C:\Users\user\Desktop\paper\paper10 Leader’s Political Skill, Knowledge Sharing, and Career Growth During Economic and Employment Uncertainty The Moderating Role of Job Insecurity\2.AMOS数据\CFA.amw

Analysis Summary

Date and Time

Date: 2026年5月9日
Time: 22:13:19

Title

Cfa: 2026年5月9日 22:13

Groups

Group number 1 (Group number 1)

Notes for Group (Group number 1)

The model is recursive.
Sample size = 423

Variable Summary (Group number 1)

Your model contains the following variables (Group number 1)

Observed, endogenous variables
LPS1
LPS2
LPS3
LPS4
LPS5
LPS6
KS1
KS2
KS3
KS4
KS5
CG1
CG2
CG3
CG4
CG5
CG6
CG7
CG8
CG9
CG10
CG11
CG12
CG13
CG14
CG15
JI4
JI3
JI2
JI1
Unobserved, exogenous variables
F1LPS
e1
e2
e3
e4
e5
e6
F2KS
e7
e8
e9
e10
e11
F3CG
e12
e13
e14
e15
e16
e17
e18
e19
e20
e21
e22
e23
e24
e25
e26
F4JI
e27
e28
e29
e30

Variable counts (Group number 1)

Number of variables in your model: 64
Number of observed variables: 30
Number of unobserved variables: 34
Number of exogenous variables: 34
Number of endogenous variables: 30

Parameter Summary (Group number 1)

Weights Covariances Variances Means Intercepts Total
Fixed 34 0 0 0 0 34
Labeled 0 0 0 0 0 0
Unlabeled 26 6 34 0 0 66
Total 60 6 34 0 0 100

Assessment of normality (Group number 1)

Variable min max skew c.r. kurtosis c.r.
JI1 1.000 5.000 -.044 -.368 -.864 -3.629
JI2 1.000 5.000 -.060 -.500 -.893 -3.750
JI3 1.000 5.000 -.006 -.055 -.934 -3.922
JI4 1.000 5.000 .002 .015 -.917 -3.849
CG15 1.000 5.000 -.005 -.042 -1.017 -4.269
CG14 1.000 5.000 -.003 -.029 -.858 -3.601
CG13 1.000 5.000 -.067 -.566 -.929 -3.899
CG12 1.000 5.000 .045 .378 -.941 -3.950
CG11 1.000 5.000 -.027 -.226 -.917 -3.851
CG10 1.000 5.000 .077 .650 -.888 -3.730
CG9 1.000 5.000 -.003 -.025 -.921 -3.867
CG8 1.000 5.000 -.060 -.502 -1.008 -4.234
CG7 1.000 5.000 -.062 -.521 -.963 -4.043
CG6 1.000 5.000 -.033 -.274 -.915 -3.842
CG5 1.000 5.000 .017 .142 -.994 -4.175
CG4 1.000 5.000 .055 .465 -1.014 -4.257
CG3 1.000 5.000 .024 .198 -.939 -3.944
CG2 1.000 5.000 .086 .718 -.977 -4.101
CG1 1.000 5.000 -.007 -.058 -.905 -3.799
KS5 1.000 5.000 -.044 -.371 -.914 -3.839
KS4 1.000 5.000 -.041 -.346 -.940 -3.948
KS3 1.000 5.000 -.012 -.099 -.945 -3.968
KS2 1.000 5.000 .036 .300 -.968 -4.062
KS1 1.000 5.000 -.046 -.387 -1.062 -4.459
LPS6 1.000 5.000 -.025 -.208 -.831 -3.489
LPS5 1.000 5.000 -.032 -.270 -.940 -3.948
LPS4 1.000 5.000 .023 .194 -.771 -3.238
LPS3 1.000 5.000 .085 .714 -.800 -3.357
LPS2 1.000 5.000 .030 .254 -.820 -3.444
LPS1 1.000 5.000 .014 .115 -.843 -3.539
Multivariate -5.168 -1.213

Observations farthest from the centroid (Mahalanobis distance) (Group number 1)

Observation number Mahalanobis d-squared p1 p2
181 53.135 .006 .912
423 49.368 .014 .985
374 48.082 .019 .989
143 47.285 .023 .989
225 47.006 .025 .980
389 46.939 .025 .956
403 45.756 .033 .986
169 45.324 .036 .986
369 45.073 .038 .981
194 44.967 .039 .968
59 44.690 .041 .963
337 44.300 .045 .967
137 43.826 .049 .977
315 43.688 .051 .969
375 43.381 .054 .971
85 43.369 .054 .952
14 43.283 .055 .935
13 43.154 .057 .920
365 43.143 .057 .882
351 42.954 .059 .875
49 42.662 .063 .890
38 42.444 .066 .893
323 42.126 .070 .914
198 41.690 .076 .949
223 41.676 .076 .927
48 41.611 .077 .908
192 41.290 .082 .933
210 41.189 .084 .923
52 41.096 .085 .910
141 40.927 .088 .912
43 40.839 .090 .899
321 40.694 .092 .898
145 40.500 .096 .908
287 40.434 .097 .891
32 40.424 .097 .858
84 40.346 .098 .840
396 40.197 .101 .844
239 40.048 .104 .849
286 39.791 .109 .884
273 39.641 .112 .890
151 39.410 .117 .914
344 39.331 .118 .905
378 39.296 .119 .885
418 39.152 .122 .892
63 39.146 .122 .862
179 39.131 .123 .830
66 38.749 .131 .907
398 38.744 .132 .881
72 38.375 .140 .939
71 38.360 .141 .922
406 38.238 .144 .926
420 38.085 .148 .936
326 38.005 .150 .932
206 38.001 .150 .912
341 37.871 .153 .919
134 37.864 .153 .898
65 37.807 .155 .888
12 37.778 .156 .868
92 37.764 .156 .841
413 37.722 .157 .822
279 37.712 .157 .788
97 37.678 .158 .762
146 37.524 .162 .791
422 37.449 .164 .785
202 37.445 .165 .745
176 37.340 .167 .754
128 37.159 .173 .797
154 37.136 .173 .769
186 37.114 .174 .738
7 37.105 .174 .699
253 37.070 .175 .672
363 37.069 .175 .625
55 36.853 .181 .701
417 36.773 .184 .700
62 36.755 .184 .666
303 36.670 .187 .669
258 36.637 .188 .642
278 36.540 .191 .653
241 36.506 .192 .627
115 36.360 .197 .668
340 36.357 .197 .624
232 36.275 .199 .628
334 36.219 .201 .616
246 36.212 .201 .573
233 36.115 .204 .588
261 35.989 .208 .621
162 35.824 .214 .678
320 35.789 .215 .656
313 35.760 .216 .630
57 35.691 .218 .629
381 35.507 .225 .699
291 35.506 .225 .657
4 35.495 .225 .620
329 35.380 .229 .649
68 35.363 .230 .617
405 35.351 .230 .580
5 35.301 .232 .568
392 35.193 .236 .596
40 35.175 .236 .563
385 35.000 .243 .636

Sample Moments (Group number 1)

Sample Covariances (Group number 1)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 1.419
JI2 .708 1.468
JI3 .768 .774 1.482
JI4 .727 .796 .797 1.425
CG15 .003 -.061 -.118 .000 1.608
CG14 -.139 -.177 -.177 -.064 .766 1.468
CG13 -.027 -.098 -.112 -.067 .816 .830 1.480
CG12 -.166 -.140 -.128 -.040 .716 .690 .827 1.498
CG11 -.001 -.119 -.112 -.039 .891 .835 .847 .837 1.541
CG10 -.097 -.113 -.227 -.052 .719 .726 .780 .763 .870 1.493
CG9 -.056 -.141 -.164 -.065 .872 .780 .818 .740 .891 .893 1.532
CG8 -.043 -.062 -.072 -.093 .733 .719 .738 .636 .709 .594 .705 1.534
CG7 -.107 -.045 -.107 .005 .801 .792 .806 .709 .761 .747 .764 .742 1.572
CG6 -.041 -.157 -.088 -.058 .809 .705 .742 .650 .801 .735 .783 .676 .678 1.551
CG5 -.048 -.105 -.098 -.008 .856 .837 .809 .712 .893 .785 .835 .723 .816 .768 1.612
CG4 .016 -.074 -.048 .047 .801 .705 .731 .740 .846 .790 .804 .645 .712 .759 .764 1.579
CG3 -.087 -.165 -.246 -.179 .823 .728 .792 .801 .771 .771 .749 .700 .783 .757 .742 .638 1.551
CG2 -.118 -.167 -.154 -.009 .759 .752 .735 .723 .769 .815 .770 .684 .799 .738 .823 .728 .735 1.521
CG1 -.124 -.183 -.138 -.079 .816 .785 .835 .688 .808 .686 .797 .730 .768 .697 .797 .716 .650 .743 1.482
KS5 .002 .042 -.022 .030 .563 .541 .488 .457 .600 .516 .537 .401 .480 .446 .555 .520 .492 .408 .491 1.481
KS4 -.017 -.024 -.022 .039 .438 .506 .499 .516 .472 .462 .528 .420 .399 .449 .491 .414 .416 .403 .470 .765 1.497
KS3 -.021 .030 -.086 -.008 .473 .454 .452 .396 .536 .453 .502 .331 .418 .378 .522 .331 .421 .353 .456 .724 .767 1.530
KS2 -.095 -.059 -.038 .028 .456 .513 .494 .435 .537 .485 .515 .407 .428 .447 .527 .362 .388 .407 .494 .799 .842 .714 1.579
KS1 .029 -.042 .053 -.070 .475 .470 .525 .463 .565 .487 .534 .383 .376 .419 .492 .385 .428 .435 .499 .757 .814 .747 .837 1.629
LPS6 .079 .074 -.018 .122 .409 .440 .364 .385 .435 .357 .364 .449 .338 .312 .404 .331 .338 .352 .367 .476 .469 .421 .463 .432 1.385
LPS5 .148 .144 .085 .222 .515 .466 .489 .404 .457 .408 .402 .360 .409 .343 .506 .319 .362 .410 .504 .434 .431 .379 .534 .430 .673 1.439
LPS4 .087 .102 .007 .109 .489 .433 .439 .363 .497 .373 .376 .350 .435 .367 .393 .414 .343 .366 .412 .490 .367 .405 .468 .442 .699 .746 1.335
LPS3 .073 .080 -.005 .083 .418 .433 .376 .380 .428 .330 .354 .397 .352 .319 .442 .296 .291 .347 .407 .384 .410 .396 .392 .383 .706 .706 .652 1.387
LPS2 .133 .176 .116 .273 .466 .449 .394 .434 .426 .377 .368 .374 .343 .341 .374 .428 .260 .330 .369 .427 .410 .399 .444 .345 .694 .758 .727 .651 1.398
LPS1 .048 .069 -.004 .164 .269 .416 .236 .250 .430 .243 .312 .251 .329 .310 .385 .265 .220 .300 .338 .355 .371 .400 .475 .362 .702 .711 .650 .671 .685 1.402
Condition number = 36.290
Eigenvalues
15.654 4.479 3.152 2.395 1.129 1.055 1.028 1.013 .961 .939 .898 .852 .806 .787 .782 .729 .715 .695 .672 .664 .635 .621 .611 .572 .560 .538 .519 .508 .476 .431
Determinant of sample covariance matrix = .082

