0	0
27	18	15	177	195.29	85.98	56.93	4	4	4	4	6	0	8	4
0	0	16	153	305.43	127.72	108.1	-2	4	-2	-2	2	0	10	11
36	20	22	100	341.47	134.88	92.82	4	4	4	-2	4	1	0	0
71	52	8	70	154.54	76.36	78.28	4	4	-2	-2	10	9	2	5
18	14	3	44	250.59	75.61	56.84	4	4	4	4	4	3	6	0
48	47	12	104	449.02	145.53	74.74	4	4	-2	4	7	3	9	4
62	54	22	164	649.48	223.29	153.23	4	4	-2	4	9	7	12	7
42	24	9	80	260.15	98.2	51.23	4	4	4	4	8	4	8	0
35	22	7	165	210.05	86.05	58.58	4	4	4	4	6	2	8	0
69	57	12	171	372.24	131.36	110.18	4	4	4	4	10	10	13	13
70	38	26	157	388.17	127.1	70.01	0	4	4	4	10	8	13	12
69	45	11	123	181.49	88.64	58.72	-2	-2	4	0	3	2	7	2
26	19	14	169	270.36	104.02	51.9	4	4	-2	-2	10	2	8	0
56	45	12	164	309.63	124.18	111.09	4	-2	4	4	10	8	13	10
40	39	23	154	325.01	111.63	62.88	4	0	4	4	5	6	10	0
47	39	5	154	195.6	91.82	77.8	4	4	4	4	10	8	13	13
65	45	15	209	272.61	112.83	122.3	4	4	-2	4	6	4	8	10
48	47	18	139	187.55	87.66	66.52	4	4	4	4	3	0	3	4
69	51	15	103	693.71	171.29	113.07	4	-2	-2	4	8	4	12	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server193.190.124.10:1001 @ 193.190.124.10:1001

\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 & 17 seconds \tabularnewline
R Server & 193.190.124.10:1001 @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]193.190.124.10:1001 @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server193.190.124.10:1001 @ 193.190.124.10:1001






PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
MODEL SPECIFICATION
Number of Cases125
Latent Variables6
Manifest Variables15
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 & 125 \tabularnewline
Latent Variables & 6 \tabularnewline
Manifest Variables & 15 \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=79513&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]125[/C][/ROW]
[ROW][C]Latent Variables[/C][C]6[/C][/ROW]
[ROW][C]Manifest Variables[/C][C]15[/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=79513&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=1

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


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

PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
MODEL SPECIFICATION
Number of Cases125
Latent Variables6
Manifest Variables15
Scaled?TRUE
Weighting Schemecentroid
Bootstrapping?TRUE
Bootstrap samples100






BLOCKS DEFINITION
BlockTypeNMVsMode
COMPUTEExogenous2Reflective
REVIEWEndogenous5Reflective
EXAM0Endogenous2Formative
EXAM1Endogenous2Formative
EXAM2Endogenous2Formative
EXAM3Endogenous2Formative

\begin{tabular}{lllllllll}
\hline
BLOCKS DEFINITION \tabularnewline
Block & Type & NMVs & Mode \tabularnewline
COMPUTE & Exogenous & 2 & Reflective \tabularnewline
REVIEW & Endogenous & 5 & Reflective \tabularnewline
EXAM0 & Endogenous & 2 & Formative \tabularnewline
EXAM1 & Endogenous & 2 & Formative \tabularnewline
EXAM2 & Endogenous & 2 & Formative \tabularnewline
EXAM3 & Endogenous & 2 & Formative \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]COMPUTE[/C][C]Exogenous[/C][C]2[/C][C]Reflective[/C][/ROW]
[ROW][C]REVIEW[/C][C]Endogenous[/C][C]5[/C][C]Reflective[/C][/ROW]
[ROW][C]EXAM0[/C][C]Endogenous[/C][C]2[/C][C]Formative[/C][/ROW]
[ROW][C]EXAM1[/C][C]Endogenous[/C][C]2[/C][C]Formative[/C][/ROW]
[ROW][C]EXAM2[/C][C]Endogenous[/C][C]2[/C][C]Formative[/C][/ROW]
[ROW][C]EXAM3[/C][C]Endogenous[/C][C]2[/C][C]Formative[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=2

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


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

BLOCKS DEFINITION
BlockTypeNMVsMode
COMPUTEExogenous2Reflective
REVIEWEndogenous5Reflective
EXAM0Endogenous2Formative
EXAM1Endogenous2Formative
EXAM2Endogenous2Formative
EXAM3Endogenous2Formative






BLOCKS UNIDIMENSIONALITY
BlockType.measureMVseig.1steig.2ndC.alphaDG.rho
COMPUTEReflective21.888302203842340.1116977961576590.9408474999762360.97127337580724
REVIEWReflective53.327797517198760.8597635189736940.8617545693076420.90494255461169
EXAM0Formative21.184666170239220.81533382976077700
EXAM1Formative21.187196415887310.81280358411269200
EXAM2Formative21.29746785747240.702532142527600
EXAM3Formative21.4570069624410.54299303755899900

\begin{tabular}{lllllllll}
\hline
BLOCKS UNIDIMENSIONALITY \tabularnewline
Block & Type.measure & MVs & eig.1st & eig.2nd & C.alpha & DG.rho \tabularnewline
COMPUTE & Reflective & 2 & 1.88830220384234 & 0.111697796157659 & 0.940847499976236 & 0.97127337580724 \tabularnewline
REVIEW & Reflective & 5 & 3.32779751719876 & 0.859763518973694 & 0.861754569307642 & 0.90494255461169 \tabularnewline
EXAM0 & Formative & 2 & 1.18466617023922 & 0.815333829760777 & 0 & 0 \tabularnewline
EXAM1 & Formative & 2 & 1.18719641588731 & 0.812803584112692 & 0 & 0 \tabularnewline
EXAM2 & Formative & 2 & 1.2974678574724 & 0.7025321425276 & 0 & 0 \tabularnewline
EXAM3 & Formative & 2 & 1.457006962441 & 0.542993037558999 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]COMPUTE[/C][C]Reflective[/C][C]2[/C][C]1.88830220384234[/C][C]0.111697796157659[/C][C]0.940847499976236[/C][C]0.97127337580724[/C][/ROW]
[ROW][C]REVIEW[/C][C]Reflective[/C][C]5[/C][C]3.32779751719876[/C][C]0.859763518973694[/C][C]0.861754569307642[/C][C]0.90494255461169[/C][/ROW]
[ROW][C]EXAM0[/C][C]Formative[/C][C]2[/C][C]1.18466617023922[/C][C]0.815333829760777[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]EXAM1[/C][C]Formative[/C][C]2[/C][C]1.18719641588731[/C][C]0.812803584112692[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]EXAM2[/C][C]Formative[/C][C]2[/C][C]1.2974678574724[/C][C]0.7025321425276[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]EXAM3[/C][C]Formative[/C][C]2[/C][C]1.457006962441[/C][C]0.542993037558999[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=3

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


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

BLOCKS UNIDIMENSIONALITY
BlockType.measureMVseig.1steig.2ndC.alphaDG.rho
COMPUTEReflective21.888302203842340.1116977961576590.9408474999762360.97127337580724
REVIEWReflective53.327797517198760.8597635189736940.8617545693076420.90494255461169
EXAM0Formative21.184666170239220.81533382976077700
EXAM1Formative21.187196415887310.81280358411269200
EXAM2Formative21.29746785747240.702532142527600
EXAM3Formative21.4570069624410.54299303755899900






OUTER MODEL
Blockweightsstd.loadscommunalredundan
COMPUTE
x$numcomp0.55380.97590.95240
x$numreceived0.47520.96710.93530
REVIEW
x$mrt0.13310.64440.41530.059
x$nfm0.33250.65010.42270.0601
x$afl0.24850.89440.80.1137
x$lpm0.26210.9370.87810.1248
x$lpc0.26730.8610.74130.1053
EXAM0
x$Q50.56030.69530.48340.0185
x$Q90.73130.83480.69680.0267
EXAM1
x$Q20.69870.81040.65670.0343
x$Q40.59650.72730.52890.0276
EXAM2
x$Q1_10.84230.94460.89220.0759
x$Q2_20.34390.59440.35340.0301
EXAM3
x$Q1_30.58770.85460.73030.2053
x$Q2_30.58390.85250.72670.2043

