χ2/df >3,but CFI,TLI,SRMR are good,how to understand it?how to further develop the model?
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Stan Silver
Jul 2, 2020, 10:59:38 AM7/2/20
to lavaan
SRMR 0.037
CFI 0.996
TLI 0.996
Above index looks good,but χ2/df=37711.897/1607,is there any further instruction to develop the model?
>summary(fitres,fit.measures=TRUE,standardized=TRUE)
lavaan 0.6-6 ended normally after 126 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 340
Number of observations 8251
Model Test User Model:
Standard Robust
Test Statistic 37711.897 58244.715
Degrees of freedom 1607 1607
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.654
Shift parameter 572.806
simple second-order correction
Model Test Baseline Model:
Test statistic 8885432.371 907084.853
Degrees of freedom 1711 1711
P-value 0.000 0.000
Scaling correction factor 9.812
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.996 0.937
Tucker-Lewis Index (TLI) 0.996 0.933
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.052 0.065
90 Percent confidence interval - lower 0.052 0.065
90 Percent confidence interval - upper 0.053 0.066
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.037 0.037
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1 =~
q10 1.000 0.497 0.497
q12 1.153 0.024 48.552 0.000 0.573 0.573
q22 1.804 0.034 53.618 0.000 0.896 0.896
q23 1.008 0.022 45.311 0.000 0.501 0.501
f2 =~
q11 1.000 0.708 0.708
q20 1.035 0.011 90.232 0.000 0.733 0.733
q24 1.164 0.011 104.810 0.000 0.825 0.825
q26 1.201 0.011 109.928 0.000 0.851 0.851
q47 1.136 0.011 107.169 0.000 0.805 0.805
q49 1.170 0.011 105.521 0.000 0.829 0.829
f3 =~
q19 1.000 0.824 0.824
q21 0.872 0.008 115.178 0.000 0.719 0.719
q25 0.861 0.008 106.151 0.000 0.710 0.710
q52 1.017 0.008 135.061 0.000 0.838 0.838
q50 1.136 0.007 172.257 0.000 0.937 0.937
q58 1.023 0.007 145.138 0.000 0.843 0.843
f4 =~
q04 1.000 0.853 0.853
q17 0.968 0.005 179.736 0.000 0.826 0.826
q18 1.016 0.005 218.253 0.000 0.866 0.866
q45 0.998 0.005 187.035 0.000 0.851 0.851
q51 0.625 0.010 64.943 0.000 0.533 0.533
q59 0.751 0.009 79.700 0.000 0.640 0.640
f5 =~
q02 1.000 0.852 0.852
q05 1.051 0.005 214.391 0.000 0.895 0.895
q13 0.852 0.007 122.920 0.000 0.725 0.725
q14 0.995 0.006 172.507 0.000 0.848 0.848
q16 0.925 0.006 152.775 0.000 0.788 0.788
q44 1.033 0.005 194.963 0.000 0.880 0.880
q57 0.966 0.006 154.096 0.000 0.823 0.823
f6 =~
q06 1.000 0.793 0.793
q15 1.092 0.006 174.815 0.000 0.865 0.865
q34 1.062 0.006 164.017 0.000 0.842 0.842
q43 1.169 0.007 171.993 0.000 0.927 0.927
q46 1.154 0.007 175.326 0.000 0.915 0.915
q56 1.124 0.007 166.276 0.000 0.891 0.891
f7 =~