Sample Correlations (Group number 1)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 1.000
JI2 .491 1.000
JI3 .530 .525 1.000
JI4 .511 .551 .549 1.000
CG15 .002 -.040 -.076 .000 1.000
CG14 -.097 -.121 -.120 -.044 .499 1.000
CG13 -.019 -.067 -.076 -.046 .529 .563 1.000
CG12 -.114 -.094 -.086 -.028 .461 .465 .555 1.000
CG11 -.001 -.079 -.074 -.026 .566 .555 .561 .551 1.000
CG10 -.067 -.077 -.153 -.036 .464 .490 .525 .510 .574 1.000
CG9 -.038 -.094 -.109 -.044 .556 .520 .543 .488 .580 .591 1.000
CG8 -.029 -.042 -.048 -.063 .467 .479 .490 .420 .461 .392 .460 1.000
CG7 -.071 -.030 -.070 .003 .504 .521 .529 .462 .489 .488 .492 .478 1.000
CG6 -.028 -.104 -.058 -.039 .512 .467 .490 .427 .518 .483 .508 .438 .435 1.000
CG5 -.032 -.068 -.063 -.005 .532 .544 .524 .458 .567 .506 .531 .460 .512 .486 1.000
CG4 .011 -.048 -.031 .031 .503 .463 .478 .481 .542 .514 .517 .415 .452 .485 .479 1.000
CG3 -.059 -.109 -.162 -.121 .521 .483 .523 .526 .499 .506 .486 .454 .501 .488 .469 .408 1.000
CG2 -.081 -.112 -.102 -.006 .485 .503 .490 .479 .502 .540 .505 .447 .517 .480 .526 .470 .479 1.000
CG1 -.085 -.124 -.093 -.054 .528 .532 .564 .462 .535 .461 .529 .484 .503 .460 .515 .468 .429 .495 1.000
KS5 .002 .028 -.015 .021 .365 .367 .329 .307 .397 .347 .357 .266 .314 .295 .359 .340 .325 .272 .332 1.000
KS4 -.011 -.017 -.015 .027 .282 .341 .335 .345 .311 .309 .348 .277 .260 .295 .316 .269 .273 .267 .315 .513 1.000
KS3 -.015 .020 -.057 -.006 .302 .303 .300 .261 .349 .299 .328 .216 .270 .245 .332 .213 .273 .232 .303 .481 .507 1.000
KS2 -.063 -.039 -.025 .019 .286 .337 .323 .283 .344 .316 .331 .261 .272 .286 .330 .229 .248 .262 .323 .522 .547 .459 1.000
KS1 .019 -.027 .034 -.046 .294 .304 .338 .296 .357 .312 .338 .242 .235 .263 .304 .240 .269 .276 .321 .487 .521 .473 .522 1.000
LPS6 .056 .052 -.012 .087 .274 .308 .254 .267 .298 .248 .250 .308 .229 .213 .271 .224 .231 .242 .256 .332 .325 .289 .313 .288 1.000
LPS5 .104 .099 .058 .155 .339 .320 .335 .275 .307 .278 .270 .242 .272 .230 .332 .212 .242 .277 .345 .297 .294 .256 .354 .281 .477 1.000
LPS4 .063 .073 .005 .079 .334 .309 .313 .257 .346 .264 .263 .245 .300 .255 .268 .285 .238 .257 .293 .349 .260 .283 .322 .300 .514 .538 1.000
LPS3 .052 .056 -.004 .059 .280 .303 .262 .264 .293 .229 .243 .272 .239 .218 .296 .200 .198 .239 .284 .268 .284 .272 .265 .255 .510 .500 .479 1.000
LPS2 .094 .123 .081 .193 .311 .313 .274 .300 .290 .261 .252 .255 .231 .231 .249 .288 .176 .226 .257 .297 .284 .272 .299 .228 .499 .534 .532 .468 1.000
LPS1 .034 .048 -.003 .116 .180 .290 .164 .173 .293 .168 .213 .171 .221 .210 .256 .178 .149 .205 .235 .246 .256 .273 .319 .239 .504 .501 .475 .481 .490 1.000
Condition number = 35.889
Eigenvalues
10.340 3.102 2.135 1.611 .744 .705 .679 .665 .641 .634 .593 .562 .540 .521 .519 .493 .481 .466 .444 .442 .429 .420 .408 .385 .371 .363 .353 .345 .321 .288

Models

Default model (Default model)

Notes for Model (Default model)

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 465
Number of distinct parameters to be estimated: 66
Degrees of freedom (465 - 66): 399

Result (Default model)

Minimum was achieved
Chi-square = 401.692
Degrees of freedom = 399
Probability level = .453

Group number 1 (Group number 1 - Default model)

Estimates (Group number 1 - Default model)

Scalar Estimates (Group number 1 - Default model)

Maximum Likelihood Estimates

Regression Weights: (Group number 1 - Default model)

Estimate S.E. C.R. P Label
LPS1 <--- F1LPS 1.000
LPS2 <--- F1LPS 1.057 .082 12.837 *** par_1
LPS3 <--- F1LPS .998 .081 12.328 *** par_2
LPS4 <--- F1LPS 1.044 .081 12.887 *** par_3
LPS5 <--- F1LPS 1.092 .084 13.043 *** par_4
LPS6 <--- F1LPS 1.035 .081 12.742 *** par_5
KS1 <--- F2KS 1.000
KS2 <--- F2KS 1.027 .078 13.225 *** par_6
KS3 <--- F2KS .924 .076 12.195 *** par_7
KS4 <--- F2KS 1.010 .076 13.316 *** par_8
KS5 <--- F2KS .976 .076 12.894 *** par_9
CG1 <--- F3CG 1.000
CG2 <--- F3CG .994 .072 13.810 *** par_10
CG3 <--- F3CG .982 .073 13.463 *** par_11
CG4 <--- F3CG .978 .073 13.330 *** par_12
CG5 <--- F3CG 1.064 .074 14.369 *** par_13
CG6 <--- F3CG .970 .073 13.334 *** par_14
CG7 <--- F3CG 1.005 .073 13.763 *** par_15
CG8 <--- F3CG .913 .072 12.681 *** par_16
CG9 <--- F3CG 1.067 .072 14.776 *** par_17
CG10 <--- F3CG 1.014 .072 14.158 *** par_18
CG11 <--- F3CG 1.104 .073 15.218 *** par_19
CG12 <--- F3CG .970 .072 13.554 *** par_20
CG13 <--- F3CG 1.054 .071 14.893 *** par_21
CG14 <--- F3CG 1.015 .071 14.389 *** par_22
CG15 <--- F3CG 1.062 .074 14.376 *** par_23
JI4 <--- F4JI 1.000
JI3 <--- F4JI 1.002 .076 13.162 *** par_24
JI2 <--- F4JI .977 .074 13.148 *** par_25
JI1 <--- F4JI .921 .074 12.515 *** par_26

Standardized Regression Weights: (Group number 1 - Default model)

Estimate
LPS1 <--- F1LPS .679
LPS2 <--- F1LPS .718
LPS3 <--- F1LPS .681
LPS4 <--- F1LPS .726
LPS5 <--- F1LPS .732
LPS6 <--- F1LPS .707
KS1 <--- F2KS .700
KS2 <--- F2KS .729
KS3 <--- F2KS .667
KS4 <--- F2KS .737
KS5 <--- F2KS .716
CG1 <--- F3CG .709
CG2 <--- F3CG .695
CG3 <--- F3CG .680
CG4 <--- F3CG .671
CG5 <--- F3CG .723
CG6 <--- F3CG .672
CG7 <--- F3CG .692
CG8 <--- F3CG .636
CG9 <--- F3CG .744
CG10 <--- F3CG .716
CG11 <--- F3CG .767
CG12 <--- F3CG .683
CG13 <--- F3CG .747
CG14 <--- F3CG .723
CG15 <--- F3CG .723
JI4 <--- F4JI .750
JI3 <--- F4JI .737
JI2 <--- F4JI .722
JI1 <--- F4JI .692

Covariances: (Group number 1 - Default model)

Estimate S.E. C.R. P Label
F1LPS <--> F2KS .410 .055 7.516 *** par_27
F1LPS <--> F3CG .361 .049 7.401 *** par_28
F1LPS <--> F4JI .100 .043 2.335 .020 par_29
F2KS <--> F3CG .465 .058 8.028 *** par_30
F2KS <--> F4JI -.012 .047 -.251 .802 par_31
F3CG <--> F4JI -.092 .044 -2.122 .034 par_32

Correlations: (Group number 1 - Default model)

Estimate
F1LPS <--> F2KS .572
F1LPS <--> F3CG .521
F1LPS <--> F4JI .140
F2KS <--> F3CG .604
F2KS <--> F4JI -.015
F3CG <--> F4JI -.120

Variances: (Group number 1 - Default model)

Estimate S.E. C.R. P Label
F1LPS .645 .087 7.462 *** par_33
F2KS .797 .104 7.693 *** par_34
F3CG .744 .091 8.219 *** par_35
F4JI .801 .098 8.144 *** par_36
e1 .756 .060 12.620 *** par_37
e2 .677 .056 12.150 *** par_38
e3 .744 .059 12.593 *** par_39
e4 .632 .052 12.047 *** par_40
e5 .669 .056 11.938 *** par_41
e6 .694 .057 12.275 *** par_42
e7 .832 .069 12.111 *** par_43
e8 .739 .063 11.647 *** par_44
e9 .849 .068 12.503 *** par_45
e10 .685 .059 11.524 *** par_46
e11 .722 .061 11.850 *** par_47
e12 .738 .054 13.588 *** par_48
e13 .787 .058 13.659 *** par_49
e14 .833 .061 13.721 *** par_50
e15 .868 .063 13.765 *** par_51
e16 .770 .057 13.513 *** par_52
e17 .851 .062 13.765 *** par_53
e18 .820 .060 13.673 *** par_54
e19 .913 .066 13.894 *** par_55
e20 .684 .051 13.375 *** par_56
e21 .729 .054 13.541 *** par_57
e22 .635 .048 13.198 *** par_58
e23 .799 .058 13.709 *** par_59
e24 .653 .049 13.350 *** par_60
e25 .701 .052 13.509 *** par_61
e26 .768 .057 13.511 *** par_62
e27 .623 .061 10.182 *** par_63
e28 .677 .064 10.501 *** par_64
e29 .703 .065 10.876 *** par_65
e30 .738 .064 11.450 *** par_66

Squared Multiple Correlations: (Group number 1 - Default model)