\begin{tabular}{lllllllll}
\hline
OUTER MODEL \tabularnewline
Block & weights & std.loads & communal & redundan \tabularnewline
COMPUTE \tabularnewline
x$numcomp & 0.5538 & 0.9759 & 0.9524 & 0 \tabularnewline
x$numreceived & 0.4752 & 0.9671 & 0.9353 & 0 \tabularnewline
REVIEW \tabularnewline
x$mrt & 0.1331 & 0.6444 & 0.4153 & 0.059 \tabularnewline
x$nfm & 0.3325 & 0.6501 & 0.4227 & 0.0601 \tabularnewline
x$afl & 0.2485 & 0.8944 & 0.8 & 0.1137 \tabularnewline
x$lpm & 0.2621 & 0.937 & 0.8781 & 0.1248 \tabularnewline
x$lpc & 0.2673 & 0.861 & 0.7413 & 0.1053 \tabularnewline
EXAM0 \tabularnewline
x$Q5 & 0.5603 & 0.6953 & 0.4834 & 0.0185 \tabularnewline
x$Q9 & 0.7313 & 0.8348 & 0.6968 & 0.0267 \tabularnewline
EXAM1 \tabularnewline
x$Q2 & 0.6987 & 0.8104 & 0.6567 & 0.0343 \tabularnewline
x$Q4 & 0.5965 & 0.7273 & 0.5289 & 0.0276 \tabularnewline
EXAM2 \tabularnewline
x$Q1_1 & 0.8423 & 0.9446 & 0.8922 & 0.0759 \tabularnewline
x$Q2_2 & 0.3439 & 0.5944 & 0.3534 & 0.0301 \tabularnewline
EXAM3 \tabularnewline
x$Q1_3 & 0.5877 & 0.8546 & 0.7303 & 0.2053 \tabularnewline
x$Q2_3 & 0.5839 & 0.8525 & 0.7267 & 0.2043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]COMPUTE[/C][/ROW]
[ROW][C]x$numcomp[/C][C]0.5538[/C][C]0.9759[/C][C]0.9524[/C][C]0[/C][/ROW]
[ROW][C]x$numreceived[/C][C]0.4752[/C][C]0.9671[/C][C]0.9353[/C][C]0[/C][/ROW]
[ROW][C]REVIEW[/C][/ROW]
[ROW][C]x$mrt[/C][C]0.1331[/C][C]0.6444[/C][C]0.4153[/C][C]0.059[/C][/ROW]
[ROW][C]x$nfm[/C][C]0.3325[/C][C]0.6501[/C][C]0.4227[/C][C]0.0601[/C][/ROW]
[ROW][C]x$afl[/C][C]0.2485[/C][C]0.8944[/C][C]0.8[/C][C]0.1137[/C][/ROW]
[ROW][C]x$lpm[/C][C]0.2621[/C][C]0.937[/C][C]0.8781[/C][C]0.1248[/C][/ROW]
[ROW][C]x$lpc[/C][C]0.2673[/C][C]0.861[/C][C]0.7413[/C][C]0.1053[/C][/ROW]
[ROW][C]EXAM0[/C][/ROW]
[ROW][C]x$Q5[/C][C]0.5603[/C][C]0.6953[/C][C]0.4834[/C][C]0.0185[/C][/ROW]
[ROW][C]x$Q9[/C][C]0.7313[/C][C]0.8348[/C][C]0.6968[/C][C]0.0267[/C][/ROW]
[ROW][C]EXAM1[/C][/ROW]
[ROW][C]x$Q2[/C][C]0.6987[/C][C]0.8104[/C][C]0.6567[/C][C]0.0343[/C][/ROW]
[ROW][C]x$Q4[/C][C]0.5965[/C][C]0.7273[/C][C]0.5289[/C][C]0.0276[/C][/ROW]
[ROW][C]EXAM2[/C][/ROW]
[ROW][C]x$Q1_1[/C][C]0.8423[/C][C]0.9446[/C][C]0.8922[/C][C]0.0759[/C][/ROW]
[ROW][C]x$Q2_2[/C][C]0.3439[/C][C]0.5944[/C][C]0.3534[/C][C]0.0301[/C][/ROW]
[ROW][C]EXAM3[/C][/ROW]
[ROW][C]x$Q1_3[/C][C]0.5877[/C][C]0.8546[/C][C]0.7303[/C][C]0.2053[/C][/ROW]
[ROW][C]x$Q2_3[/C][C]0.5839[/C][C]0.8525[/C][C]0.7267[/C][C]0.2043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=4

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


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

OUTER MODEL
Blockweightsstd.loadscommunalredundan
COMPUTE
x$numcomp0.55380.97590.95240
x$numreceived0.47520.96710.93530
REVIEW
x$mrt0.13310.64440.41530.059
x$nfm0.33250.65010.42270.0601
x$afl0.24850.89440.80.1137
x$lpm0.26210.9370.87810.1248
x$lpc0.26730.8610.74130.1053
EXAM0
x$Q50.56030.69530.48340.0185
x$Q90.73130.83480.69680.0267
EXAM1
x$Q20.69870.81040.65670.0343
x$Q40.59650.72730.52890.0276
EXAM2
x$Q1_10.84230.94460.89220.0759
x$Q2_20.34390.59440.35340.0301
EXAM3
x$Q1_30.58770.85460.73030.2053
x$Q2_30.58390.85250.72670.2043






CORRELATIONS BETWEEN MVs AND LVs
BlockCOMPUTEREVIEWEXAM0EXAM1EXAM2EXAM3
COMPUTE
x$numcomp0.97590.39210.14350.01950.32310.3105
x$numreceived0.96710.33640.1227-0.02570.29240.3123
REVIEW
x$mrt0.21340.64440.11780.0810.06040.0976
x$nfm0.39290.65010.23330.19370.21640.3864
x$afl0.25920.89440.11180.10020.26230.3292
x$lpm0.28310.9370.13410.11510.24520.3434
x$lpc0.29670.8610.14840.17260.23240.2929
EXAM0
x$Q50.10340.1080.69530.1510.15810.0477
x$Q90.10910.18510.83480.12640.0620.1411
EXAM1
x$Q2-0.00320.14530.10740.8104-0.07930.0082
x$Q40.00140.12550.1710.7273-0.00230.1209
EXAM2
x$Q1_10.2610.26410.0866-0.09720.94460.374
x$Q2_20.28510.14290.17730.0730.59440.3936
EXAM3
x$Q1_30.29180.30080.0766-0.00830.41910.8546
x$Q2_30.2550.36790.14540.14170.34940.8525

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN MVs AND LVs \tabularnewline
Block & COMPUTE & REVIEW & EXAM0 & EXAM1 & EXAM2 & EXAM3 \tabularnewline
COMPUTE \tabularnewline
x$numcomp & 0.9759 & 0.3921 & 0.1435 & 0.0195 & 0.3231 & 0.3105 \tabularnewline
x$numreceived & 0.9671 & 0.3364 & 0.1227 & -0.0257 & 0.2924 & 0.3123 \tabularnewline
REVIEW \tabularnewline
x$mrt & 0.2134 & 0.6444 & 0.1178 & 0.081 & 0.0604 & 0.0976 \tabularnewline
x$nfm & 0.3929 & 0.6501 & 0.2333 & 0.1937 & 0.2164 & 0.3864 \tabularnewline
x$afl & 0.2592 & 0.8944 & 0.1118 & 0.1002 & 0.2623 & 0.3292 \tabularnewline
x$lpm & 0.2831 & 0.937 & 0.1341 & 0.1151 & 0.2452 & 0.3434 \tabularnewline
x$lpc & 0.2967 & 0.861 & 0.1484 & 0.1726 & 0.2324 & 0.2929 \tabularnewline
EXAM0 \tabularnewline
x$Q5 & 0.1034 & 0.108 & 0.6953 & 0.151 & 0.1581 & 0.0477 \tabularnewline
x$Q9 & 0.1091 & 0.1851 & 0.8348 & 0.1264 & 0.062 & 0.1411 \tabularnewline
EXAM1 \tabularnewline
x$Q2 & -0.0032 & 0.1453 & 0.1074 & 0.8104 & -0.0793 & 0.0082 \tabularnewline
x$Q4 & 0.0014 & 0.1255 & 0.171 & 0.7273 & -0.0023 & 0.1209 \tabularnewline
EXAM2 \tabularnewline
x$Q1_1 & 0.261 & 0.2641 & 0.0866 & -0.0972 & 0.9446 & 0.374 \tabularnewline
x$Q2_2 & 0.2851 & 0.1429 & 0.1773 & 0.073 & 0.5944 & 0.3936 \tabularnewline
EXAM3 \tabularnewline
x$Q1_3 & 0.2918 & 0.3008 & 0.0766 & -0.0083 & 0.4191 & 0.8546 \tabularnewline
x$Q2_3 & 0.255 & 0.3679 & 0.1454 & 0.1417 & 0.3494 & 0.8525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&T=5