q31 1.000 0.844 0.844
q33 0.940 0.008 116.628 0.000 0.794 0.794
q36 0.805 0.009 94.409 0.000 0.680 0.680
q38 0.964 0.008 128.267 0.000 0.814 0.814
q40 1.037 0.008 138.088 0.000 0.876 0.876
q55 0.987 0.007 135.795 0.000 0.834 0.834
q48 0.932 0.008 118.577 0.000 0.787 0.787
f8 =~
q01 1.000 0.716 0.716
q08 1.124 0.011 104.085 0.000 0.805 0.805
q09 1.040 0.011 92.741 0.000 0.745 0.745
q29 1.073 0.011 96.398 0.000 0.768 0.768
q39 0.967 0.012 82.528 0.000 0.693 0.693
q41 1.071 0.011 97.444 0.000 0.767 0.767
f9 =~
q07 1.000 0.802 0.802
q27 0.998 0.007 150.485 0.000 0.800 0.800
q32 1.061 0.008 126.345 0.000 0.851 0.851
q35 0.938 0.009 104.624 0.000 0.752 0.752
q53 1.029 0.009 118.599 0.000 0.825 0.825
f10 =~
q28 1.000 0.706 0.706
q30 1.118 0.013 83.485 0.000 0.789 0.789
q37 1.053 0.013 81.785 0.000 0.743 0.743
q42 1.078 0.014 76.158 0.000 0.761 0.761
q54 0.994 0.012 84.200 0.000 0.701 0.701
q60 0.947 0.012 76.553 0.000 0.668 0.668
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1 ~~
f2 0.311 0.007 43.078 0.000 0.885 0.885
f3 0.329 0.007 45.268 0.000 0.803 0.803
f4 0.379 0.008 48.627 0.000 0.896 0.896
f5 0.377 0.008 48.774 0.000 0.891 0.891
f6 0.359 0.008 46.967 0.000 0.911 0.911
f7 0.325 0.007 44.460 0.000 0.774 0.774
f8 0.301 0.007 43.106 0.000 0.845 0.845
f9 0.332 0.007 45.473 0.000 0.834 0.834
f10 0.273 0.007 39.996 0.000 0.780 0.780
f2 ~~
f3 0.494 0.006 76.951 0.000 0.847 0.847
f4 0.590 0.006 92.543 0.000 0.977 0.977
f5 0.531 0.006 82.561 0.000 0.881 0.881
f6 0.493 0.006 77.231 0.000 0.879 0.879
f7 0.451 0.006 69.898 0.000 0.753 0.753
f8 0.461 0.007 68.936 0.000 0.909 0.909
f9 0.483 0.007 73.765 0.000 0.850 0.850
f10 0.439 0.007 65.349 0.000 0.878 0.878
f3 ~~
f4 0.595 0.006 98.592 0.000 0.847 0.847
f5 0.616 0.006 102.654 0.000 0.877 0.877
f6 0.567 0.006 93.093 0.000 0.868 0.868
f7 0.522 0.007 79.945 0.000 0.750 0.750
f8 0.470 0.007 71.508 0.000 0.796 0.796
f9 0.545 0.006 84.314 0.000 0.825 0.825
f10 0.457 0.007 66.561 0.000 0.786 0.786
f4 ~~
f5 0.682 0.005 129.534 0.000 0.939 0.939
f6 0.620 0.006 108.880 0.000 0.918 0.918
f7 0.565 0.006 91.525 0.000 0.785 0.785
f8 0.554 0.007 84.918 0.000 0.908 0.908
f9 0.584 0.006 94.967 0.000 0.854 0.854
f10 0.522 0.007 77.622 0.000 0.868 0.868
f5 ~~
f6 0.659 0.006 116.592 0.000 0.975 0.975
f7 0.594 0.006 98.611 0.000 0.826 0.826
f8 0.547 0.006 84.487 0.000 0.898 0.898
f9 0.598 0.006 95.811 0.000 0.875 0.875
f10 0.470 0.007 69.416 0.000 0.782 0.782
f6 ~~
f7 0.558 0.006 90.545 0.000 0.834 0.834
f8 0.494 0.006 76.305 0.000 0.870 0.870
f9 0.546 0.006 87.578 0.000 0.859 0.859
f10 0.435 0.007 64.413 0.000 0.777 0.777
f7 ~~
f8 0.506 0.006 78.684 0.000 0.837 0.837