Estimate
JI1 .480
JI2 .521
JI3 .543
JI4 .563
CG15 .522
CG14 .522
CG13 .559
CG12 .467
CG11 .588
CG10 .512
CG9 .553
CG8 .405
CG7 .479
CG6 .451
CG5 .523
CG4 .450
CG3 .463
CG2 .483
CG1 .502
KS5 .513
KS4 .542
KS3 .445
KS2 .532
KS1 .489
LPS6 .499
LPS5 .535
LPS4 .527
LPS3 .464
LPS2 .515
LPS1 .460

Matrices (Group number 1 - Default model)

Implied (for all variables) Covariances (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
F4JI .801
F3CG -.092 .744
F2KS -.012 .465 .797
F1LPS .100 .361 .410 .645
JI1 .738 -.085 -.011 .093 1.419
JI2 .783 -.090 -.012 .098 .721 1.468
JI3 .803 -.093 -.012 .101 .740 .785 1.482
JI4 .801 -.092 -.012 .100 .738 .783 .803 1.425
CG15 -.098 .790 .494 .384 -.090 -.096 -.098 -.098 1.608
CG14 -.094 .755 .472 .367 -.086 -.092 -.094 -.094 .802 1.468
CG13 -.097 .784 .490 .381 -.090 -.095 -.098 -.097 .833 .796 1.480
CG12 -.090 .721 .451 .350 -.083 -.087 -.090 -.090 .766 .732 .760 1.498
CG11 -.102 .821 .514 .399 -.094 -.100 -.102 -.102 .872 .834 .866 .796 1.541
CG10 -.094 .754 .472 .366 -.086 -.091 -.094 -.094 .801 .766 .795 .731 .833 1.493
CG9 -.099 .794 .497 .386 -.091 -.096 -.099 -.099 .843 .806 .837 .770 .877 .805 1.532
CG8 -.084 .680 .425 .330 -.078 -.082 -.085 -.084 .722 .690 .716 .659 .750 .689 .725 1.534
CG7 -.093 .748 .468 .363 -.086 -.091 -.093 -.093 .795 .760 .789 .725 .826 .758 .798 .683 1.572
CG6 -.090 .722 .451 .350 -.083 -.088 -.090 -.090 .766 .733 .760 .700 .796 .731 .770 .659 .725 1.551
CG5 -.098 .792 .495 .385 -.091 -.096 -.098 -.098 .841 .804 .835 .768 .874 .803 .845 .723 .796 .768 1.612
CG4 -.090 .728 .455 .353 -.083 -.088 -.090 -.090 .773 .739 .767 .705 .803 .737 .776 .665 .731 .705 .774 1.579
CG3 -.091 .731 .457 .355 -.084 -.089 -.091 -.091 .776 .742 .770 .709 .807 .741 .780 .668 .735 .709 .778 .714 1.551
CG2 -.092 .739 .462 .359 -.085 -.090 -.092 -.092 .785 .751 .779 .717 .816 .749 .789 .675 .743 .717 .787 .723 .726 1.521
CG1 -.092 .744 .465 .361 -.085 -.090 -.093 -.092 .790 .755 .784 .721 .821 .754 .794 .680 .748 .722 .792 .728 .731 .739 1.482
KS5 -.012 .454 .778 .401 -.011 -.011 -.012 -.012 .482 .461 .479 .440 .501 .460 .485 .415 .457 .440 .483 .444 .446 .451 .454 1.481
KS4 -.012 .470 .805 .414 -.011 -.012 -.012 -.012 .499 .477 .495 .455 .519 .476 .501 .429 .472 .456 .500 .459 .461 .467 .470 .785 1.497
KS3 -.011 .430 .737 .379 -.010 -.011 -.011 -.011 .457 .437 .453 .417 .475 .436 .459 .393 .433 .417 .458 .421 .422 .427 .430 .719 .744 1.530
KS2 -.012 .478 .818 .421 -.011 -.012 -.012 -.012 .507 .485 .504 .463 .527 .484 .510 .436 .480 .463 .508 .467 .469 .475 .478 .799 .826 .756 1.579
KS1 -.012 .465 .797 .410 -.011 -.012 -.012 -.012 .494 .472 .490 .451 .514 .472 .497 .425 .468 .451 .495 .455 .457 .462 .465 .778 .805 .737 .818 1.629
LPS6 .104 .374 .425 .668 .096 .102 .104 .104 .397 .380 .394 .363 .413 .379 .399 .342 .376 .363 .398 .366 .367 .372 .374 .415 .429 .393 .436 .425 1.385
LPS5 .110 .395 .448 .705 .101 .107 .110 .110 .419 .401 .416 .383 .436 .400 .421 .361 .397 .383 .420 .386 .388 .392 .395 .438 .453 .414 .460 .448 .730 1.439
LPS4 .105 .377 .428 .674 .097 .102 .105 .105 .401 .383 .398 .366 .416 .382 .402 .345 .379 .366 .401 .369 .370 .375 .377 .418 .432 .396 .440 .428 .697 .736 1.335
LPS3 .100 .361 .410 .644 .092 .098 .100 .100 .383 .366 .380 .350 .398 .366 .385 .330 .363 .350 .384 .353 .354 .358 .361 .400 .414 .379 .421 .410 .667 .704 .673 1.387
LPS2 .106 .382 .434 .682 .098 .104 .106 .106 .406 .388 .402 .370 .421 .387 .408 .349 .384 .370 .406 .373 .375 .379 .382 .423 .438 .401 .445 .434 .706 .745 .712 .681 1.398
LPS1 .100 .361 .410 .645 .093 .098 .101 .100 .384 .367 .381 .350 .399 .366 .386 .330 .363 .350 .385 .353 .355 .359 .361 .401 .414 .379 .421 .410 .668 .705 .674 .644 .682 1.402

Implied (for all variables) Correlations (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
F4JI 1.000
F3CG -.120 1.000
F2KS -.015 .604 1.000
F1LPS .140 .521 .572 1.000
JI1 .692 -.083 -.010 .097 1.000
JI2 .722 -.086 -.011 .101 .500 1.000
JI3 .737 -.088 -.011 .103 .510 .532 1.000
JI4 .750 -.090 -.011 .105 .519 .541 .553 1.000
CG15 -.086 .723 .437 .377 -.060 -.062 -.064 -.065 1.000
CG14 -.086 .723 .437 .377 -.060 -.062 -.064 -.065 .522 1.000
CG13 -.089 .747 .452 .390 -.062 -.065 -.066 -.067 .540 .540 1.000
CG12 -.082 .683 .413 .356 -.057 -.059 -.060 -.061 .494 .494 .511 1.000
CG11 -.092 .767 .463 .400 -.064 -.066 -.068 -.069 .554 .554 .573 .524 1.000
CG10 -.086 .716 .432 .373 -.059 -.062 -.063 -.064 .517 .517 .535 .489 .549 1.000
CG9 -.089 .744 .449 .388 -.062 -.064 -.066 -.067 .537 .538 .556 .508 .570 .532 1.000
CG8 -.076 .636 .384 .332 -.053 -.055 -.056 -.057 .460 .460 .476 .435 .488 .455 .473 1.000
CG7 -.083 .692 .418 .361 -.057 -.060 -.061 -.062 .500 .500 .517 .473 .531 .495 .515 .440 1.000
CG6 -.080 .672 .406 .350 -.056 -.058 -.059 -.060 .485 .486 .502 .459 .515 .481 .500 .427 .465 1.000
CG5 -.086 .723 .437 .377 -.060 -.062 -.064 -.065 .522 .523 .540 .494 .554 .517 .538 .460 .500 .486 1.000
CG4 -.080 .671 .406 .350 -.056 -.058 -.059 -.060 .485 .485 .502 .459 .515 .480 .499 .427 .464 .451 .485 1.000
CG3 -.081 .680 .411 .355 -.056 -.059 -.060 -.061 .492 .492 .508 .465 .522 .487 .506 .433 .471 .457 .492 .457 1.000
CG2 -.083 .695 .420 .362 -.058 -.060 -.061 -.062 .502 .502 .519 .475 .533 .497 .517 .442 .481 .467 .502 .466 .473 1.000
CG1 -.085 .709 .428 .369 -.059 -.061 -.062 -.064 .512 .512 .530 .484 .543 .507 .527 .451 .490 .476 .512 .476 .482 .492 1.000
KS5 -.011 .433 .716 .410 -.007 -.008 -.008 -.008 .313 .313 .323 .296 .332 .310 .322 .275 .299 .291 .313 .290 .294 .301 .307 1.000
KS4 -.011 .445 .737 .421 -.008 -.008 -.008 -.008 .322 .322 .333 .304 .341 .318 .331 .283 .308 .299 .322 .299 .303 .309 .315 .527 1.000
KS3 -.010 .403 .667 .382 -.007 -.007 -.007 -.007 .291 .291 .301 .275 .309 .288 .300 .256 .279 .271 .291 .271 .274 .280 .286 .478 .491 1.000
KS2 -.011 .441 .729 .417 -.007 -.008 -.008 -.008 .318 .319 .329 .301 .338 .315 .328 .280 .305 .296 .319 .296 .300 .306 .312 .522 .537 .487 1.000
KS1 -.010 .423 .700 .400 -.007 -.007 -.008 -.008 .305 .306 .316 .289 .324 .302 .314 .269 .292 .284 .306 .284 .288 .294 .300 .501 .515 .467 .510 1.000
LPS6 .099 .368 .404 .707 .068 .071 .073 .074 .266 .266 .275 .252 .283 .264 .274 .234 .255 .247 .266 .247 .251 .256 .261 .289 .298 .270 .295 .283 1.000
LPS5 .102 .381 .419 .732 .071 .074 .075 .077 .276 .276 .285 .261 .293 .273 .284 .243 .264 .256 .276 .256 .260 .265 .270 .300 .308 .279 .305 .293 .517 1.000
LPS4 .101 .378 .415 .726 .070 .073 .075 .076 .273 .274 .283 .259 .290 .271 .281 .241 .262 .254 .274 .254 .257 .263 .268 .297 .306 .277 .303 .290 .513 .531 1.000
LPS3 .095 .355 .390 .681 .066 .069 .070 .071 .257 .257 .265 .243 .272 .254 .264 .226 .246 .239 .257 .238 .242 .247 .252 .279 .287 .260 .284 .273 .481 .498 .494 1.000
LPS2 .100 .374 .411 .718 .069 .072 .074 .075 .270 .271 .280 .256 .287 .268 .278 .238 .259 .251 .271 .251 .255 .260 .265 .294 .303 .274 .300 .287 .507 .525 .521 .489 1.000
LPS1 .095 .354 .388 .679 .066 .068 .070 .071 .256 .256 .264 .242 .271 .253 .263 .225 .245 .238 .256 .237 .241 .246 .251 .278 .286 .259 .283 .272 .479 .496 .492 .462 .487 1.000