[TABLE]
[ROW][C]CORRELATIONS BETWEEN MVs AND LVs[/C][/ROW]
[ROW][C]Block[/C][C]COMPUTE[/C][C]REVIEW[/C][C]EXAM0[/C][C]EXAM1[/C][C]EXAM2[/C][C]EXAM3[/C][/ROW]
[ROW][C]COMPUTE[/C][/ROW]
[ROW][C]x$numcomp[/C][C]0.9759[/C][C]0.3921[/C][C]0.1435[/C][C]0.0195[/C][C]0.3231[/C][C]0.3105[/C][/ROW]
[ROW][C]x$numreceived[/C][C]0.9671[/C][C]0.3364[/C][C]0.1227[/C][C]-0.0257[/C][C]0.2924[/C][C]0.3123[/C][/ROW]
[ROW][C]REVIEW[/C][/ROW]
[ROW][C]x$mrt[/C][C]0.2134[/C][C]0.6444[/C][C]0.1178[/C][C]0.081[/C][C]0.0604[/C][C]0.0976[/C][/ROW]
[ROW][C]x$nfm[/C][C]0.3929[/C][C]0.6501[/C][C]0.2333[/C][C]0.1937[/C][C]0.2164[/C][C]0.3864[/C][/ROW]
[ROW][C]x$afl[/C][C]0.2592[/C][C]0.8944[/C][C]0.1118[/C][C]0.1002[/C][C]0.2623[/C][C]0.3292[/C][/ROW]
[ROW][C]x$lpm[/C][C]0.2831[/C][C]0.937[/C][C]0.1341[/C][C]0.1151[/C][C]0.2452[/C][C]0.3434[/C][/ROW]
[ROW][C]x$lpc[/C][C]0.2967[/C][C]0.861[/C][C]0.1484[/C][C]0.1726[/C][C]0.2324[/C][C]0.2929[/C][/ROW]
[ROW][C]EXAM0[/C][/ROW]
[ROW][C]x$Q5[/C][C]0.1034[/C][C]0.108[/C][C]0.6953[/C][C]0.151[/C][C]0.1581[/C][C]0.0477[/C][/ROW]
[ROW][C]x$Q9[/C][C]0.1091[/C][C]0.1851[/C][C]0.8348[/C][C]0.1264[/C][C]0.062[/C][C]0.1411[/C][/ROW]
[ROW][C]EXAM1[/C][/ROW]
[ROW][C]x$Q2[/C][C]-0.0032[/C][C]0.1453[/C][C]0.1074[/C][C]0.8104[/C][C]-0.0793[/C][C]0.0082[/C][/ROW]
[ROW][C]x$Q4[/C][C]0.0014[/C][C]0.1255[/C][C]0.171[/C][C]0.7273[/C][C]-0.0023[/C][C]0.1209[/C][/ROW]
[ROW][C]EXAM2[/C][/ROW]
[ROW][C]x$Q1_1[/C][C]0.261[/C][C]0.2641[/C][C]0.0866[/C][C]-0.0972[/C][C]0.9446[/C][C]0.374[/C][/ROW]
[ROW][C]x$Q2_2[/C][C]0.2851[/C][C]0.1429[/C][C]0.1773[/C][C]0.073[/C][C]0.5944[/C][C]0.3936[/C][/ROW]
[ROW][C]EXAM3[/C][/ROW]
[ROW][C]x$Q1_3[/C][C]0.2918[/C][C]0.3008[/C][C]0.0766[/C][C]-0.0083[/C][C]0.4191[/C][C]0.8546[/C][/ROW]
[ROW][C]x$Q2_3[/C][C]0.255[/C][C]0.3679[/C][C]0.1454[/C][C]0.1417[/C][C]0.3494[/C][C]0.8525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=5

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


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

CORRELATIONS BETWEEN MVs AND LVs
BlockCOMPUTEREVIEWEXAM0EXAM1EXAM2EXAM3
COMPUTE
x$numcomp0.97590.39210.14350.01950.32310.3105
x$numreceived0.96710.33640.1227-0.02570.29240.3123
REVIEW
x$mrt0.21340.64440.11780.0810.06040.0976
x$nfm0.39290.65010.23330.19370.21640.3864
x$afl0.25920.89440.11180.10020.26230.3292
x$lpm0.28310.9370.13410.11510.24520.3434
x$lpc0.29670.8610.14840.17260.23240.2929
EXAM0
x$Q50.10340.1080.69530.1510.15810.0477
x$Q90.10910.18510.83480.12640.0620.1411
EXAM1
x$Q2-0.00320.14530.10740.8104-0.07930.0082
x$Q40.00140.12550.1710.7273-0.00230.1209
EXAM2
x$Q1_10.2610.26410.0866-0.09720.94460.374
x$Q2_20.28510.14290.17730.0730.59440.3936
EXAM3
x$Q1_30.29180.30080.0766-0.00830.41910.8546
x$Q2_30.2550.36790.14540.14170.34940.8525






INNER MODEL
BlockConceptValue
S2
1R20.1421
2Intercept0
3path_S10.377
S3
1R20.0384
2Intercept0
3path_S20.1959
S4
1R20.0522
2Intercept0
3path_S20.1474
4path_S30.1482
S5
1R20.0851
2Intercept0
3path_S20.2906
4path_S4-0.1081
S6
1R20.2811
2Intercept0
3path_S20.2907
4path_S50.3714

\begin{tabular}{lllllllll}
\hline
INNER MODEL \tabularnewline
Block & Concept & Value \tabularnewline
S2 \tabularnewline
1 & R2 & 0.1421 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S1 & 0.377 \tabularnewline
S3 \tabularnewline
1 & R2 & 0.0384 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.1959 \tabularnewline
S4 \tabularnewline
1 & R2 & 0.0522 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.1474 \tabularnewline
4 & path_S3 & 0.1482 \tabularnewline
S5 \tabularnewline
1 & R2 & 0.0851 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.2906 \tabularnewline
4 & path_S4 & -0.1081 \tabularnewline
S6 \tabularnewline
1 & R2 & 0.2811 \tabularnewline
2 & Intercept & 0 \tabularnewline
3 & path_S2 & 0.2907 \tabularnewline
4 & path_S5 & 0.3714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]0.1421[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S1[/C][C]0.377[/C][/ROW]
[ROW][C]S3[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.0384[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.1959[/C][/ROW]
[ROW][C]S4[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.0522[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.1474[/C][/ROW]
[ROW][C]4[/C][C]path_S3[/C][C]0.1482[/C][/ROW]
[ROW][C]S5[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.0851[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.2906[/C][/ROW]
[ROW][C]4[/C][C]path_S4[/C][C]-0.1081[/C][/ROW]
[ROW][C]S6[/C][/ROW]
[ROW][C]1[/C][C]R2[/C][C]0.2811[/C][/ROW]
[ROW][C]2[/C][C]Intercept[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]path_S2[/C][C]0.2907[/C][/ROW]
[ROW][C]4[/C][C]path_S5[/C][C]0.3714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=6

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


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

INNER MODEL
BlockConceptValue
S2
1R20.1421
2Intercept0
3path_S10.377
S3
1R20.0384
2Intercept0
3path_S20.1959
S4
1R20.0522
2Intercept0
3path_S20.1474
4path_S30.1482
S5
1R20.0851
2Intercept0
3path_S20.2906
4path_S4-0.1081
S6
1R20.2811
2Intercept0
3path_S20.2907
4path_S50.3714






CORRELATIONS BETWEEN LVs
COMPUTEREVIEWEXAM0EXAM1EXAM2EXAM3
COMPUTE10.3770.1377-0.00140.31790.3203
REVIEW0.37710.19590.17640.27160.3916
EXAM00.13770.195910.1770.13390.1299
EXAM1-0.00140.17640.1771-0.05680.0779
EXAM20.31790.27160.1339-0.056810.4504
EXAM30.32030.39160.12990.07790.45041

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN LVs \tabularnewline
 & COMPUTE & REVIEW & EXAM0 & EXAM1 & EXAM2 & EXAM3 \tabularnewline
COMPUTE & 1 & 0.377 & 0.1377 & -0.0014 & 0.3179 & 0.3203 \tabularnewline
REVIEW & 0.377 & 1 & 0.1959 & 0.1764 & 0.2716 & 0.3916 \tabularnewline
EXAM0 & 0.1377 & 0.1959 & 1 & 0.177 & 0.1339 & 0.1299 \tabularnewline
EXAM1 & -0.0014 & 0.1764 & 0.177 & 1 & -0.0568 & 0.0779 \tabularnewline
EXAM2 & 0.3179 & 0.2716 & 0.1339 & -0.0568 & 1 & 0.4504 \tabularnewline
EXAM3 & 0.3203 & 0.3916 & 0.1299 & 0.0779 & 0.4504 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&T=7

[TABLE]
[ROW][C]CORRELATIONS BETWEEN LVs[/C][/ROW]
[ROW][C][/C][C]COMPUTE[/C][C]REVIEW[/C][C]EXAM0[/C][C]EXAM1[/C][C]EXAM2[/C][C]EXAM3[/C][/ROW]
[ROW][C]COMPUTE[/C][C]1[/C][C]0.377[/C][C]0.1377[/C][C]-0.0014[/C][C]0.3179[/C][C]0.3203[/C][/ROW]
[ROW][C]REVIEW[/C][C]0.377[/C][C]1[/C][C]0.1959[/C][C]0.1764[/C][C]0.2716[/C][C]0.3916[/C][/ROW]
[ROW][C]EXAM0[/C][C]0.1377[/C][C]0.1959[/C][C]1[/C][C]0.177[/C][C]0.1339[/C][C]0.1299[/C][/ROW]
[ROW][C]EXAM1[/C][C]-0.0014[/C][C]0.1764[/C][C]0.177[/C][C]1[/C][C]-0.0568[/C][C]0.0779[/C][/ROW]
[ROW][C]EXAM2[/C][C]0.3179[/C][C]0.2716[/C][C]0.1339[/C][C]-0.0568[/C][C]1[/C][C]0.4504[/C][/ROW]
[ROW][C]EXAM3[/C][C]0.3203[/C][C]0.3916[/C][C]0.1299[/C][C]0.0779[/C][C]0.4504[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=7