f9 0.544 0.006 85.746 0.000 0.803 0.803
f10 0.460 0.007 67.107 0.000 0.773 0.773
f8 ~~
f9 0.498 0.007 76.223 0.000 0.868 0.868
f10 0.442 0.007 64.892 0.000 0.875 0.875
f9 ~~
f10 0.467 0.007 66.817 0.000 0.826 0.826
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.q10 0.000 0.000 0.000
.q12 0.000 0.000 0.000
.q22 0.000 0.000 0.000
.q23 0.000 0.000 0.000
.q11 0.000 0.000 0.000
.q20 0.000 0.000 0.000
.q24 0.000 0.000 0.000
.q26 0.000 0.000 0.000
.q47 0.000 0.000 0.000
.q49 0.000 0.000 0.000
.q19 0.000 0.000 0.000
.q21 0.000 0.000 0.000
.q25 0.000 0.000 0.000
.q52 0.000 0.000 0.000
.q50 0.000 0.000 0.000
.q58 0.000 0.000 0.000
.q04 0.000 0.000 0.000
.q17 0.000 0.000 0.000
.q18 0.000 0.000 0.000
.q45 0.000 0.000 0.000
.q51 0.000 0.000 0.000
.q59 0.000 0.000 0.000
.q02 0.000 0.000 0.000
.q05 0.000 0.000 0.000
.q13 0.000 0.000 0.000
.q14 0.000 0.000 0.000
.q16 0.000 0.000 0.000
.q44 0.000 0.000 0.000
.q57 0.000 0.000 0.000
.q06 0.000 0.000 0.000
.q15 0.000 0.000 0.000
.q34 0.000 0.000 0.000
.q43 0.000 0.000 0.000
.q46 0.000 0.000 0.000
.q56 0.000 0.000 0.000
.q31 0.000 0.000 0.000
.q33 0.000 0.000 0.000
.q36 0.000 0.000 0.000
.q38 0.000 0.000 0.000
.q40 0.000 0.000 0.000
.q55 0.000 0.000 0.000
.q48 0.000 0.000 0.000
.q01 0.000 0.000 0.000
.q08 0.000 0.000 0.000
.q09 0.000 0.000 0.000
.q29 0.000 0.000 0.000
.q39 0.000 0.000 0.000
.q41 0.000 0.000 0.000
.q07 0.000 0.000 0.000
.q27 0.000 0.000 0.000
.q32 0.000 0.000 0.000
.q35 0.000 0.000 0.000
.q53 0.000 0.000 0.000
.q28 0.000 0.000 0.000
.q30 0.000 0.000 0.000
.q37 0.000 0.000 0.000
.q42 0.000 0.000 0.000
.q54 0.000 0.000 0.000
.q60 0.000 0.000 0.000
f1 0.000 0.000 0.000
f2 0.000 0.000 0.000
f3 0.000 0.000 0.000
f4 0.000 0.000 0.000
f5 0.000 0.000 0.000
f6 0.000 0.000 0.000
f7 0.000 0.000 0.000
f8 0.000 0.000 0.000
f9 0.000 0.000 0.000
f10 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
q10|t1 -0.750 0.015 -48.999 0.000 -0.750 -0.750
q10|t2 0.152 0.014 10.971 0.000 0.152 0.152
q10|t3 0.952 0.016 58.282 0.000 0.952 0.952
q10|t4 1.562 0.022 70.843 0.000 1.562 1.562
q12|t1 -1.014 0.017 -60.658 0.000 -1.014 -1.014
q12|t2 -0.367 0.014 -25.931 0.000 -0.367 -0.367
q12|t3 0.269 0.014 19.231 0.000 0.269 0.269
q12|t4 0.968 0.016 58.913 0.000 0.968 0.968
q22|t1 -0.977 0.016 -59.274 0.000 -0.977 -0.977
q22|t2 -0.403 0.014 -28.332 0.000 -0.403 -0.403
q22|t3 0.180 0.014 12.950 0.000 0.180 0.180
q22|t4 0.826 0.016 52.779 0.000 0.826 0.826
q23|t1 -0.086 0.014 -6.241 0.000 -0.086 -0.086
q23|t2 0.644 0.015 43.239 0.000 0.644 0.644
q23|t3 1.202 0.018 66.364 0.000 1.202 1.202
q23|t4 1.634 0.023 70.742 0.000 1.634 1.634
q11|t1 -1.364 0.020 -69.434 0.000 -1.364 -1.364