Implied Covariances (Group number 1 - Default model)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 1.419
JI2 .721 1.468
JI3 .740 .785 1.482
JI4 .738 .783 .803 1.425
CG15 -.090 -.096 -.098 -.098 1.608
CG14 -.086 -.092 -.094 -.094 .802 1.468
CG13 -.090 -.095 -.098 -.097 .833 .796 1.480
CG12 -.083 -.087 -.090 -.090 .766 .732 .760 1.498
CG11 -.094 -.100 -.102 -.102 .872 .834 .866 .796 1.541
CG10 -.086 -.091 -.094 -.094 .801 .766 .795 .731 .833 1.493
CG9 -.091 -.096 -.099 -.099 .843 .806 .837 .770 .877 .805 1.532
CG8 -.078 -.082 -.085 -.084 .722 .690 .716 .659 .750 .689 .725 1.534
CG7 -.086 -.091 -.093 -.093 .795 .760 .789 .725 .826 .758 .798 .683 1.572
CG6 -.083 -.088 -.090 -.090 .766 .733 .760 .700 .796 .731 .770 .659 .725 1.551
CG5 -.091 -.096 -.098 -.098 .841 .804 .835 .768 .874 .803 .845 .723 .796 .768 1.612
CG4 -.083 -.088 -.090 -.090 .773 .739 .767 .705 .803 .737 .776 .665 .731 .705 .774 1.579
CG3 -.084 -.089 -.091 -.091 .776 .742 .770 .709 .807 .741 .780 .668 .735 .709 .778 .714 1.551
CG2 -.085 -.090 -.092 -.092 .785 .751 .779 .717 .816 .749 .789 .675 .743 .717 .787 .723 .726 1.521
CG1 -.085 -.090 -.093 -.092 .790 .755 .784 .721 .821 .754 .794 .680 .748 .722 .792 .728 .731 .739 1.482
KS5 -.011 -.011 -.012 -.012 .482 .461 .479 .440 .501 .460 .485 .415 .457 .440 .483 .444 .446 .451 .454 1.481
KS4 -.011 -.012 -.012 -.012 .499 .477 .495 .455 .519 .476 .501 .429 .472 .456 .500 .459 .461 .467 .470 .785 1.497
KS3 -.010 -.011 -.011 -.011 .457 .437 .453 .417 .475 .436 .459 .393 .433 .417 .458 .421 .422 .427 .430 .719 .744 1.530
KS2 -.011 -.012 -.012 -.012 .507 .485 .504 .463 .527 .484 .510 .436 .480 .463 .508 .467 .469 .475 .478 .799 .826 .756 1.579
KS1 -.011 -.012 -.012 -.012 .494 .472 .490 .451 .514 .472 .497 .425 .468 .451 .495 .455 .457 .462 .465 .778 .805 .737 .818 1.629
LPS6 .096 .102 .104 .104 .397 .380 .394 .363 .413 .379 .399 .342 .376 .363 .398 .366 .367 .372 .374 .415 .429 .393 .436 .425 1.385
LPS5 .101 .107 .110 .110 .419 .401 .416 .383 .436 .400 .421 .361 .397 .383 .420 .386 .388 .392 .395 .438 .453 .414 .460 .448 .730 1.439
LPS4 .097 .102 .105 .105 .401 .383 .398 .366 .416 .382 .402 .345 .379 .366 .401 .369 .370 .375 .377 .418 .432 .396 .440 .428 .697 .736 1.335
LPS3 .092 .098 .100 .100 .383 .366 .380 .350 .398 .366 .385 .330 .363 .350 .384 .353 .354 .358 .361 .400 .414 .379 .421 .410 .667 .704 .673 1.387
LPS2 .098 .104 .106 .106 .406 .388 .402 .370 .421 .387 .408 .349 .384 .370 .406 .373 .375 .379 .382 .423 .438 .401 .445 .434 .706 .745 .712 .681 1.398
LPS1 .093 .098 .101 .100 .384 .367 .381 .350 .399 .366 .386 .330 .363 .350 .385 .353 .355 .359 .361 .401 .414 .379 .421 .410 .668 .705 .674 .644 .682 1.402

Implied Correlations (Group number 1 - Default model)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 1.000
JI2 .500 1.000
JI3 .510 .532 1.000
JI4 .519 .541 .553 1.000
CG15 -.060 -.062 -.064 -.065 1.000
CG14 -.060 -.062 -.064 -.065 .522 1.000
CG13 -.062 -.065 -.066 -.067 .540 .540 1.000
CG12 -.057 -.059 -.060 -.061 .494 .494 .511 1.000
CG11 -.064 -.066 -.068 -.069 .554 .554 .573 .524 1.000
CG10 -.059 -.062 -.063 -.064 .517 .517 .535 .489 .549 1.000
CG9 -.062 -.064 -.066 -.067 .537 .538 .556 .508 .570 .532 1.000
CG8 -.053 -.055 -.056 -.057 .460 .460 .476 .435 .488 .455 .473 1.000
CG7 -.057 -.060 -.061 -.062 .500 .500 .517 .473 .531 .495 .515 .440 1.000
CG6 -.056 -.058 -.059 -.060 .485 .486 .502 .459 .515 .481 .500 .427 .465 1.000
CG5 -.060 -.062 -.064 -.065 .522 .523 .540 .494 .554 .517 .538 .460 .500 .486 1.000
CG4 -.056 -.058 -.059 -.060 .485 .485 .502 .459 .515 .480 .499 .427 .464 .451 .485 1.000
CG3 -.056 -.059 -.060 -.061 .492 .492 .508 .465 .522 .487 .506 .433 .471 .457 .492 .457 1.000
CG2 -.058 -.060 -.061 -.062 .502 .502 .519 .475 .533 .497 .517 .442 .481 .467 .502 .466 .473 1.000
CG1 -.059 -.061 -.062 -.064 .512 .512 .530 .484 .543 .507 .527 .451 .490 .476 .512 .476 .482 .492 1.000
KS5 -.007 -.008 -.008 -.008 .313 .313 .323 .296 .332 .310 .322 .275 .299 .291 .313 .290 .294 .301 .307 1.000
KS4 -.008 -.008 -.008 -.008 .322 .322 .333 .304 .341 .318 .331 .283 .308 .299 .322 .299 .303 .309 .315 .527 1.000
KS3 -.007 -.007 -.007 -.007 .291 .291 .301 .275 .309 .288 .300 .256 .279 .271 .291 .271 .274 .280 .286 .478 .491 1.000
KS2 -.007 -.008 -.008 -.008 .318 .319 .329 .301 .338 .315 .328 .280 .305 .296 .319 .296 .300 .306 .312 .522 .537 .487 1.000
KS1 -.007 -.007 -.008 -.008 .305 .306 .316 .289 .324 .302 .314 .269 .292 .284 .306 .284 .288 .294 .300 .501 .515 .467 .510 1.000
LPS6 .068 .071 .073 .074 .266 .266 .275 .252 .283 .264 .274 .234 .255 .247 .266 .247 .251 .256 .261 .289 .298 .270 .295 .283 1.000
LPS5 .071 .074 .075 .077 .276 .276 .285 .261 .293 .273 .284 .243 .264 .256 .276 .256 .260 .265 .270 .300 .308 .279 .305 .293 .517 1.000
LPS4 .070 .073 .075 .076 .273 .274 .283 .259 .290 .271 .281 .241 .262 .254 .274 .254 .257 .263 .268 .297 .306 .277 .303 .290 .513 .531 1.000
LPS3 .066 .069 .070 .071 .257 .257 .265 .243 .272 .254 .264 .226 .246 .239 .257 .238 .242 .247 .252 .279 .287 .260 .284 .273 .481 .498 .494 1.000
LPS2 .069 .072 .074 .075 .270 .271 .280 .256 .287 .268 .278 .238 .259 .251 .271 .251 .255 .260 .265 .294 .303 .274 .300 .287 .507 .525 .521 .489 1.000
LPS1 .066 .068 .070 .071 .256 .256 .264 .242 .271 .253 .263 .225 .245 .238 .256 .237 .241 .246 .251 .278 .286 .259 .283 .272 .479 .496 .492 .462 .487 1.000

Residual Covariances (Group number 1 - Default model)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 .000
JI2 -.013 .000
JI3 .028 -.011 .000
JI4 -.011 .013 -.006 .000
CG15 .093 .035 -.020 .099 .000
CG14 -.053 -.085 -.083 .030 -.036 .000
CG13 .062 -.003 -.015 .030 -.017 .033 .000
CG12 -.084 -.052 -.039 .049 -.050 -.042 .067 .000
CG11 .093 -.020 -.010 .063 .019 .001 -.019 .041 .000
CG10 -.011 -.022 -.133 .042 -.083 -.040 -.015 .032 .038 .000
CG9 .035 -.044 -.066 .034 .029 -.026 -.019 -.030 .015 .088 .000
CG8 .034 .020 .013 -.009 .011 .029 .021 -.023 -.041 -.095 -.021 .000
CG7 -.021 .046 -.013 .097 .007 .032 .018 -.016 -.065 -.011 -.035 .059 .000
CG6 .042 -.069 .002 .032 .042 -.028 -.018 -.049 .005 .004 .013 .017 -.047 .000
CG5 .043 -.009 .001 .090 .015 .033 -.026 -.056 .020 -.018 -.010 .000 .019 .000 .000
CG4 .099 .015 .043 .137 .029 -.034 -.036 .035 .043 .052 .027 -.019 -.020 .053 -.011 .000
CG3 -.004 -.076 -.155 -.089 .047 -.014 .022 .093 -.036 .030 -.031 .032 .048 .048 -.035 -.076 .000
CG2 -.034 -.078 -.062 .083 -.026 .001 -.044 .006 -.047 .065 -.019 .008 .056 .021 .036 .005 .009 .000
CG1 -.039 -.093 -.045 .013 .025 .029 .050 -.033 -.013 -.068 .003 .051 .020 -.024 .005 -.011 -.081 .003 .000
KS5 .013 .053 -.010 .041 .080 .080 .009 .017 .099 .056 .052 -.013 .023 .006 .072 .076 .046 -.043 .037 .000
KS4 -.006 -.013 -.010 .051 -.061 .029 .004 .061 -.046 -.014 .026 -.009 -.073 -.007 -.009 -.046 -.045 -.064 .000 -.021 .000
KS3 -.011 .041 -.075 .003 .016 .017 -.001 -.021 .061 .017 .043 -.062 -.014 -.039 .064 -.090 -.002 -.074 .026 .005 .023 .000
KS2 -.083 -.047 -.026 .040 -.051 .028 -.009 -.028 .009 .000 .006 -.030 -.052 -.016 .019 -.105 -.081 -.068 .016 .000 .016 -.042 .000
KS1 .040 -.030 .065 -.058 -.019 -.002 .034 .012 .052 .015 .038 -.042 -.092 -.033 -.003 -.070 -.029 -.028 .034 -.021 .009 .011 .019 .000
LPS6 -.017 -.027 -.122 .018 .012 .060 -.030 .022 .022 -.023 -.035 .108 -.038 -.051 .006 -.035 -.029 -.020 -.007 .061 .040 .029 .027 .008 .000
LPS5 .047 .037 -.025 .113 .096 .065 .073 .021 .021 .008 -.020 -.001 .012 -.040 .086 -.067 -.026 .018 .109 -.004 -.021 -.035 .074 -.018 -.056 .000
LPS4 -.010 -.001 -.098 .004 .089 .050 .042 -.002 .081 -.010 -.027 .006 .056 .001 -.009 .045 -.028 -.009 .034 .072 -.065 .009 .028 .013 .002 .010 .000
LPS3 -.020 -.018 -.106 -.018 .035 .066 -.005 .030 .030 -.036 -.031 .068 -.010 -.030 .058 -.057 -.064 -.012 .046 -.016 -.004 .017 -.028 -.027 .039 .002 -.021 .000
LPS2 .035 .072 .010 .167 .060 .061 -.008 .064 .005 -.010 -.039 .025 -.041 -.029 -.032 .055 -.115 -.050 -.013 .004 -.027 -.002 -.001 -.089 -.012 .013 .015 -.029 .000
LPS1 -.045 -.029 -.105 .063 -.114 .049 -.145 -.100 .031 -.123 -.074 -.079 -.035 -.041 .001 -.089 -.135 -.059 -.023 -.046 -.043 .020 .054 -.049 .034 .006 -.024 .027 .003 .000