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


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

CORRELATIONS BETWEEN LVs
COMPUTEREVIEWEXAM0EXAM1EXAM2EXAM3
COMPUTE10.3770.1377-0.00140.31790.3203
REVIEW0.37710.19590.17640.27160.3916
EXAM00.13770.195910.1770.13390.1299
EXAM1-0.00140.17640.1771-0.05680.0779
EXAM20.31790.27160.1339-0.056810.4504
EXAM30.32030.39160.12990.07790.45041






SUMMARY INNER MODEL
LV.TypeMeasureMVsR.squareAv.CommuAv.RedunAVE
COMPUTEExogenRflct200.943800.944
REVIEWEndogenRflct50.14210.65150.09260.651
EXAM0EndogenFrmtv20.03840.59010.02260
EXAM1EndogenFrmtv20.05220.59280.0310
EXAM2EndogenFrmtv20.08510.62280.0530
EXAM3EndogenFrmtv20.28110.72850.20480

\begin{tabular}{lllllllll}
\hline
SUMMARY INNER MODEL \tabularnewline
 & LV.Type & Measure & MVs & R.square & Av.Commu & Av.Redun & AVE \tabularnewline
COMPUTE & Exogen & Rflct & 2 & 0 & 0.9438 & 0 & 0.944 \tabularnewline
REVIEW & Endogen & Rflct & 5 & 0.1421 & 0.6515 & 0.0926 & 0.651 \tabularnewline
EXAM0 & Endogen & Frmtv & 2 & 0.0384 & 0.5901 & 0.0226 & 0 \tabularnewline
EXAM1 & Endogen & Frmtv & 2 & 0.0522 & 0.5928 & 0.031 & 0 \tabularnewline
EXAM2 & Endogen & Frmtv & 2 & 0.0851 & 0.6228 & 0.053 & 0 \tabularnewline
EXAM3 & Endogen & Frmtv & 2 & 0.2811 & 0.7285 & 0.2048 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]COMPUTE[/C][C]Exogen[/C][C]Rflct[/C][C]2[/C][C]0[/C][C]0.9438[/C][C]0[/C][C]0.944[/C][/ROW]
[ROW][C]REVIEW[/C][C]Endogen[/C][C]Rflct[/C][C]5[/C][C]0.1421[/C][C]0.6515[/C][C]0.0926[/C][C]0.651[/C][/ROW]
[ROW][C]EXAM0[/C][C]Endogen[/C][C]Frmtv[/C][C]2[/C][C]0.0384[/C][C]0.5901[/C][C]0.0226[/C][C]0[/C][/ROW]
[ROW][C]EXAM1[/C][C]Endogen[/C][C]Frmtv[/C][C]2[/C][C]0.0522[/C][C]0.5928[/C][C]0.031[/C][C]0[/C][/ROW]
[ROW][C]EXAM2[/C][C]Endogen[/C][C]Frmtv[/C][C]2[/C][C]0.0851[/C][C]0.6228[/C][C]0.053[/C][C]0[/C][/ROW]
[ROW][C]EXAM3[/C][C]Endogen[/C][C]Frmtv[/C][C]2[/C][C]0.2811[/C][C]0.7285[/C][C]0.2048[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=8

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


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

SUMMARY INNER MODEL
LV.TypeMeasureMVsR.squareAv.CommuAv.RedunAVE
COMPUTEExogenRflct200.943800.944
REVIEWEndogenRflct50.14210.65150.09260.651
EXAM0EndogenFrmtv20.03840.59010.02260
EXAM1EndogenFrmtv20.05220.59280.0310
EXAM2EndogenFrmtv20.08510.62280.0530
EXAM3EndogenFrmtv20.28110.72850.20480






GOODNESS-OF-FIT
GoFValue
Absolute0.285579477892365
Relative0.818028650850482
Outer.mod0.994436145545231
Inner.mod0.82260550817164

\begin{tabular}{lllllllll}
\hline
GOODNESS-OF-FIT \tabularnewline
GoF & Value \tabularnewline
Absolute & 0.285579477892365 \tabularnewline
Relative & 0.818028650850482 \tabularnewline
Outer.mod & 0.994436145545231 \tabularnewline
Inner.mod & 0.82260550817164 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&T=9

[TABLE]
[ROW][C]GOODNESS-OF-FIT[/C][/ROW]
[ROW][C]GoF[/C][C]Value[/C][/ROW]
[ROW][C]Absolute[/C][C]0.285579477892365[/C][/ROW]
[ROW][C]Relative[/C][C]0.818028650850482[/C][/ROW]
[ROW][C]Outer.mod[/C][C]0.994436145545231[/C][/ROW]
[ROW][C]Inner.mod[/C][C]0.82260550817164[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=9

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


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

GOODNESS-OF-FIT
GoFValue
Absolute0.285579477892365
Relative0.818028650850482
Outer.mod0.994436145545231
Inner.mod0.82260550817164






TOTAL EFFECTS
relationshipsdir.effectind.effecttot.effect
S1->S20.37697588155530200.376975881555302
S1->S300.07383224460288060.0738322446028806
S1->S400.06649054212154650.0664905421215465
S1->S500.1023785337480080.102378533748008
S1->S600.1476245457448980.147624545744898
S2->S30.19585402731407800.195854027314078
S2->S40.1473554942755650.02902327522879730.176378769504363
S2->S50.290636626547546-0.01905815493386860.271578471613678
S2->S60.2907351374422490.1008669595827350.391602097024984
S3->S40.14818829935140800.148188299351408
S3->S50-0.0160121060848867-0.0160121060848867
S3->S60-0.00594705628801173-0.00594705628801173
S4->S5-0.1080524316357540-0.108052431635754
S4->S60-0.0401317534113076-0.0401317534113076
S5->S60.37140999794055600.371409997940556

\begin{tabular}{lllllllll}
\hline
TOTAL EFFECTS \tabularnewline
relationships & dir.effect & ind.effect & tot.effect \tabularnewline
S1->S2 & 0.376975881555302 & 0 & 0.376975881555302 \tabularnewline
S1->S3 & 0 & 0.0738322446028806 & 0.0738322446028806 \tabularnewline
S1->S4 & 0 & 0.0664905421215465 & 0.0664905421215465 \tabularnewline
S1->S5 & 0 & 0.102378533748008 & 0.102378533748008 \tabularnewline
S1->S6 & 0 & 0.147624545744898 & 0.147624545744898 \tabularnewline
S2->S3 & 0.195854027314078 & 0 & 0.195854027314078 \tabularnewline
S2->S4 & 0.147355494275565 & 0.0290232752287973 & 0.176378769504363 \tabularnewline
S2->S5 & 0.290636626547546 & -0.0190581549338686 & 0.271578471613678 \tabularnewline
S2->S6 & 0.290735137442249 & 0.100866959582735 & 0.391602097024984 \tabularnewline
S3->S4 & 0.148188299351408 & 0 & 0.148188299351408 \tabularnewline
S3->S5 & 0 & -0.0160121060848867 & -0.0160121060848867 \tabularnewline
S3->S6 & 0 & -0.00594705628801173 & -0.00594705628801173 \tabularnewline
S4->S5 & -0.108052431635754 & 0 & -0.108052431635754 \tabularnewline
S4->S6 & 0 & -0.0401317534113076 & -0.0401317534113076 \tabularnewline
S5->S6 & 0.371409997940556 & 0 & 0.371409997940556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]0.376975881555302[/C][C]0[/C][C]0.376975881555302[/C][/ROW]
[ROW][C]S1->S3[/C][C]0[/C][C]0.0738322446028806[/C][C]0.0738322446028806[/C][/ROW]
[ROW][C]S1->S4[/C][C]0[/C][C]0.0664905421215465[/C][C]0.0664905421215465[/C][/ROW]
[ROW][C]S1->S5[/C][C]0[/C][C]0.102378533748008[/C][C]0.102378533748008[/C][/ROW]
[ROW][C]S1->S6[/C][C]0[/C][C]0.147624545744898[/C][C]0.147624545744898[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.195854027314078[/C][C]0[/C][C]0.195854027314078[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.147355494275565[/C][C]0.0290232752287973[/C][C]0.176378769504363[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.290636626547546[/C][C]-0.0190581549338686[/C][C]0.271578471613678[/C][/ROW]
[ROW][C]S2->S6[/C][C]0.290735137442249[/C][C]0.100866959582735[/C][C]0.391602097024984[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.148188299351408[/C][C]0[/C][C]0.148188299351408[/C][/ROW]
[ROW][C]S3->S5[/C][C]0[/C][C]-0.0160121060848867[/C][C]-0.0160121060848867[/C][/ROW]
[ROW][C]S3->S6[/C][C]0[/C][C]-0.00594705628801173[/C][C]-0.00594705628801173[/C][/ROW]
[ROW][C]S4->S5[/C][C]-0.108052431635754[/C][C]0[/C][C]-0.108052431635754[/C][/ROW]
[ROW][C]S4->S6[/C][C]0[/C][C]-0.0401317534113076[/C][C]-0.0401317534113076[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.371409997940556[/C][C]0[/C][C]0.371409997940556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=10