q11|t2 -0.644 0.015 -43.239 0.000 -0.644 -0.644
q11|t3 0.374 0.014 26.411 0.000 0.374 0.374
q11|t4 1.120 0.017 64.182 0.000 1.120 1.120
q20|t1 -0.223 0.014 -16.026 0.000 -0.223 -0.223
q20|t2 0.569 0.015 38.859 0.000 0.569 0.569
q20|t3 1.232 0.018 67.075 0.000 1.232 1.232
q20|t4 1.713 0.024 70.278 0.000 1.713 1.713
q24|t1 -0.645 0.015 -43.302 0.000 -0.645 -0.645
q24|t2 0.066 0.014 4.788 0.000 0.066 0.066
q24|t3 0.713 0.015 47.038 0.000 0.713 0.713
q24|t4 1.222 0.018 66.857 0.000 1.222 1.222
q26|t1 -0.710 0.015 -46.891 0.000 -0.710 -0.710
q26|t2 -0.071 0.014 -5.162 0.000 -0.071 -0.071
q26|t3 0.445 0.014 31.075 0.000 0.445 0.445
q26|t4 0.985 0.017 59.576 0.000 0.985 0.985
q47|t1 -0.766 0.015 -49.807 0.000 -0.766 -0.766
q47|t2 -0.015 0.014 -1.068 0.286 -0.015 -0.015
q47|t3 0.813 0.016 52.128 0.000 0.813 0.813
q47|t4 1.377 0.020 69.608 0.000 1.377 1.377
q49|t1 -0.498 0.014 -34.481 0.000 -0.498 -0.498
q49|t2 0.258 0.014 18.441 0.000 0.258 0.258
q49|t3 0.889 0.016 55.668 0.000 0.889 0.889
q49|t4 1.380 0.020 69.647 0.000 1.380 1.380
q19|t1 -0.908 0.016 -56.456 0.000 -0.908 -0.908
q19|t2 -0.166 0.014 -11.939 0.000 -0.166 -0.166
q19|t3 0.489 0.014 33.940 0.000 0.489 0.489
q19|t4 1.036 0.017 61.446 0.000 1.036 1.036
q21|t1 -0.166 0.014 -11.961 0.000 -0.166 -0.166
q21|t2 0.594 0.015 40.360 0.000 0.594 0.594
q21|t3 1.179 0.018 65.793 0.000 1.179 1.179
q21|t4 1.590 0.022 70.841 0.000 1.590 1.590
q25|t1 -0.773 0.015 -50.158 0.000 -0.773 -0.773
q25|t2 0.109 0.014 7.914 0.000 0.109 0.109
q25|t3 0.821 0.016 52.535 0.000 0.821 0.821
q25|t4 1.428 0.020 70.170 0.000 1.428 1.428
q52|t1 -0.304 0.014 -21.686 0.000 -0.304 -0.304
q52|t2 0.282 0.014 20.130 0.000 0.282 0.282
q52|t3 0.816 0.016 52.291 0.000 0.816 0.816
q52|t4 1.207 0.018 66.485 0.000 1.207 1.207
q50|t1 -0.910 0.016 -56.574 0.000 -0.910 -0.910
q50|t2 -0.262 0.014 -18.726 0.000 -0.262 -0.262
q50|t3 0.277 0.014 19.779 0.000 0.277 0.277
q50|t4 0.859 0.016 54.292 0.000 0.859 0.859
q58|t1 -0.295 0.014 -21.051 0.000 -0.295 -0.295
q58|t2 0.313 0.014 22.300 0.000 0.313 0.313
q58|t3 0.854 0.016 54.092 0.000 0.854 0.854
q58|t4 1.297 0.019 68.380 0.000 1.297 1.297
q04|t1 -0.718 0.015 -47.331 0.000 -0.718 -0.718
q04|t2 -0.108 0.014 -7.826 0.000 -0.108 -0.108
q04|t3 0.572 0.015 39.031 0.000 0.572 0.572
q04|t4 1.162 0.018 65.349 0.000 1.162 1.162
q17|t1 -1.018 0.017 -60.805 0.000 -1.018 -1.018
q17|t2 -0.459 0.014 -32.031 0.000 -0.459 -0.459
q17|t3 0.089 0.014 6.439 0.000 0.089 0.089
q17|t4 0.686 0.015 45.587 0.000 0.686 0.686
q18|t1 -0.752 0.015 -49.102 0.000 -0.752 -0.752
q18|t2 -0.099 0.014 -7.143 0.000 -0.099 -0.099
q18|t3 0.478 0.014 33.246 0.000 0.478 0.478