Standardized Residual Covariances (Group number 1 - Default model)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
JI1 .000
JI2 -.167 .000
JI3 .356 -.131 .000
JI4 -.147 .165 -.073 .000
CG15 1.264 .464 -.260 1.335 .000
CG14 -.752 -1.194 -1.156 .427 -.431 .000
CG13 .881 -.041 -.206 .425 -.204 .411 .000
CG12 -1.178 -.724 -.530 .689 -.593 -.524 .819 .000
CG11 1.289 -.270 -.135 .869 .218 .008 -.226 .489 .000
CG10 -.157 -.304 -1.839 .588 -.972 -.495 -.187 .389 .448 .000
CG9 .487 -.606 -.894 .467 .334 -.315 -.229 -.366 .174 1.058 .000
CG8 .478 .273 .174 -.123 .131 .355 .262 -.284 -.495 -1.178 -.251 .000
CG7 -.288 .616 -.181 1.334 .079 .391 .210 -.195 -.754 -.136 -.410 .712 .000
CG6 .577 -.942 .022 .441 .495 -.343 -.219 -.603 .057 .049 .151 .207 -.561 .000
CG5 .578 -.117 .012 1.222 .169 .391 -.303 -.662 .222 -.206 -.119 .000 .225 .006 .000
CG4 1.363 .196 .575 1.875 .335 -.414 -.435 .421 .506 .630 .325 -.234 -.235 .639 -.122 .000
CG3 -.049 -1.039 -2.092 -1.221 .544 -.169 .262 1.134 -.423 .361 -.363 .394 .568 .578 -.411 -.909 .000
CG2 -.472 -1.067 -.842 1.155 -.310 .013 -.541 .071 -.560 .796 -.224 .101 .668 .255 .426 .066 .109 .000
CG1 -.546 -1.290 -.628 .188 .301 .364 .617 -.413 -.156 -.843 .033 .628 .243 -.297 .057 -.137 -.984 .041 .000
KS5 .184 .741 -.138 .586 1.023 1.068 .116 .221 1.273 .738 .681 -.177 .298 .079 .913 .979 .596 -.570 .492 .000
KS4 -.080 -.178 -.134 .718 -.774 .383 .055 .797 -.592 -.186 .340 -.115 -.933 -.087 -.107 -.586 -.582 -.827 .003 -.252 .000
KS3 -.158 .556 -1.017 .039 .203 .227 -.018 -.280 .783 .216 .548 -.811 -.181 -.505 .808 -1.145 -.019 -.958 .336 .066 .282 .000
KS2 -1.144 -.638 -.345 .555 -.628 .359 -.121 -.361 .116 .004 .070 -.379 -.654 -.207 .231 -1.315 -1.024 -.862 .209 .006 .184 -.504 .000
KS1 .541 -.399 .856 -.783 -.232 -.026 .431 .150 .636 .188 .465 -.527 -1.133 -.406 -.040 -.857 -.362 -.347 .426 -.247 .107 .127 .213 .000
LPS6 -.246 -.389 -1.745 .261 .155 .834 -.421 .309 .302 -.313 -.480 1.477 -.513 -.687 .084 -.468 -.399 -.273 -.104 .842 .544 .390 .363 .101 .000
LPS5 .677 .520 -.354 1.610 1.250 .884 .987 .283 .277 .109 -.263 -.013 .160 -.529 1.120 -.880 -.347 .245 1.481 -.053 -.285 -.469 .966 -.236 -.730 .000
LPS4 -.145 -.013 -1.433 .058 1.200 .703 .588 -.031 1.107 -.134 -.373 .079 .766 .013 -.117 .618 -.384 -.128 .486 1.012 -.903 .129 .384 .180 .028 .137 .000
LPS3 -.288 -.254 -1.510 -.256 .468 .924 -.066 .418 .407 -.491 -.420 .932 -.141 -.415 .777 -.771 -.867 -.162 .641 -.221 -.050 .232 -.376 -.357 .526 .029 -.281 .000
LPS2 .506 1.032 .141 2.421 .794 .850 -.113 .878 .062 -.136 -.531 .343 -.551 -.399 -.428 .734 -1.557 -.677 -.174 .050 -.372 -.031 -.010 -1.163 -.151 .165 .204 -.391 .000
LPS1 -.651 -.413 -1.491 .919 -1.515 .682 -1.994 -1.377 .424 -1.694 -.999 -1.086 -.467 -.551 .012 -1.189 -1.830 -.807 -.321 -.627 -.584 .277 .716 -.640 .450 .082 -.320 .358 .043 .000

Factor Score Weights (Group number 1 - Default model)

JI1 JI2 JI3 JI4 CG15 CG14 CG13 CG12 CG11 CG10 CG9 CG8 CG7 CG6 CG5 CG4 CG3 CG2 CG1 KS5 KS4 KS3 KS2 KS1 LPS6 LPS5 LPS4 LPS3 LPS2 LPS1
F4JI .180 .201 .214 .232 -.003 -.003 -.003 -.003 -.004 -.003 -.003 -.002 -.003 -.002 -.003 -.002 -.002 -.003 -.003 .000 .000 .000 .000 .000 .007 .008 .008 .007 .008 .007
F3CG -.003 -.003 -.003 -.003 .061 .064 .071 .054 .077 .062 .069 .044 .054 .050 .061 .050 .052 .056 .060 .007 .008 .006 .007 .006 .005 .005 .005 .004 .005 .004
F2KS .000 .000 .000 .000 .007 .008 .009 .007 .009 .007 .008 .005 .007 .006 .007 .006 .006 .007 .007 .157 .171 .126 .161 .139 .014 .015 .015 .012 .014 .012
F1LPS .006 .007 .007 .008 .004 .005 .005 .004 .005 .004 .005 .003 .004 .004 .004 .004 .004 .004 .004 .012 .014 .010 .013 .011 .126 .138 .140 .113 .132 .112

Total Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .921 .000 .000 .000
JI2 .977 .000 .000 .000
JI3 1.002 .000 .000 .000
JI4 1.000 .000 .000 .000
CG15 .000 1.062 .000 .000
CG14 .000 1.015 .000 .000
CG13 .000 1.054 .000 .000
CG12 .000 .970 .000 .000
CG11 .000 1.104 .000 .000
CG10 .000 1.014 .000 .000
CG9 .000 1.067 .000 .000
CG8 .000 .913 .000 .000
CG7 .000 1.005 .000 .000
CG6 .000 .970 .000 .000
CG5 .000 1.064 .000 .000
CG4 .000 .978 .000 .000
CG3 .000 .982 .000 .000
CG2 .000 .994 .000 .000
CG1 .000 1.000 .000 .000
KS5 .000 .000 .976 .000
KS4 .000 .000 1.010 .000
KS3 .000 .000 .924 .000
KS2 .000 .000 1.027 .000
KS1 .000 .000 1.000 .000
LPS6 .000 .000 .000 1.035
LPS5 .000 .000 .000 1.092
LPS4 .000 .000 .000 1.044
LPS3 .000 .000 .000 .998
LPS2 .000 .000 .000 1.057
LPS1 .000 .000 .000 1.000

Standardized Total Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .692 .000 .000 .000
JI2 .722 .000 .000 .000
JI3 .737 .000 .000 .000
JI4 .750 .000 .000 .000
CG15 .000 .723 .000 .000
CG14 .000 .723 .000 .000
CG13 .000 .747 .000 .000
CG12 .000 .683 .000 .000
CG11 .000 .767 .000 .000
CG10 .000 .716 .000 .000
CG9 .000 .744 .000 .000
CG8 .000 .636 .000 .000
CG7 .000 .692 .000 .000
CG6 .000 .672 .000 .000
CG5 .000 .723 .000 .000
CG4 .000 .671 .000 .000
CG3 .000 .680 .000 .000
CG2 .000 .695 .000 .000
CG1 .000 .709 .000 .000
KS5 .000 .000 .716 .000
KS4 .000 .000 .737 .000
KS3 .000 .000 .667 .000
KS2 .000 .000 .729 .000
KS1 .000 .000 .700 .000
LPS6 .000 .000 .000 .707
LPS5 .000 .000 .000 .732
LPS4 .000 .000 .000 .726
LPS3 .000 .000 .000 .681
LPS2 .000 .000 .000 .718
LPS1 .000 .000 .000 .679

Direct Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .921 .000 .000 .000
JI2 .977 .000 .000 .000
JI3 1.002 .000 .000 .000
JI4 1.000 .000 .000 .000
CG15 .000 1.062 .000 .000
CG14 .000 1.015 .000 .000
CG13 .000 1.054 .000 .000
CG12 .000 .970 .000 .000
CG11 .000 1.104 .000 .000
CG10 .000 1.014 .000 .000
CG9 .000 1.067 .000 .000
CG8 .000 .913 .000 .000
CG7 .000 1.005 .000 .000
CG6 .000 .970 .000 .000
CG5 .000 1.064 .000 .000
CG4 .000 .978 .000 .000
CG3 .000 .982 .000 .000
CG2 .000 .994 .000 .000
CG1 .000 1.000 .000 .000
KS5 .000 .000 .976 .000
KS4 .000 .000 1.010 .000
KS3 .000 .000 .924 .000
KS2 .000 .000 1.027 .000
KS1 .000 .000 1.000 .000
LPS6 .000 .000 .000 1.035
LPS5 .000 .000 .000 1.092
LPS4 .000 .000 .000 1.044
LPS3 .000 .000 .000 .998
LPS2 .000 .000 .000 1.057
LPS1 .000 .000 .000 1.000