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=10

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


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

TOTAL EFFECTS
relationshipsdir.effectind.effecttot.effect
S1->S20.37697588155530200.376975881555302
S1->S300.07383224460288060.0738322446028806
S1->S400.06649054212154650.0664905421215465
S1->S500.1023785337480080.102378533748008
S1->S600.1476245457448980.147624545744898
S2->S30.19585402731407800.195854027314078
S2->S40.1473554942755650.02902327522879730.176378769504363
S2->S50.290636626547546-0.01905815493386860.271578471613678
S2->S60.2907351374422490.1008669595827350.391602097024984
S3->S40.14818829935140800.148188299351408
S3->S50-0.0160121060848867-0.0160121060848867
S3->S60-0.00594705628801173-0.00594705628801173
S4->S5-0.1080524316357540-0.108052431635754
S4->S60-0.0401317534113076-0.0401317534113076
S5->S60.37140999794055600.371409997940556






BOOTSTRAP VALIDATION - WEIGHTS
OriginalMean.BootStd.Errorperc.05perc.95
x$numcomp0.5537536178266430.5554620307251140.02730019649927080.5204985669708910.594266765009948
x$numreceived0.4752204730287470.4735110901945050.02680021997701490.4361566266769040.505196008904125
x$mrt0.1330907372424830.1294359840079250.05408247128873080.02215816979131460.202673975052174
x$nfm0.33252613682620.3186287688991110.04621583822224630.2438636449853720.398935948998172
x$afl0.2485136536739050.255246020234790.02528508120705950.2197732971972640.297083556305511
x$lpm0.2621178939292340.2673844694672920.01803382483148740.2452680645335820.293771981157163
x$lpc0.2673197486321620.2642376990533530.03899690720906750.2034584770090330.324773274644734
x$Q50.560256610620340.450670104628580.430518170214844-0.6807793660763530.98006820734314
x$Q90.7312949667198720.6709346533377880.2989090431619080.1051620287112680.98507454527349
x$Q20.6986922679885110.5865553288106070.316512889058067-0.06742624222984320.9696495470284
x$Q40.5964870665918810.5849751649623540.3545032963076560.02873059037555251.00365383649942
x$Q1_10.8422690902850160.7279074568327020.2134628745239150.2854026930133861.00057252966934
x$Q2_20.3438904681691230.4489924287100730.252946438891498-0.001903162553553880.868596827525318
x$Q1_30.5877055444125290.575585077912920.1829270530578140.2966776693908450.863828645332055
x$Q2_30.5839051649981680.5739103616688060.1880511393168790.2633217083443950.829320250519676

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - WEIGHTS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
x$numcomp & 0.553753617826643 & 0.555462030725114 & 0.0273001964992708 & 0.520498566970891 & 0.594266765009948 \tabularnewline
x$numreceived & 0.475220473028747 & 0.473511090194505 & 0.0268002199770149 & 0.436156626676904 & 0.505196008904125 \tabularnewline
x$mrt & 0.133090737242483 & 0.129435984007925 & 0.0540824712887308 & 0.0221581697913146 & 0.202673975052174 \tabularnewline
x$nfm & 0.3325261368262 & 0.318628768899111 & 0.0462158382222463 & 0.243863644985372 & 0.398935948998172 \tabularnewline
x$afl & 0.248513653673905 & 0.25524602023479 & 0.0252850812070595 & 0.219773297197264 & 0.297083556305511 \tabularnewline
x$lpm & 0.262117893929234 & 0.267384469467292 & 0.0180338248314874 & 0.245268064533582 & 0.293771981157163 \tabularnewline
x$lpc & 0.267319748632162 & 0.264237699053353 & 0.0389969072090675 & 0.203458477009033 & 0.324773274644734 \tabularnewline
x$Q5 & 0.56025661062034 & 0.45067010462858 & 0.430518170214844 & -0.680779366076353 & 0.98006820734314 \tabularnewline
x$Q9 & 0.731294966719872 & 0.670934653337788 & 0.298909043161908 & 0.105162028711268 & 0.98507454527349 \tabularnewline
x$Q2 & 0.698692267988511 & 0.586555328810607 & 0.316512889058067 & -0.0674262422298432 & 0.9696495470284 \tabularnewline
x$Q4 & 0.596487066591881 & 0.584975164962354 & 0.354503296307656 & 0.0287305903755525 & 1.00365383649942 \tabularnewline
x$Q1_1 & 0.842269090285016 & 0.727907456832702 & 0.213462874523915 & 0.285402693013386 & 1.00057252966934 \tabularnewline
x$Q2_2 & 0.343890468169123 & 0.448992428710073 & 0.252946438891498 & -0.00190316255355388 & 0.868596827525318 \tabularnewline
x$Q1_3 & 0.587705544412529 & 0.57558507791292 & 0.182927053057814 & 0.296677669390845 & 0.863828645332055 \tabularnewline
x$Q2_3 & 0.583905164998168 & 0.573910361668806 & 0.188051139316879 & 0.263321708344395 & 0.829320250519676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]x$numcomp[/C][C]0.553753617826643[/C][C]0.555462030725114[/C][C]0.0273001964992708[/C][C]0.520498566970891[/C][C]0.594266765009948[/C][/ROW]
[ROW][C]x$numreceived[/C][C]0.475220473028747[/C][C]0.473511090194505[/C][C]0.0268002199770149[/C][C]0.436156626676904[/C][C]0.505196008904125[/C][/ROW]
[ROW][C]x$mrt[/C][C]0.133090737242483[/C][C]0.129435984007925[/C][C]0.0540824712887308[/C][C]0.0221581697913146[/C][C]0.202673975052174[/C][/ROW]
[ROW][C]x$nfm[/C][C]0.3325261368262[/C][C]0.318628768899111[/C][C]0.0462158382222463[/C][C]0.243863644985372[/C][C]0.398935948998172[/C][/ROW]
[ROW][C]x$afl[/C][C]0.248513653673905[/C][C]0.25524602023479[/C][C]0.0252850812070595[/C][C]0.219773297197264[/C][C]0.297083556305511[/C][/ROW]
[ROW][C]x$lpm[/C][C]0.262117893929234[/C][C]0.267384469467292[/C][C]0.0180338248314874[/C][C]0.245268064533582[/C][C]0.293771981157163[/C][/ROW]
[ROW][C]x$lpc[/C][C]0.267319748632162[/C][C]0.264237699053353[/C][C]0.0389969072090675[/C][C]0.203458477009033[/C][C]0.324773274644734[/C][/ROW]
[ROW][C]x$Q5[/C][C]0.56025661062034[/C][C]0.45067010462858[/C][C]0.430518170214844[/C][C]-0.680779366076353[/C][C]0.98006820734314[/C][/ROW]
[ROW][C]x$Q9[/C][C]0.731294966719872[/C][C]0.670934653337788[/C][C]0.298909043161908[/C][C]0.105162028711268[/C][C]0.98507454527349[/C][/ROW]
[ROW][C]x$Q2[/C][C]0.698692267988511[/C][C]0.586555328810607[/C][C]0.316512889058067[/C][C]-0.0674262422298432[/C][C]0.9696495470284[/C][/ROW]
[ROW][C]x$Q4[/C][C]0.596487066591881[/C][C]0.584975164962354[/C][C]0.354503296307656[/C][C]0.0287305903755525[/C][C]1.00365383649942[/C][/ROW]
[ROW][C]x$Q1_1[/C][C]0.842269090285016[/C][C]0.727907456832702[/C][C]0.213462874523915[/C][C]0.285402693013386[/C][C]1.00057252966934[/C][/ROW]
[ROW][C]x$Q2_2[/C][C]0.343890468169123[/C][C]0.448992428710073[/C][C]0.252946438891498[/C][C]-0.00190316255355388[/C][C]0.868596827525318[/C][/ROW]
[ROW][C]x$Q1_3[/C][C]0.587705544412529[/C][C]0.57558507791292[/C][C]0.182927053057814[/C][C]0.296677669390845[/C][C]0.863828645332055[/C][/ROW]
[ROW][C]x$Q2_3[/C][C]0.583905164998168[/C][C]0.573910361668806[/C][C]0.188051139316879[/C][C]0.263321708344395[/C][C]0.829320250519676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=11