q18|t4 1.031 0.017 61.264 0.000 1.031 1.031
q45|t1 -0.770 0.015 -50.014 0.000 -0.770 -0.770
q45|t2 -0.255 0.014 -18.266 0.000 -0.255 -0.255
q45|t3 0.243 0.014 17.454 0.000 0.243 0.243
q45|t4 0.786 0.015 50.817 0.000 0.786 0.786
q51|t1 -0.457 0.014 -31.901 0.000 -0.457 -0.457
q51|t2 0.200 0.014 14.357 0.000 0.200 0.200
q51|t3 0.797 0.016 51.372 0.000 0.797 0.797
q51|t4 1.254 0.019 67.557 0.000 1.254 1.254
q59|t1 0.136 0.014 9.806 0.000 0.136 0.136
q59|t2 0.803 0.016 51.638 0.000 0.803 0.803
q59|t3 1.367 0.020 69.476 0.000 1.367 1.367
q59|t4 1.819 0.026 69.091 0.000 1.819 1.819
q02|t1 -1.262 0.019 -67.708 0.000 -1.262 -1.262
q02|t2 -0.617 0.015 -41.685 0.000 -0.617 -0.617
q02|t3 0.013 0.014 0.914 0.361 0.013 0.013
q02|t4 0.727 0.015 47.791 0.000 0.727 0.727
q05|t1 -0.911 0.016 -56.594 0.000 -0.911 -0.911
q05|t2 -0.327 0.014 -23.219 0.000 -0.327 -0.327
q05|t3 0.298 0.014 21.226 0.000 0.298 0.298
q05|t4 0.994 0.017 59.914 0.000 0.994 0.994
q13|t1 -0.816 0.016 -52.311 0.000 -0.816 -0.816
q13|t2 -0.124 0.014 -8.970 0.000 -0.124 -0.124
q13|t3 0.443 0.014 30.944 0.000 0.443 0.443
q13|t4 0.962 0.016 58.704 0.000 0.962 0.962
q14|t1 -0.539 0.015 -37.008 0.000 -0.539 -0.539
q14|t2 -0.038 0.014 -2.719 0.007 -0.038 -0.038
q14|t3 0.572 0.015 39.031 0.000 0.572 0.572
q14|t4 1.057 0.017 62.185 0.000 1.057 1.057
q16|t1 -0.916 0.016 -56.809 0.000 -0.916 -0.916
q16|t2 -0.221 0.014 -15.873 0.000 -0.221 -0.221
q16|t3 0.519 0.014 35.778 0.000 0.519 0.519
q16|t4 1.119 0.017 64.148 0.000 1.119 1.119
q44|t1 -0.931 0.016 -57.452 0.000 -0.931 -0.931
q44|t2 -0.239 0.014 -17.146 0.000 -0.239 -0.239
q44|t3 0.387 0.014 27.306 0.000 0.387 0.387
q44|t4 0.974 0.016 59.160 0.000 0.974 0.974
q57|t1 -0.165 0.014 -11.895 0.000 -0.165 -0.165
q57|t2 0.259 0.014 18.551 0.000 0.259 0.259
q57|t3 0.818 0.016 52.372 0.000 0.818 0.818
q57|t4 1.195 0.018 66.212 0.000 1.195 1.195
q06|t1 -1.135 0.018 -64.616 0.000 -1.135 -1.135
q06|t2 -0.413 0.014 -28.985 0.000 -0.413 -0.413
q06|t3 0.313 0.014 22.300 0.000 0.313 0.313
q06|t4 1.022 0.017 60.952 0.000 1.022 1.022
q15|t1 -0.688 0.015 -45.693 0.000 -0.688 -0.688
q15|t2 0.059 0.014 4.282 0.000 0.059 0.059
q15|t3 0.725 0.015 47.686 0.000 0.725 0.725
q15|t4 1.255 0.019 67.570 0.000 1.255 1.255
q34|t1 -0.551 0.015 -37.762 0.000 -0.551 -0.551
q34|t2 0.013 0.014 0.936 0.349 0.013 0.013
q34|t3 0.587 0.015 39.910 0.000 0.587 0.587
q34|t4 1.034 0.017 61.373 0.000 1.034 1.034
q43|t1 -1.028 0.017 -61.172 0.000 -1.028 -1.028
q43|t2 -0.453 0.014 -31.618 0.000 -0.453 -0.453
q43|t3 0.139 0.014 10.004 0.000 0.139 0.139
q43|t4 0.756 0.015 49.310 0.000 0.756 0.756
q46|t1 -0.838 0.016 -53.346 0.000 -0.838 -0.838
q46|t2 -0.210 0.014 -15.104 0.000 -0.210 -0.210