Standardized Direct Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .692 .000 .000 .000
JI2 .722 .000 .000 .000
JI3 .737 .000 .000 .000
JI4 .750 .000 .000 .000
CG15 .000 .723 .000 .000
CG14 .000 .723 .000 .000
CG13 .000 .747 .000 .000
CG12 .000 .683 .000 .000
CG11 .000 .767 .000 .000
CG10 .000 .716 .000 .000
CG9 .000 .744 .000 .000
CG8 .000 .636 .000 .000
CG7 .000 .692 .000 .000
CG6 .000 .672 .000 .000
CG5 .000 .723 .000 .000
CG4 .000 .671 .000 .000
CG3 .000 .680 .000 .000
CG2 .000 .695 .000 .000
CG1 .000 .709 .000 .000
KS5 .000 .000 .716 .000
KS4 .000 .000 .737 .000
KS3 .000 .000 .667 .000
KS2 .000 .000 .729 .000
KS1 .000 .000 .700 .000
LPS6 .000 .000 .000 .707
LPS5 .000 .000 .000 .732
LPS4 .000 .000 .000 .726
LPS3 .000 .000 .000 .681
LPS2 .000 .000 .000 .718
LPS1 .000 .000 .000 .679

Indirect Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .000 .000 .000 .000
JI2 .000 .000 .000 .000
JI3 .000 .000 .000 .000
JI4 .000 .000 .000 .000
CG15 .000 .000 .000 .000
CG14 .000 .000 .000 .000
CG13 .000 .000 .000 .000
CG12 .000 .000 .000 .000
CG11 .000 .000 .000 .000
CG10 .000 .000 .000 .000
CG9 .000 .000 .000 .000
CG8 .000 .000 .000 .000
CG7 .000 .000 .000 .000
CG6 .000 .000 .000 .000
CG5 .000 .000 .000 .000
CG4 .000 .000 .000 .000
CG3 .000 .000 .000 .000
CG2 .000 .000 .000 .000
CG1 .000 .000 .000 .000
KS5 .000 .000 .000 .000
KS4 .000 .000 .000 .000
KS3 .000 .000 .000 .000
KS2 .000 .000 .000 .000
KS1 .000 .000 .000 .000
LPS6 .000 .000 .000 .000
LPS5 .000 .000 .000 .000
LPS4 .000 .000 .000 .000
LPS3 .000 .000 .000 .000
LPS2 .000 .000 .000 .000
LPS1 .000 .000 .000 .000

Standardized Indirect Effects (Group number 1 - Default model)

F4JI F3CG F2KS F1LPS
JI1 .000 .000 .000 .000
JI2 .000 .000 .000 .000
JI3 .000 .000 .000 .000
JI4 .000 .000 .000 .000
CG15 .000 .000 .000 .000
CG14 .000 .000 .000 .000
CG13 .000 .000 .000 .000
CG12 .000 .000 .000 .000
CG11 .000 .000 .000 .000
CG10 .000 .000 .000 .000
CG9 .000 .000 .000 .000
CG8 .000 .000 .000 .000
CG7 .000 .000 .000 .000
CG6 .000 .000 .000 .000
CG5 .000 .000 .000 .000
CG4 .000 .000 .000 .000
CG3 .000 .000 .000 .000
CG2 .000 .000 .000 .000
CG1 .000 .000 .000 .000
KS5 .000 .000 .000 .000
KS4 .000 .000 .000 .000
KS3 .000 .000 .000 .000
KS2 .000 .000 .000 .000
KS1 .000 .000 .000 .000
LPS6 .000 .000 .000 .000
LPS5 .000 .000 .000 .000
LPS4 .000 .000 .000 .000
LPS3 .000 .000 .000 .000
LPS2 .000 .000 .000 .000
LPS1 .000 .000 .000 .000

Modification Indices (Group number 1 - Default model)

Covariances: (Group number 1 - Default model)

M.I. Par Change
e28 <--> F1LPS 4.662 -.070
e23 <--> e30 5.699 -.100
e23 <--> e24 4.083 .076
e21 <--> e28 5.451 -.093
e21 <--> e26 5.832 -.094
e20 <--> e21 7.546 .101
e19 <--> e21 6.395 -.106
e14 <--> e23 6.052 .103
e12 <--> e21 4.153 -.077
e12 <--> e14 4.985 -.090
e11 <--> e15 4.559 .092
e10 <--> e23 4.423 .086
e10 <--> e22 4.092 -.075
e7 <--> e28 7.479 .121
e7 <--> e27 8.416 -.125
e6 <--> e19 6.935 .112
e5 <--> e15 4.550 -.088
e5 <--> e12 4.392 .080
e4 <--> e10 4.677 -.082
e2 <--> F4JI 4.362 .087
e2 <--> e27 4.209 .080
e2 <--> e15 5.325 .095
e1 <--> F3CG 4.378 -.067
e1 <--> e26 6.298 -.102
e1 <--> e24 7.272 -.102
e1 <--> e22 4.319 .078

Variances: (Group number 1 - Default model)

M.I. Par Change

Regression Weights: (Group number 1 - Default model)

M.I. Par Change
JI3 <--- F1LPS 4.770 -.132
JI3 <--- CG10 6.056 -.091
JI3 <--- LPS6 4.804 -.084
JI3 <--- LPS1 4.189 -.078
JI4 <--- CG2 5.741 .085
JI4 <--- LPS2 6.958 .098
JI4 <--- LPS1 4.241 .076
CG14 <--- LPS1 4.899 .079
CG11 <--- LPS1 4.229 .070
CG8 <--- LPS6 4.112 .082
CG3 <--- F4JI 4.278 -.116
CG3 <--- JI3 4.980 -.084
CG3 <--- JI4 6.555 -.098
CG3 <--- LPS2 4.590 -.083
KS5 <--- CG4 5.332 .083
LPS2 <--- F4JI 4.204 .109
LPS2 <--- JI4 6.664 .093
LPS1 <--- CG15 7.822 -.099
LPS1 <--- CG13 8.296 -.106
LPS1 <--- CG12 4.690 -.079
LPS1 <--- CG10 4.277 -.076

Minimization History (Default model)

Iteration Negative
eigenvalues		 Condition # Smallest
eigenvalue		 Diameter F NTries Ratio
0 e 8 -1.597 9999.000 5748.813 0 9999.000
1 e 9 -.134 3.733 2766.585 19 .199
2 e 1 -.111 2.772 717.109 5 .689
3 e 0 7745.898 .455 569.669 5 .717
4 e 0 242.945 .780 502.866 7 .000
5 e 0 199.649 .714 434.865 2 .000
6 e 0 212.267 .392 403.198 1 1.093
7 e 0 220.801 .135 401.721 1 1.078
8 e 0 213.064 .024 401.692 1 1.020
9 e 0 215.564 .001 401.692 1 1.001

Pairwise Parameter Comparisons (Default model)

Variance-covariance Matrix of Estimates (Default model)

par_1 par_2 par_3 par_4 par_5 par_6 par_7 par_8 par_9 par_10 par_11 par_12 par_13 par_14 par_15 par_16 par_17 par_18 par_19 par_20 par_21 par_22 par_23 par_24 par_25 par_26 par_27 par_28 par_29 par_30 par_31 par_32 par_33 par_34 par_35 par_36 par_37 par_38 par_39 par_40 par_41 par_42 par_43 par_44 par_45 par_46 par_47 par_48 par_49 par_50 par_51 par_52 par_53 par_54 par_55 par_56 par_57 par_58 par_59 par_60 par_61 par_62 par_63 par_64 par_65 par_66
par_1 .007
par_2 .003 .007
par_3 .004 .003 .007
par_4 .004 .004 .004 .007
par_5 .004 .003 .004 .004 .007
par_6 .000 .000 .000 .000 .000 .006
par_7 .000 .000 .000 .000 .000 .003 .006
par_8 .000 .000 .000 .000 .000 .003 .003 .006
par_9 .000 .000 .000 .000 .000 .003 .003 .003 .006
par_10 .000 .000 .000 .000 .000 .000 .000 .000 .000 .005
par_11 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .005
par_12 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .002 .005
par_13 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .005
par_14 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .002 .002 .003 .005
par_15 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .002 .003 .002 .005
par_16 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .002 .002 .002 .002 .002 .005
par_17 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .003 .003 .003 .002 .005
par_18 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .003 .003 .003 .002 .003 .005
par_19 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .003 .003 .003 .003 .003 .003 .005
par_20 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .002 .002 .003 .002 .002 .002 .003 .003 .003 .005
par_21 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .003 .003 .003 .002 .003 .003 .003 .003 .005
par_22 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .002 .003 .002 .003 .002 .003 .003 .003 .002 .003 .005
par_23 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .003 .003 .003 .003 .002 .003 .003 .003 .003 .003 .003 .005
par_24 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .006
par_25 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .006
par_26 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .002 .005
par_27 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_28 -.001 -.001 -.001 -.001 -.001 .000 .000 .000 .000 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 .000 .000 .000 .002 .002
par_29 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002
par_30 .000 .000 .000 .000 .000 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 -.001 .000 .000 .000 .002 .002 .000 .003
par_31 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .002
par_32 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .001 .002
par_33 -.005 -.004 -.005 -.005 -.004 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .003 .001 .001 .000 .000 .007
par_34 .000 .000 .000 .000 .000 -.005 -.005 -.005 -.005 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .001 .000 .004 .000 .000 .001 .011
par_35 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.004 -.004 -.004 -.004 -.004 -.004 -.003 -.004 -.004 -.004 -.004 -.004 -.004 -.004 .000 .000 .000 .001 .003 .000 .003 .000 -.001 .001 .001 .008
par_36 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.004 -.004 -.004 .000 .000 .001 .000 .000 -.001 .000 .000 .000 .010
par_37 .001 .001 .001 .001 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .004
par_38 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_39 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_40 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_41 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_42 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_43 .000 .000 .000 .000 .000 .001 .001 .001 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .005
par_44 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_45 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .005
par_46 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_47 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_48 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_49 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_50 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_51 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_52 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_53 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_54 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_55 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_56 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_57 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_58 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002
par_59 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_60 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002
par_61 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_62 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003
par_63 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .001 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.002 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_64 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.002 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .004
par_65 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004
par_66 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .004