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=11

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


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

BOOTSTRAP VALIDATION - WEIGHTS
OriginalMean.BootStd.Errorperc.05perc.95
x$numcomp0.5537536178266430.5554620307251140.02730019649927080.5204985669708910.594266765009948
x$numreceived0.4752204730287470.4735110901945050.02680021997701490.4361566266769040.505196008904125
x$mrt0.1330907372424830.1294359840079250.05408247128873080.02215816979131460.202673975052174
x$nfm0.33252613682620.3186287688991110.04621583822224630.2438636449853720.398935948998172
x$afl0.2485136536739050.255246020234790.02528508120705950.2197732971972640.297083556305511
x$lpm0.2621178939292340.2673844694672920.01803382483148740.2452680645335820.293771981157163
x$lpc0.2673197486321620.2642376990533530.03899690720906750.2034584770090330.324773274644734
x$Q50.560256610620340.450670104628580.430518170214844-0.6807793660763530.98006820734314
x$Q90.7312949667198720.6709346533377880.2989090431619080.1051620287112680.98507454527349
x$Q20.6986922679885110.5865553288106070.316512889058067-0.06742624222984320.9696495470284
x$Q40.5964870665918810.5849751649623540.3545032963076560.02873059037555251.00365383649942
x$Q1_10.8422690902850160.7279074568327020.2134628745239150.2854026930133861.00057252966934
x$Q2_20.3438904681691230.4489924287100730.252946438891498-0.001903162553553880.868596827525318
x$Q1_30.5877055444125290.575585077912920.1829270530578140.2966776693908450.863828645332055
x$Q2_30.5839051649981680.5739103616688060.1880511393168790.2633217083443950.829320250519676






BOOTSTRAP VALIDATION - LOADINGS
OriginalMean.BootStd.Errorperc.05perc.95
x$numcomp0.975893011329080.976000867230820.004143815410017580.9704865213227560.983262829786502
x$numreceived0.9671210321298240.966647176246580.006654394722595580.956434314381810.976466613670537
x$mrt0.6444189287266940.6523344719548280.08392927035898780.4988411692484740.78972315186129
x$nfm0.650120970022150.639928241296660.06066699224449020.5384987631522360.734561402546278
x$afl0.8944220453666670.8954356094731540.02260318126835940.8605996624741770.929704612193706
x$lpm0.9370458402389330.9399507027790520.01307102153869410.9207377712227430.959937350552744
x$lpc0.860987725935480.8548108694143690.04325485137460620.7746666686161050.91197539408801
x$Q50.6953020514397190.5711122195267080.396791614694739-0.4470576646720510.985552275161499
x$Q90.8347554093543380.7433356809338930.2731595762426990.1778864641539350.996250626485454
x$Q20.8103525089776450.7009210543273930.256237618230370.1561672516921730.990599245844105
x$Q40.7272797549675050.690900994366370.3130530693104890.2271968024131210.995035237505536
x$Q1_10.9445654510564650.8599762010299190.1430793729364560.6000346557194050.999135608691803
x$Q2_20.5944384498714340.6626434199857250.1995043911984690.2783099115108520.962811846323056
x$Q1_30.8545542702219530.8373382670808580.1052944274522940.6572132186536160.974699320155514
x$Q2_30.8524906906598730.83931179841570.1037250866825670.6338944947294320.966762104638334

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - LOADINGS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
x$numcomp & 0.97589301132908 & 0.97600086723082 & 0.00414381541001758 & 0.970486521322756 & 0.983262829786502 \tabularnewline
x$numreceived & 0.967121032129824 & 0.96664717624658 & 0.00665439472259558 & 0.95643431438181 & 0.976466613670537 \tabularnewline
x$mrt & 0.644418928726694 & 0.652334471954828 & 0.0839292703589878 & 0.498841169248474 & 0.78972315186129 \tabularnewline
x$nfm & 0.65012097002215 & 0.63992824129666 & 0.0606669922444902 & 0.538498763152236 & 0.734561402546278 \tabularnewline
x$afl & 0.894422045366667 & 0.895435609473154 & 0.0226031812683594 & 0.860599662474177 & 0.929704612193706 \tabularnewline
x$lpm & 0.937045840238933 & 0.939950702779052 & 0.0130710215386941 & 0.920737771222743 & 0.959937350552744 \tabularnewline
x$lpc & 0.86098772593548 & 0.854810869414369 & 0.0432548513746062 & 0.774666668616105 & 0.91197539408801 \tabularnewline
x$Q5 & 0.695302051439719 & 0.571112219526708 & 0.396791614694739 & -0.447057664672051 & 0.985552275161499 \tabularnewline
x$Q9 & 0.834755409354338 & 0.743335680933893 & 0.273159576242699 & 0.177886464153935 & 0.996250626485454 \tabularnewline
x$Q2 & 0.810352508977645 & 0.700921054327393 & 0.25623761823037 & 0.156167251692173 & 0.990599245844105 \tabularnewline
x$Q4 & 0.727279754967505 & 0.69090099436637 & 0.313053069310489 & 0.227196802413121 & 0.995035237505536 \tabularnewline
x$Q1_1 & 0.944565451056465 & 0.859976201029919 & 0.143079372936456 & 0.600034655719405 & 0.999135608691803 \tabularnewline
x$Q2_2 & 0.594438449871434 & 0.662643419985725 & 0.199504391198469 & 0.278309911510852 & 0.962811846323056 \tabularnewline
x$Q1_3 & 0.854554270221953 & 0.837338267080858 & 0.105294427452294 & 0.657213218653616 & 0.974699320155514 \tabularnewline
x$Q2_3 & 0.852490690659873 & 0.8393117984157 & 0.103725086682567 & 0.633894494729432 & 0.966762104638334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]x$numcomp[/C][C]0.97589301132908[/C][C]0.97600086723082[/C][C]0.00414381541001758[/C][C]0.970486521322756[/C][C]0.983262829786502[/C][/ROW]
[ROW][C]x$numreceived[/C][C]0.967121032129824[/C][C]0.96664717624658[/C][C]0.00665439472259558[/C][C]0.95643431438181[/C][C]0.976466613670537[/C][/ROW]
[ROW][C]x$mrt[/C][C]0.644418928726694[/C][C]0.652334471954828[/C][C]0.0839292703589878[/C][C]0.498841169248474[/C][C]0.78972315186129[/C][/ROW]
[ROW][C]x$nfm[/C][C]0.65012097002215[/C][C]0.63992824129666[/C][C]0.0606669922444902[/C][C]0.538498763152236[/C][C]0.734561402546278[/C][/ROW]
[ROW][C]x$afl[/C][C]0.894422045366667[/C][C]0.895435609473154[/C][C]0.0226031812683594[/C][C]0.860599662474177[/C][C]0.929704612193706[/C][/ROW]
[ROW][C]x$lpm[/C][C]0.937045840238933[/C][C]0.939950702779052[/C][C]0.0130710215386941[/C][C]0.920737771222743[/C][C]0.959937350552744[/C][/ROW]
[ROW][C]x$lpc[/C][C]0.86098772593548[/C][C]0.854810869414369[/C][C]0.0432548513746062[/C][C]0.774666668616105[/C][C]0.91197539408801[/C][/ROW]
[ROW][C]x$Q5[/C][C]0.695302051439719[/C][C]0.571112219526708[/C][C]0.396791614694739[/C][C]-0.447057664672051[/C][C]0.985552275161499[/C][/ROW]
[ROW][C]x$Q9[/C][C]0.834755409354338[/C][C]0.743335680933893[/C][C]0.273159576242699[/C][C]0.177886464153935[/C][C]0.996250626485454[/C][/ROW]
[ROW][C]x$Q2[/C][C]0.810352508977645[/C][C]0.700921054327393[/C][C]0.25623761823037[/C][C]0.156167251692173[/C][C]0.990599245844105[/C][/ROW]
[ROW][C]x$Q4[/C][C]0.727279754967505[/C][C]0.69090099436637[/C][C]0.313053069310489[/C][C]0.227196802413121[/C][C]0.995035237505536[/C][/ROW]
[ROW][C]x$Q1_1[/C][C]0.944565451056465[/C][C]0.859976201029919[/C][C]0.143079372936456[/C][C]0.600034655719405[/C][C]0.999135608691803[/C][/ROW]
[ROW][C]x$Q2_2[/C][C]0.594438449871434[/C][C]0.662643419985725[/C][C]0.199504391198469[/C][C]0.278309911510852[/C][C]0.962811846323056[/C][/ROW]
[ROW][C]x$Q1_3[/C][C]0.854554270221953[/C][C]0.837338267080858[/C][C]0.105294427452294[/C][C]0.657213218653616[/C][C]0.974699320155514[/C][/ROW]
[ROW][C]x$Q2_3[/C][C]0.852490690659873[/C][C]0.8393117984157[/C][C]0.103725086682567[/C][C]0.633894494729432[/C][C]0.966762104638334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=12

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=12

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


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

BOOTSTRAP VALIDATION - LOADINGS
OriginalMean.BootStd.Errorperc.05perc.95
x$numcomp0.975893011329080.976000867230820.004143815410017580.9704865213227560.983262829786502
x$numreceived0.9671210321298240.966647176246580.006654394722595580.956434314381810.976466613670537
x$mrt0.6444189287266940.6523344719548280.08392927035898780.4988411692484740.78972315186129
x$nfm0.650120970022150.639928241296660.06066699224449020.5384987631522360.734561402546278
x$afl0.8944220453666670.8954356094731540.02260318126835940.8605996624741770.929704612193706
x$lpm0.9370458402389330.9399507027790520.01307102153869410.9207377712227430.959937350552744
x$lpc0.860987725935480.8548108694143690.04325485137460620.7746666686161050.91197539408801
x$Q50.6953020514397190.5711122195267080.396791614694739-0.4470576646720510.985552275161499
x$Q90.8347554093543380.7433356809338930.2731595762426990.1778864641539350.996250626485454
x$Q20.8103525089776450.7009210543273930.256237618230370.1561672516921730.990599245844105
x$Q40.7272797549675050.690900994366370.3130530693104890.2271968024131210.995035237505536
x$Q1_10.9445654510564650.8599762010299190.1430793729364560.6000346557194050.999135608691803
x$Q2_20.5944384498714340.6626434199857250.1995043911984690.2783099115108520.962811846323056
x$Q1_30.8545542702219530.8373382670808580.1052944274522940.6572132186536160.974699320155514
x$Q2_30.8524906906598730.83931179841570.1037250866825670.6338944947294320.966762104638334