q46|t3 0.377 0.014 26.651 0.000 0.377 0.377
q46|t4 0.939 0.016 57.762 0.000 0.939 0.939
q56|t1 -0.912 0.016 -56.653 0.000 -0.912 -0.912
q56|t2 -0.366 0.014 -25.865 0.000 -0.366 -0.366
q56|t3 0.128 0.014 9.234 0.000 0.128 0.128
q56|t4 0.672 0.015 44.828 0.000 0.672 0.672
q31|t1 -1.005 0.017 -60.324 0.000 -1.005 -1.005
q31|t2 -0.396 0.014 -27.874 0.000 -0.396 -0.396
q31|t3 0.342 0.014 24.247 0.000 0.342 0.342
q31|t4 0.928 0.016 57.336 0.000 0.928 0.928
q33|t1 -0.922 0.016 -57.083 0.000 -0.922 -0.922
q33|t2 -0.368 0.014 -26.018 0.000 -0.368 -0.368
q33|t3 0.204 0.014 14.643 0.000 0.204 0.204
q33|t4 0.749 0.015 48.957 0.000 0.749 0.749
q36|t1 -0.963 0.016 -58.723 0.000 -0.963 -0.963
q36|t2 -0.230 0.014 -16.510 0.000 -0.230 -0.230
q36|t3 0.398 0.014 28.004 0.000 0.398 0.398
q36|t4 0.930 0.016 57.394 0.000 0.930 0.930
q38|t1 -0.820 0.016 -52.494 0.000 -0.820 -0.820
q38|t2 -0.187 0.014 -13.478 0.000 -0.187 -0.187
q38|t3 0.474 0.014 32.964 0.000 0.474 0.474
q38|t4 0.912 0.016 56.653 0.000 0.912 0.912
q40|t1 -0.453 0.014 -31.597 0.000 -0.453 -0.453
q40|t2 0.107 0.014 7.715 0.000 0.107 0.107
q40|t3 0.721 0.015 47.477 0.000 0.721 0.721
q40|t4 1.081 0.017 62.980 0.000 1.081 1.081
q55|t1 -0.758 0.015 -49.434 0.000 -0.758 -0.758
q55|t2 -0.162 0.014 -11.653 0.000 -0.162 -0.162
q55|t3 0.437 0.014 30.596 0.000 0.437 0.437
q55|t4 0.865 0.016 54.573 0.000 0.865 0.865
q48|t1 -0.939 0.016 -57.782 0.000 -0.939 -0.939
q48|t2 -0.369 0.014 -26.106 0.000 -0.369 -0.369
q48|t3 0.153 0.014 11.059 0.000 0.153 0.153
q48|t4 0.728 0.015 47.853 0.000 0.728 0.728
q01|t1 -0.619 0.015 -41.834 0.000 -0.619 -0.619
q01|t2 0.003 0.014 0.253 0.800 0.003 0.003
q01|t3 0.682 0.015 45.398 0.000 0.682 0.682
q01|t4 1.198 0.018 66.288 0.000 1.198 1.198
q08|t1 -0.373 0.014 -26.368 0.000 -0.373 -0.373
q08|t2 0.288 0.014 20.547 0.000 0.288 0.288
q08|t3 0.901 0.016 56.161 0.000 0.901 0.901
q08|t4 1.356 0.020 69.318 0.000 1.356 1.356
q09|t1 -0.810 0.016 -52.006 0.000 -0.810 -0.810
q09|t2 -0.253 0.014 -18.090 0.000 -0.253 -0.253
q09|t3 0.414 0.014 29.073 0.000 0.414 0.414
q09|t4 0.955 0.016 58.417 0.000 0.955 0.955
q29|t1 -0.604 0.015 -40.959 0.000 -0.604 -0.604
q29|t2 0.162 0.014 11.697 0.000 0.162 0.162
q29|t3 0.999 0.017 60.119 0.000 0.999 0.999
q29|t4 1.496 0.021 70.655 0.000 1.496 1.496
q39|t1 -0.322 0.014 -22.891 0.000 -0.322 -0.322
q39|t2 0.282 0.014 20.174 0.000 0.282 0.282
q39|t3 0.893 0.016 55.846 0.000 0.893 0.893
q39|t4 1.359 0.020 69.361 0.000 1.359 1.359
q41|t1 -0.595 0.015 -40.402 0.000 -0.595 -0.595
q41|t2 -0.004 0.014 -0.275 0.783 -0.004 -0.004
q41|t3 0.613 0.015 41.450 0.000 0.613 0.613
q41|t4 1.065 0.017 62.452 0.000 1.065 1.065