Correlations of Estimates (Default model)

par_1 par_2 par_3 par_4 par_5 par_6 par_7 par_8 par_9 par_10 par_11 par_12 par_13 par_14 par_15 par_16 par_17 par_18 par_19 par_20 par_21 par_22 par_23 par_24 par_25 par_26 par_27 par_28 par_29 par_30 par_31 par_32 par_33 par_34 par_35 par_36 par_37 par_38 par_39 par_40 par_41 par_42 par_43 par_44 par_45 par_46 par_47 par_48 par_49 par_50 par_51 par_52 par_53 par_54 par_55 par_56 par_57 par_58 par_59 par_60 par_61 par_62 par_63 par_64 par_65 par_66
par_1 1.000
par_2 .518 1.000
par_3 .562 .527 1.000
par_4 .563 .532 .569 1.000
par_5 .537 .521 .548 .537 1.000
par_6 .000 -.002 -.002 .000 -.001 1.000
par_7 .000 .000 -.001 -.002 -.001 .484 1.000
par_8 .001 .000 -.002 .000 .001 .543 .504 1.000
par_9 .002 .000 .001 .000 .001 .529 .491 .529 1.000
par_10 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000
par_11 .000 .000 .000 .000 .000 .000 .000 .000 .001 .474 1.000
par_12 .000 .000 .000 -.001 .000 .000 .000 .000 .001 .465 .454 1.000
par_13 .000 .000 .000 .000 .000 .000 .001 .000 .001 .502 .490 .483 1.000
par_14 .000 .000 .000 -.001 .000 .000 .000 .001 .000 .467 .461 .453 .484 1.000
par_15 .000 .000 .000 -.001 .000 .001 .001 .000 .001 .481 .473 .461 .498 .461 1.000
par_16 .000 .000 .000 .000 .001 .000 .000 .001 .000 .439 .433 .423 .456 .426 .440 1.000
par_17 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .514 .505 .499 .535 .499 .510 .468 1.000
par_18 .000 .000 .000 .000 .000 .000 .000 .000 .000 .501 .490 .483 .516 .482 .493 .448 .537 1.000
par_19 .000 .000 .000 -.001 .000 .000 .000 -.001 .000 .528 .520 .516 .553 .514 .524 .482 .569 .551 1.000
par_20 .001 .000 .000 .000 .001 .000 .000 .001 .000 .474 .471 .460 .490 .456 .470 .431 .505 .492 .525 1.000
par_21 .001 .000 .001 .000 .000 .000 .000 .000 .000 .513 .509 .497 .535 .498 .514 .472 .551 .532 .568 .512 1.000
par_22 .000 .000 .000 -.001 .000 .000 .000 .001 .001 .499 .490 .481 .521 .482 .498 .457 .533 .514 .551 .490 .538 1.000
par_23 .001 .000 .001 .000 .001 .000 .000 .000 .001 .498 .493 .484 .520 .486 .497 .456 .536 .512 .552 .489 .535 .516 1.000
par_24 -.001 .001 .001 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .000 .000 .000 .000 1.000
par_25 .000 .001 .001 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 -.001 .000 .000 .472 1.000
par_26 -.001 .002 .001 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .484 .449 1.000
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par_40 -.016 -.001 -.176 -.013 -.010 .002 .001 .004 -.001 .000 .000 -.001 .000 .000 -.001 .000 .000 .000 .000 .000 .000 .000 -.001 -.002 -.002 -.002 .019 .015 .018 -.001 .000 .000 .030 -.001 .000 .002 -.044 -.021 -.042 1.000
par_41 -.004 .001 -.002 -.169 .026 -.002 .002 .000 .002 .001 .001 .002 .000 .001 .001 .001 .001 .001 .001 .001 .000 .001 .000 .001 .001 .001 .017 .006 -.014 -.001 .000 .000 .018 .000 -.001 -.001 -.025 -.022 -.028 -.028 1.000
par_42 .020 -.002 .015 .038 -.149 .001 .000 -.001 -.001 .000 -.001 .000 .000 .000 .000 -.001 -.001 -.001 .000 -.001 .000 .000 .000 -.001 .000 -.001 -.002 .009 .018 .000 .000 .000 .000 .000 .000 .001 .000 -.040 .004 -.031 -.080 1.000
par_43 .001 -.001 -.001 -.001 .000 .186 .172 .191 .197 .000 .000 .001 .001 .000 .001 .000 .000 .000 .000 .000 .000 .001 .000 .000 .000 .000 -.080 .000 .000 -.093 .003 .000 .000 -.202 .000 .000 -.001 -.003 .001 .002 .001 .000 1.000
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par_52 .001 .000 .001 .000 .001 -.001 -.001 .000 -.001 -.003 .005 .002 -.067 .001 -.001 .000 .002 .003 -.001 .007 .004 -.003 -.001 .000 .000 .000 .000 .000 .000 .000 .000 -.005 -.001 .001 .002 .000 .001 -.001 .001 -.001 .001 .000 -.001 .000 .001 -.001 .000 -.003 .003 -.011 -.006 1.000
par_53 .000 .001 .000 .001 .001 -.001 .000 -.001 .000 -.004 -.006 -.007 -.002 -.064 .002 -.003 -.004 -.003 -.003 .002 .000 .000 -.006 .000 .000 .000 .000 .006 .000 .005 .000 -.001 -.001 .000 .005 .000 .001 .000 .000 .000 -.001 .000 .000 .001 -.001 .001 -.001 -.008 .000 .004 .005 -.004 1.000
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par_58 .001 .001 .000 .001 .001 .001 .000 .002 .000 .005 .003 -.008 -.005 -.003 .007 .004 -.005 -.008 -.076 -.008 .001 -.002 -.005 .000 .000 .001 -.001 .000 .000 .000 .000 -.006 -.001 -.001 .006 .000 .001 -.001 .000 .001 -.001 .000 .001 .000 .001 -.003 .001 -.010 -.018 -.014 .005 -.002 -.004 -.023 -.013 -.002 .005 1.000
par_59 -.001 -.001 .000 .000 -.001 .000 .001 -.001 .001 -.004 -.012 -.006 .002 .001 -.002 -.001 -.001 -.007 -.008 -.067 -.010 .001 .001 .000 .000 -.001 .000 .004 .000 .003 .000 .002 .001 .000 .006 .000 -.001 .001 .000 -.001 .000 .000 .000 -.001 -.001 .002 -.001 -.010 -.003 .012 .002 -.015 -.011 -.006 -.006 -.011 .003 .004 1.000
par_60 -.001 -.001 -.001 -.002 -.001 .001 .001 .000 .002 .012 .003 .011 .010 .009 .004 .003 .010 .009 .010 -.002 -.064 .002 .009 .001 .001 .001 .000 .001 .000 -.002 .000 -.002 .001 -.001 -.004 -.001 -.002 .000 .000 .001 .001 -.001 .001 .000 .000 .001 -.002 .007 -.016 .000 -.013 -.013 -.009 -.001 .001 -.013 -.011 -.015 .010 1.000
par_61 .000 .000 .001 .000 .000 -.001 .000 -.001 -.001 .004 .005 .008 .000 .007 .000 .000 .008 .009 .005 .008 .000 -.064 .008 .000 -.001 -.001 .000 -.007 .000 -.003 .000 .006 .000 .001 -.002 .000 .000 .000 .000 -.001 .000 .000 -.001 .000 .000 .001 .000 .003 -.004 -.007 -.011 .003 -.009 .003 .002 -.014 -.015 -.007 -.013 .003 1.000
par_62 -.001 -.001 -.001 -.001 -.001 .000 .000 .001 -.001 .006 -.002 .000 .001 -.002 .002 .001 .000 .012 .001 .008 .005 .007 -.065 .000 .000 .000 .001 -.002 .000 .002 .000 -.007 .001 .000 -.001 .000 -.002 .001 .000 .001 .001 -.001 .000 -.001 .001 -.001 .001 .001 -.010 .004 .001 -.002 .004 -.003 -.001 .001 -.023 -.002 -.014 -.011 -.013 1.000
par_63 -.001 .002 .002 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 -.001 .000 .000 .330 .296 .305 .000 .000 -.093 .000 -.011 -.005 -.001 .000 .000 -.316 .001 .004 -.003 -.003 .002 -.001 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .001 .000 -.001 .001 -.001 .001 -.001 .000 1.000
par_64 .001 -.001 .000 .001 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .000 .000 -.322 -.008 -.052 .000 .000 .074 .000 .007 .015 .000 .000 .000 .087 .000 -.002 .001 .001 -.001 .000 .000 -.001 .000 .000 .001 -.001 .000 .001 .000 .000 -.001 .000 -.001 .000 .001 .000 .000 .000 .000 .000 -.140 1.000
par_65 -.001 -.001 -.001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .001 .000 .001 .000 .000 .000 .000 .001 .022 -.281 .014 .000 .000 .003 .000 -.001 .009 .001 .000 .000 .046 -.001 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .001 .000 .000 .000 .000 .001 -.001 .000 .000 -.001 .000 .000 .000 .001 .000 -.074 -.113 1.000
par_66 .001 -.001 -.001 .000 -.001 .000 .000 .000 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -.039 -.007 -.274 .000 .000 .017 .000 .006 -.020 .000 .000 .000 .065 -.001 -.002 .001 .001 .000 .001 .000 .000 .000 .000 .000 .000 .000 -.001 .000 .000 .000 .001 .000 .000 .000 -.001 .001 .000 .000 .000 -.104 -.025 -.083 1.000

Critical Ratios for Differences between Parameters (Default model)