BOOTSTRAP VALIDATION - PATHS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.3769758815553020.3850507505033020.0714128821243570.2790267757221660.488480292554856
S2->S30.1958540273140780.1735498720716010.09609909012379670.01280150078919160.301956012716224
S2->S40.1473554942755650.1478267515198350.0719530449264070.02301159777716950.237522144614576
S2->S50.2906366265475460.2759011753665660.09280019834980930.1347923137607840.410405458177398
S2->S60.2907351374422490.3062713172947210.06814253877012490.1815876072972890.404034039269814
S3->S40.1481882993514080.1865100418512630.1045932568650280.06282414615260010.361049436574114
S4->S5-0.108052431635754-0.07397434588454610.148123903013346-0.2668689950438990.17213873479644
S5->S60.3714099979405560.3879448445885410.08346114101001330.264175147012250.506640226705388

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - PATHS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S1->S2 & 0.376975881555302 & 0.385050750503302 & 0.071412882124357 & 0.279026775722166 & 0.488480292554856 \tabularnewline
S2->S3 & 0.195854027314078 & 0.173549872071601 & 0.0960990901237967 & 0.0128015007891916 & 0.301956012716224 \tabularnewline
S2->S4 & 0.147355494275565 & 0.147826751519835 & 0.071953044926407 & 0.0230115977771695 & 0.237522144614576 \tabularnewline
S2->S5 & 0.290636626547546 & 0.275901175366566 & 0.0928001983498093 & 0.134792313760784 & 0.410405458177398 \tabularnewline
S2->S6 & 0.290735137442249 & 0.306271317294721 & 0.0681425387701249 & 0.181587607297289 & 0.404034039269814 \tabularnewline
S3->S4 & 0.148188299351408 & 0.186510041851263 & 0.104593256865028 & 0.0628241461526001 & 0.361049436574114 \tabularnewline
S4->S5 & -0.108052431635754 & -0.0739743458845461 & 0.148123903013346 & -0.266868995043899 & 0.17213873479644 \tabularnewline
S5->S6 & 0.371409997940556 & 0.387944844588541 & 0.0834611410100133 & 0.26417514701225 & 0.506640226705388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]0.376975881555302[/C][C]0.385050750503302[/C][C]0.071412882124357[/C][C]0.279026775722166[/C][C]0.488480292554856[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.195854027314078[/C][C]0.173549872071601[/C][C]0.0960990901237967[/C][C]0.0128015007891916[/C][C]0.301956012716224[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.147355494275565[/C][C]0.147826751519835[/C][C]0.071953044926407[/C][C]0.0230115977771695[/C][C]0.237522144614576[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.290636626547546[/C][C]0.275901175366566[/C][C]0.0928001983498093[/C][C]0.134792313760784[/C][C]0.410405458177398[/C][/ROW]
[ROW][C]S2->S6[/C][C]0.290735137442249[/C][C]0.306271317294721[/C][C]0.0681425387701249[/C][C]0.181587607297289[/C][C]0.404034039269814[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.148188299351408[/C][C]0.186510041851263[/C][C]0.104593256865028[/C][C]0.0628241461526001[/C][C]0.361049436574114[/C][/ROW]
[ROW][C]S4->S5[/C][C]-0.108052431635754[/C][C]-0.0739743458845461[/C][C]0.148123903013346[/C][C]-0.266868995043899[/C][C]0.17213873479644[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.371409997940556[/C][C]0.387944844588541[/C][C]0.0834611410100133[/C][C]0.26417514701225[/C][C]0.506640226705388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=13

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=13

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


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

BOOTSTRAP VALIDATION - PATHS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.3769758815553020.3850507505033020.0714128821243570.2790267757221660.488480292554856
S2->S30.1958540273140780.1735498720716010.09609909012379670.01280150078919160.301956012716224
S2->S40.1473554942755650.1478267515198350.0719530449264070.02301159777716950.237522144614576
S2->S50.2906366265475460.2759011753665660.09280019834980930.1347923137607840.410405458177398
S2->S60.2907351374422490.3062713172947210.06814253877012490.1815876072972890.404034039269814
S3->S40.1481882993514080.1865100418512630.1045932568650280.06282414615260010.361049436574114
S4->S5-0.108052431635754-0.07397434588454610.148123903013346-0.2668689950438990.17213873479644
S5->S60.3714099979405560.3879448445885410.08346114101001330.264175147012250.506640226705388






BOOTSTRAP VALIDATION - RSQ
OriginalMean.BootStd.Errorperc.05perc.95
S20.1421108152743970.153312882199130.05734070039674560.07785596482469020.238621572109348
S30.03835880001514340.03926224286746430.02928832097515010.002924096054482270.091177992705348
S40.05222689189152890.08155626784662980.04208499871734470.03271421290911610.162264317437125
S50.08506698095693720.1030811155063810.04443812397534580.04829501070640890.17238091487901
S60.2811234454291040.3171990265654340.07469786737445510.1868075474886930.426993071956298

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - RSQ \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S2 & 0.142110815274397 & 0.15331288219913 & 0.0573407003967456 & 0.0778559648246902 & 0.238621572109348 \tabularnewline
S3 & 0.0383588000151434 & 0.0392622428674643 & 0.0292883209751501 & 0.00292409605448227 & 0.091177992705348 \tabularnewline
S4 & 0.0522268918915289 & 0.0815562678466298 & 0.0420849987173447 & 0.0327142129091161 & 0.162264317437125 \tabularnewline
S5 & 0.0850669809569372 & 0.103081115506381 & 0.0444381239753458 & 0.0482950107064089 & 0.17238091487901 \tabularnewline
S6 & 0.281123445429104 & 0.317199026565434 & 0.0746978673744551 & 0.186807547488693 & 0.426993071956298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]0.142110815274397[/C][C]0.15331288219913[/C][C]0.0573407003967456[/C][C]0.0778559648246902[/C][C]0.238621572109348[/C][/ROW]
[ROW][C]S3[/C][C]0.0383588000151434[/C][C]0.0392622428674643[/C][C]0.0292883209751501[/C][C]0.00292409605448227[/C][C]0.091177992705348[/C][/ROW]
[ROW][C]S4[/C][C]0.0522268918915289[/C][C]0.0815562678466298[/C][C]0.0420849987173447[/C][C]0.0327142129091161[/C][C]0.162264317437125[/C][/ROW]
[ROW][C]S5[/C][C]0.0850669809569372[/C][C]0.103081115506381[/C][C]0.0444381239753458[/C][C]0.0482950107064089[/C][C]0.17238091487901[/C][/ROW]
[ROW][C]S6[/C][C]0.281123445429104[/C][C]0.317199026565434[/C][C]0.0746978673744551[/C][C]0.186807547488693[/C][C]0.426993071956298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=14

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=14

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


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

BOOTSTRAP VALIDATION - RSQ
OriginalMean.BootStd.Errorperc.05perc.95
S20.1421108152743970.153312882199130.05734070039674560.07785596482469020.238621572109348
S30.03835880001514340.03926224286746430.02928832097515010.002924096054482270.091177992705348
S40.05222689189152890.08155626784662980.04208499871734470.03271421290911610.162264317437125
S50.08506698095693720.1030811155063810.04443812397534580.04829501070640890.17238091487901
S60.2811234454291040.3171990265654340.07469786737445510.1868075474886930.426993071956298






BOOTSTRAP VALIDATION - TOTAL EFFECTS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.3769758815553020.3850507505033020.0714128821243570.2790267757221660.488480292554856
S1->S30.07383224460288060.0685177072329860.04124231424093550.00421481956682420.134564566479968
S1->S40.06649054212154650.06859485928174240.03250226893399780.02342856245264360.111832415145125
S1->S50.1023785337480080.1026060698774740.04170210083918250.04221563058017460.177279232572301
S1->S60.1476245457448980.1565301123277060.03617884172352870.1009520165599530.216933279483106
S2->S30.1958540273140780.1735498720716010.09609909012379670.01280150078919160.301956012716224
S2->S40.1763787695043630.178109473815340.0760632290444570.05734540043567380.293301957489129
S2->S50.2715784716136780.2630451528446750.08801400897980830.1239265919261110.388667852002544
S2->S60.3916020970249840.4088626126050980.07029515547300890.2916930361213980.512377287924419
S3->S40.1481882993514080.1865100418512630.1045932568650280.06282414615260010.361049436574114
S3->S5-0.0160121060848867-0.0107078091518430.0305380859928105-0.05273375408853740.0455885124285204
S3->S6-0.00594705628801173-0.003973919017614670.0118180909011259-0.02183883452057170.0135577289231678
S4->S5-0.108052431635754-0.07397434588454610.148123903013346-0.2668689950438990.17213873479644
S4->S6-0.0401317534113076-0.02847427822559090.0593913661867966-0.1051270259554220.0776164495998728
S5->S60.3714099979405560.3879448445885410.08346114101001330.264175147012250.506640226705388

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - TOTAL EFFECTS \tabularnewline
 & Original & Mean.Boot & Std.Error & perc.05 & perc.95 \tabularnewline
S1->S2 & 0.376975881555302 & 0.385050750503302 & 0.071412882124357 & 0.279026775722166 & 0.488480292554856 \tabularnewline
S1->S3 & 0.0738322446028806 & 0.068517707232986 & 0.0412423142409355 & 0.0042148195668242 & 0.134564566479968 \tabularnewline
S1->S4 & 0.0664905421215465 & 0.0685948592817424 & 0.0325022689339978 & 0.0234285624526436 & 0.111832415145125 \tabularnewline
S1->S5 & 0.102378533748008 & 0.102606069877474 & 0.0417021008391825 & 0.0422156305801746 & 0.177279232572301 \tabularnewline
S1->S6 & 0.147624545744898 & 0.156530112327706 & 0.0361788417235287 & 0.100952016559953 & 0.216933279483106 \tabularnewline
S2->S3 & 0.195854027314078 & 0.173549872071601 & 0.0960990901237967 & 0.0128015007891916 & 0.301956012716224 \tabularnewline
S2->S4 & 0.176378769504363 & 0.17810947381534 & 0.076063229044457 & 0.0573454004356738 & 0.293301957489129 \tabularnewline
S2->S5 & 0.271578471613678 & 0.263045152844675 & 0.0880140089798083 & 0.123926591926111 & 0.388667852002544 \tabularnewline
S2->S6 & 0.391602097024984 & 0.408862612605098 & 0.0702951554730089 & 0.291693036121398 & 0.512377287924419 \tabularnewline
S3->S4 & 0.148188299351408 & 0.186510041851263 & 0.104593256865028 & 0.0628241461526001 & 0.361049436574114 \tabularnewline
S3->S5 & -0.0160121060848867 & -0.010707809151843 & 0.0305380859928105 & -0.0527337540885374 & 0.0455885124285204 \tabularnewline
S3->S6 & -0.00594705628801173 & -0.00397391901761467 & 0.0118180909011259 & -0.0218388345205717 & 0.0135577289231678 \tabularnewline
S4->S5 & -0.108052431635754 & -0.0739743458845461 & 0.148123903013346 & -0.266868995043899 & 0.17213873479644 \tabularnewline
S4->S6 & -0.0401317534113076 & -0.0284742782255909 & 0.0593913661867966 & -0.105127025955422 & 0.0776164495998728 \tabularnewline
S5->S6 & 0.371409997940556 & 0.387944844588541 & 0.0834611410100133 & 0.26417514701225 & 0.506640226705388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=79513&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]0.376975881555302[/C][C]0.385050750503302[/C][C]0.071412882124357[/C][C]0.279026775722166[/C][C]0.488480292554856[/C][/ROW]
[ROW][C]S1->S3[/C][C]0.0738322446028806[/C][C]0.068517707232986[/C][C]0.0412423142409355[/C][C]0.0042148195668242[/C][C]0.134564566479968[/C][/ROW]
[ROW][C]S1->S4[/C][C]0.0664905421215465[/C][C]0.0685948592817424[/C][C]0.0325022689339978[/C][C]0.0234285624526436[/C][C]0.111832415145125[/C][/ROW]
[ROW][C]S1->S5[/C][C]0.102378533748008[/C][C]0.102606069877474[/C][C]0.0417021008391825[/C][C]0.0422156305801746[/C][C]0.177279232572301[/C][/ROW]
[ROW][C]S1->S6[/C][C]0.147624545744898[/C][C]0.156530112327706[/C][C]0.0361788417235287[/C][C]0.100952016559953[/C][C]0.216933279483106[/C][/ROW]
[ROW][C]S2->S3[/C][C]0.195854027314078[/C][C]0.173549872071601[/C][C]0.0960990901237967[/C][C]0.0128015007891916[/C][C]0.301956012716224[/C][/ROW]
[ROW][C]S2->S4[/C][C]0.176378769504363[/C][C]0.17810947381534[/C][C]0.076063229044457[/C][C]0.0573454004356738[/C][C]0.293301957489129[/C][/ROW]
[ROW][C]S2->S5[/C][C]0.271578471613678[/C][C]0.263045152844675[/C][C]0.0880140089798083[/C][C]0.123926591926111[/C][C]0.388667852002544[/C][/ROW]
[ROW][C]S2->S6[/C][C]0.391602097024984[/C][C]0.408862612605098[/C][C]0.0702951554730089[/C][C]0.291693036121398[/C][C]0.512377287924419[/C][/ROW]
[ROW][C]S3->S4[/C][C]0.148188299351408[/C][C]0.186510041851263[/C][C]0.104593256865028[/C][C]0.0628241461526001[/C][C]0.361049436574114[/C][/ROW]
[ROW][C]S3->S5[/C][C]-0.0160121060848867[/C][C]-0.010707809151843[/C][C]0.0305380859928105[/C][C]-0.0527337540885374[/C][C]0.0455885124285204[/C][/ROW]
[ROW][C]S3->S6[/C][C]-0.00594705628801173[/C][C]-0.00397391901761467[/C][C]0.0118180909011259[/C][C]-0.0218388345205717[/C][C]0.0135577289231678[/C][/ROW]
[ROW][C]S4->S5[/C][C]-0.108052431635754[/C][C]-0.0739743458845461[/C][C]0.148123903013346[/C][C]-0.266868995043899[/C][C]0.17213873479644[/C][/ROW]
[ROW][C]S4->S6[/C][C]-0.0401317534113076[/C][C]-0.0284742782255909[/C][C]0.0593913661867966[/C][C]-0.105127025955422[/C][C]0.0776164495998728[/C][/ROW]
[ROW][C]S5->S6[/C][C]0.371409997940556[/C][C]0.387944844588541[/C][C]0.0834611410100133[/C][C]0.26417514701225[/C][C]0.506640226705388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=79513&T=15

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=79513&T=15

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


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

BOOTSTRAP VALIDATION - TOTAL EFFECTS
OriginalMean.BootStd.Errorperc.05perc.95
S1->S20.3769758815553020.3850507505033020.0714128821243570.2790267757221660.488480292554856
S1->S30.07383224460288060.0685177072329860.04124231424093550.00421481956682420.134564566479968
S1->S40.06649054212154650.06859485928174240.03250226893399780.02342856245264360.111832415145125
S1->S50.1023785337480080.1026060698774740.04170210083918250.04221563058017460.177279232572301
S1->S60.1476245457448980.1565301123277060.03617884172352870.1009520165599530.216933279483106
S2->S30.1958540273140780.1735498720716010.09609909012379670.01280150078919160.301956012716224
S2->S40.1763787695043630.178109473815340.0760632290444570.05734540043567380.293301957489129
S2->S50.2715784716136780.2630451528446750.08801400897980830.1239265919261110.388667852002544
S2->S60.3916020970249840.4088626126050980.07029515547300890.2916930361213980.512377287924419
S3->S40.1481882993514080.1865100418512630.1045932568650280.06282414615260010.361049436574114
S3->S5-0.0160121060848867-0.0107078091518430.0305380859928105-0.05273375408853740.0455885124285204
S3->S6-0.00594705628801173-0.003973919017614670.0118180909011259-0.02183883452057170.0135577289231678
S4->S5-0.108052431635754-0.07397434588454610.148123903013346-0.2668689950438990.17213873479644
S4->S6-0.0401317534113076-0.02847427822559090.0593913661867966-0.1051270259554220.0776164495998728
S5->S60.3714099979405560.3879448445885410.08346114101001330.264175147012250.506640226705388


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = COMPUTE REVIEW EXAM0 EXAM1 EXAM2 EXAM3 ; par2 = A A B B B B ; par3 = 1 2 ; par4 = 3 4 5 6 7 ; par5 = 8 9 ; par6 = 10 11 ; par7 = 12 13 ; par8 = 14 15 ; 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 = 0 1 0 1 0 0 ; par16 = 0 1 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) {
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$total.efs[myind,'perc.05'] < 0) {
myr[i,j] = 0
}
myind = myind + 1
}
}
}
bitmap(file='test2.png')
plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames,
lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle',
box.prop = 0.5, box.cex = 1, arr.type = 'triangle',
arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue',
arr.col = 'blue', arr.width = 0.2, main = c('Inner Model',
'Total Effects'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'MODEL SPECIFICATION',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of Cases',header=TRUE)
a<-table.element(a,r$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')
-SERVER-193.190.124.10:1001