par_1 par_2 par_3 par_4 par_5 par_6 par_7 par_8 par_9 par_10 par_11 par_12 par_13 par_14 par_15 par_16 par_17 par_18 par_19 par_20 par_21 par_22 par_23 par_24 par_25 par_26 par_27 par_28 par_29 par_30 par_31 par_32 par_33 par_34 par_35 par_36 par_37 par_38 par_39 par_40 par_41 par_42 par_43 par_44 par_45 par_46 par_47 par_48 par_49 par_50 par_51 par_52 par_53 par_54 par_55 par_56 par_57 par_58 par_59 par_60 par_61 par_62 par_63 par_64 par_65 par_66
par_1 .000
par_2 -.727 .000
par_3 -.170 .575 .000
par_4 .459 1.178 .636 .000
par_5 -.274 .463 -.111 -.720 .000
par_6 -.265 .251 -.152 -.575 -.075 .000
par_7 -1.182 -.666 -1.074 -1.485 -.996 -1.311 .000
par_8 -.421 .100 -.307 -.733 -.230 -.233 1.127 .000
par_9 -.722 -.202 -.611 -1.030 -.532 -.680 .675 -.456 .000
par_10 -.577 -.045 -.463 -.895 -.383 -.313 .661 -.153 .167 .000
par_11 -.678 -.150 -.566 -.993 -.486 -.419 .547 -.261 .057 -.154 .000
par_12 -.717 -.190 -.604 -1.030 -.525 -.459 .504 -.302 .015 -.211 -.057 .000
par_13 .067 .598 .185 -.253 .263 .349 1.317 .514 .831 .967 1.104 1.153 .000
par_14 -.792 -.264 -.680 -1.106 -.601 -.536 .430 -.380 -.061 -.320 -.164 -.105 -1.267 .000
par_15 -.465 .065 -.351 -.782 -.272 -.199 .769 -.039 .280 .161 .312 .366 -.795 .473 .000
par_16 -1.309 -.784 -1.201 -1.619 -1.121 -1.069 -.105 -.919 -.599 -1.050 -.888 -.822 -1.976 -.724 -1.197 .000
par_17 .096 .633 .216 -.228 .295 .382 1.363 .550 .871 1.035 1.178 1.228 .044 1.344 .857 2.066 .000
par_18 -.394 .142 -.277 -.714 -.198 -.122 .856 .040 .362 .281 .434 .489 -.702 .600 .113 1.329 -.772 .000
par_19 .429 .969 .553 .103 .630 .726 1.709 .898 1.219 1.571 1.708 1.757 .573 1.874 1.385 2.586 .545 1.319 .000
par_20 -.799 -.267 -.686 -1.115 -.606 -.541 .432 -.384 -.062 -.326 -.168 -.108 -1.285 -.001 -.483 .732 -1.365 -.612 -1.913 .000
par_21 -.025 .517 .096 -.350 .175 .260 1.249 .428 .752 .859 1.010 1.055 -.144 1.174 .684 1.914 -.194 .585 -.748 1.201 .000
par_22 -.382 .157 -.265 -.704 -.185 -.109 .877 .055 .379 .304 .458 .512 -.689 .625 .136 1.370 -.752 .022 -1.305 .637 -.570 .000
par_23 .048 .580 .167 -.272 .245 .330 1.299 .495 .813 .936 1.081 1.128 -.029 1.242 .767 1.951 -.074 .671 -.604 1.257 .114 .657 .000
par_24 -.486 .034 -.374 -.797 -.296 -.225 .723 -.069 .243 .082 .191 .232 -.583 .309 -.031 .846 -.619 -.111 -.966 .312 -.499 -.126 -.564 .000
par_25 -.718 -.195 -.607 -1.030 -.528 -.462 .495 -.307 .009 -.160 -.049 -.006 -.830 .071 -.273 .613 -.870 -.356 -1.221 .072 -.750 -.373 -.811 -.325 .000
par_26 -1.224 -.704 -1.118 -1.533 -1.038 -.983 -.029 -.834 -.517 -.701 -.585 -.541 -1.366 -.466 -.810 .077 -1.413 -.899 -1.764 -.469 -1.298 -.920 -1.347 -1.061 -.715 .000
par_27 -5.741 -5.314 -5.688 -5.988 -5.628 -5.743 -4.899 -5.638 -5.357 -6.457 -6.274 -6.204 -7.105 -6.150 -6.524 -5.565 -7.254 -6.700 -7.639 -6.213 -7.200 -6.779 -7.092 -6.316 -6.144 -5.575 .000
par_28 -6.444 -6.006 -6.395 -6.696 -6.314 -7.256 -6.245 -7.187 -6.822 -6.523 -6.353 -6.301 -7.094 -6.252 -6.586 -5.746 -7.214 -6.729 -7.554 -6.311 -7.175 -6.819 -7.087 -7.083 -6.923 -6.339 -1.047 .000
par_29 -9.973 -9.416 -9.880 -10.182 -9.768 -10.439 -9.456 -10.431 -10.057 -10.656 -10.412 -10.318 -11.253 -10.290 -10.676 -9.692 -11.501 -10.935 -11.900 -10.413 -11.515 -11.072 -11.249 -9.735 -9.774 -9.205 -4.642 -4.026 .000
par_30 -5.872 -5.353 -5.808 -6.157 -5.709 -5.049 -4.237 -4.959 -4.692 -5.059 -4.921 -4.870 -5.622 -4.820 -5.131 -4.332 -5.717 -5.255 -6.029 -4.859 -5.655 -5.313 -5.608 -5.609 -5.429 -4.867 1.027 2.037 4.962 .000
par_31 -11.270 -10.786 -11.270 -11.495 -11.152 -11.461 -10.528 -11.508 -11.163 -11.695 -11.450 -11.356 -12.263 -11.332 -11.707 -10.755 -12.518 -11.971 -12.904 -11.462 -12.542 -12.111 -12.261 -11.314 -11.249 -10.668 -6.101 -5.529 -2.416 -6.139 .000
par_32 -12.340 -11.863 -12.356 -12.552 -12.234 -12.573 -11.632 -12.604 -12.235 -13.365 -13.056 -13.000 -13.967 -12.960 -13.373 -12.340 -14.270 -13.674 -14.710 -13.116 -14.336 -13.839 -13.975 -12.707 -12.662 -12.169 -7.353 -6.982 -4.173 -7.378 -1.829 .000
par_33 -2.688 -2.349 -2.617 -2.888 -2.574 -3.281 -2.426 -3.165 -2.875 -3.094 -2.975 -2.929 -3.676 -2.869 -3.180 -2.380 -3.742 -3.279 -4.061 -2.887 -3.654 -3.313 -3.660 -3.095 -2.906 -2.429 3.497 4.140 6.126 1.854 6.814 7.763 .000
par_34 -1.962 -1.533 -1.877 -2.218 -1.809 -1.402 -.798 -1.308 -1.106 -1.559 -1.461 -1.424 -2.097 -1.364 -1.644 -.924 -2.139 -1.721 -2.426 -1.371 -2.049 -1.741 -2.083 -1.596 -1.412 -.979 4.695 4.096 6.199 4.222 7.054 7.898 1.175 .000
par_35 -2.554 -2.093 -2.466 -2.824 -2.392 -2.368 -1.527 -2.247 -1.964 -1.728 -1.643 -1.614 -2.173 -1.564 -1.800 -1.191 -2.207 -1.860 -2.438 -1.569 -2.134 -1.881 -2.162 -2.181 -1.988 -1.519 3.409 5.250 6.323 4.031 7.268 7.815 .821 -.406 .000
par_36 -1.991 -1.545 -1.900 -2.252 -1.831 -1.798 -.991 -1.676 -1.407 -1.576 -1.475 -1.437 -2.133 -1.375 -1.665 -.920 -2.178 -1.745 -2.474 -1.382 -2.085 -1.766 -2.118 -1.284 -1.142 -.787 3.473 3.991 7.122 2.943 7.426 7.900 1.193 .031 .429 .000
par_37 -3.200 -2.584 -3.112 -3.543 -2.975 -2.754 -1.739 -2.620 -2.276 -2.532 -2.391 -2.336 -3.229 -2.263 -2.636 -1.677 -3.311 -2.756 -3.691 -2.285 -3.210 -2.796 -3.214 -2.537 -2.312 -1.739 4.116 4.963 8.827 3.491 10.082 11.458 .979 -.339 .112 -.390 .000
par_38 -3.554 -3.284 -3.718 -4.117 -3.642 -3.652 -2.623 -3.526 -3.174 -3.473 -3.319 -3.258 -4.172 -3.189 -3.570 -2.591 -4.271 -3.705 -4.661 -3.220 -4.179 -3.756 -4.154 -3.446 -3.228 -2.646 3.463 4.301 8.098 2.638 9.447 10.883 .313 -1.017 -.628 -1.095 -.953 .000
par_39 -3.121 -2.386 -3.020 -3.422 -2.897 -2.905 -1.882 -2.766 -2.422 -2.685 -2.541 -2.486 -3.383 -2.413 -2.788 -1.824 -3.468 -2.911 -3.851 -2.436 -3.368 -2.952 -3.367 -2.680 -2.457 -1.882 4.164 5.003 8.878 3.362 10.006 11.395 .938 -.447 -.005 -.504 -.151 .797 .000
par_40 -4.321 -3.796 -3.961 -4.631 -4.151 -4.216 -3.176 -4.104 -3.735 -4.062 -3.897 -3.833 -4.763 -3.766 -4.152 -3.160 -4.876 -4.302 -5.271 -3.807 -4.790 -4.360 -4.745 -4.001 -3.791 -3.200 2.954 3.803 7.906 2.130 9.136 10.625 -.136 -1.421 -1.073 -1.521 -1.530 -.588 -1.386 .000
par_41 -3.887 -3.349 -3.803 -3.908 -3.758 -3.734 -2.714 -3.615 -3.264 -3.562 -3.406 -3.349 -4.256 -3.278 -3.657 -2.682 -4.360 -3.795 -4.748 -3.311 -4.267 -3.846 -4.241 -3.528 -3.313 -2.731 3.332 4.150 7.993 2.523 9.304 10.730 .229 -1.087 -.707 -1.170 -1.053 -.107 -.906 .475 .000
par_42 -3.671 -3.084 -3.570 -4.018 -3.233 -3.471 -2.442 -3.340 -2.989 -3.279 -3.126 -3.068 -3.977 -2.998 -3.377 -2.401 -4.073 -3.509 -4.461 -3.027 -3.980 -3.558 -3.961 -3.254 -3.036 -2.454 3.600 4.468 8.428 2.819 9.593 11.018 .465 -.876 -.474 -.950 -.763 .199 -.614 .787 .299 .000
par_43 -2.099 -1.569 -1.995 -2.405 -1.912 -2.083 -.996 -1.931 -1.574 -1.626 -1.500 -1.452 -2.301 -1.379 -1.733 -.821 -2.362 -1.834 -2.723 -1.389 -2.253 -1.864 -2.283 -1.662 -1.435 -.891 4.627 5.582 9.026 3.902 10.146 11.366 1.687 .257 .771 .253 .827 1.743 .974 2.315 1.839 1.554 .000
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Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF
Default model 66 401.692 399 .453 1.007
Saturated model 465 .000 0
Independence model 30 6139.206 435 .000 14.113

RMR, GFI

Model RMR GFI AGFI PGFI
Default model .047 .941 .931 .807
Saturated model .000 1.000
Independence model .496 .230 .177 .215

Baseline Comparisons

Model NFI
Delta1		 RFI
rho1		 IFI
Delta2		 TLI
rho2		 CFI
Default model .935 .929 1.000 .999 1.000
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI
Default model .917 .857 .917
Saturated model .000 .000 .000
Independence model 1.000 .000 .000

NCP

Model NCP LO 90 HI 90
Default model 2.692 .000 53.757
Saturated model .000 .000 .000
Independence model 5704.206 5454.284 5960.564

FMIN

Model FMIN F0 LO 90 HI 90
Default model .952 .006 .000 .127
Saturated model .000 .000 .000 .000
Independence model 14.548 13.517 12.925 14.125

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE
Default model .004 .000 .018 1.000
Independence model .176 .172 .180 .000

AIC

Model AIC BCC BIC CAIC
Default model 533.692 544.158 800.819 866.819
Saturated model 930.000 1003.734 2812.028 3277.028
Independence model 6199.206 6203.963 6320.628 6350.628

ECVI

Model ECVI LO 90 HI 90 MECVI
Default model 1.265 1.258 1.386 1.289
Saturated model 2.204 2.204 2.204 2.379
Independence model 14.690 14.098 15.298 14.701

HOELTER

Model HOELTER
.05		 HOELTER
.01
Default model 470 492
Independence model 34 35

Execution time summary

Minimization: .042
Miscellaneous: .417
Bootstrap: .000
Total: .459