q07|t1 -1.157 0.018 -65.221 0.000 -1.157 -1.157
q07|t2 -0.584 0.015 -39.760 0.000 -0.584 -0.584
q07|t3 0.058 0.014 4.194 0.000 0.058 0.058
q07|t4 0.780 0.015 50.550 0.000 0.780 0.780
q27|t1 -0.915 0.016 -56.790 0.000 -0.915 -0.915
q27|t2 -0.369 0.014 -26.084 0.000 -0.369 -0.369
q27|t3 0.239 0.014 17.124 0.000 0.239 0.239
q27|t4 0.879 0.016 55.211 0.000 0.879 0.879
q32|t1 -0.591 0.015 -40.188 0.000 -0.591 -0.591
q32|t2 0.083 0.014 5.999 0.000 0.083 0.083
q32|t3 0.687 0.015 45.630 0.000 0.687 0.687
q32|t4 1.237 0.018 67.176 0.000 1.237 1.237
q35|t1 -0.311 0.014 -22.146 0.000 -0.311 -0.311
q35|t2 0.345 0.014 24.466 0.000 0.345 0.345
q35|t3 0.947 0.016 58.090 0.000 0.947 0.947
q35|t4 1.408 0.020 69.971 0.000 1.408 1.408
q53|t1 -0.416 0.014 -29.181 0.000 -0.416 -0.416
q53|t2 0.143 0.014 10.334 0.000 0.143 0.143
q53|t3 0.694 0.015 46.009 0.000 0.694 0.694
q53|t4 1.204 0.018 66.425 0.000 1.204 1.204
q28|t1 -0.727 0.015 -47.812 0.000 -0.727 -0.727
q28|t2 -0.074 0.014 -5.339 0.000 -0.074 -0.074
q28|t3 0.558 0.015 38.192 0.000 0.558 0.558
q28|t4 1.046 0.017 61.808 0.000 1.046 1.046
q30|t1 -0.244 0.014 -17.520 0.000 -0.244 -0.244
q30|t2 0.475 0.014 33.029 0.000 0.475 0.475
q30|t3 1.152 0.018 65.091 0.000 1.152 1.152
q30|t4 1.578 0.022 70.848 0.000 1.578 1.578
q37|t1 -0.005 0.014 -0.363 0.716 -0.005 -0.005
q37|t2 0.661 0.015 44.235 0.000 0.661 0.661
q37|t3 1.217 0.018 66.739 0.000 1.217 1.217
q37|t4 1.600 0.023 70.829 0.000 1.600 1.600
q42|t1 0.019 0.014 1.354 0.176 0.019 0.019
q42|t2 0.593 0.015 40.274 0.000 0.593 0.593
q42|t3 1.122 0.017 64.216 0.000 1.122 1.122
q42|t4 1.484 0.021 70.592 0.000 1.484 1.484
q54|t1 -0.334 0.014 -23.701 0.000 -0.334 -0.334
q54|t2 0.395 0.014 27.830 0.000 0.395 0.395
q54|t3 0.968 0.016 58.932 0.000 0.968 0.968
q54|t4 1.404 0.020 69.928 0.000 1.404 1.404
q60|t1 -0.541 0.015 -37.159 0.000 -0.541 -0.541
q60|t2 0.118 0.014 8.530 0.000 0.118 0.118
q60|t3 0.829 0.016 52.901 0.000 0.829 0.829
q60|t4 1.359 0.020 69.361 0.000 1.359 1.359
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.q10 0.753 0.753 0.753
.q12 0.672 0.672 0.672
.q22 0.198 0.198 0.198
.q23 0.749 0.749 0.749
.q11 0.498 0.498 0.498
.q20 0.463 0.463 0.463
.q24 0.320 0.320 0.320
.q26 0.276 0.276 0.276
.q47 0.352 0.352 0.352
.q49 0.313 0.313 0.313
.q19 0.321 0.321 0.321
.q21 0.483 0.483 0.483
.q25 0.496 0.496 0.496
.q52 0.298 0.298 0.298
.q50 0.123 0.123 0.123
.q58 0.289 0.289 0.289
.q04 0.273 0.273 0.273
.q17 0.319 0.319 0.319
.q18 0.250 0.250 0.250
.q45 0.276 0.276 0.276
.q51 0.716 0.716 0.716
.q59 0.590 0.590 0.590
.q02 0.275 0.275 0.275
.q05 0.199 0.199 0.199
.q13 0.474 0.474 0.474
.q14 0.281 0.281 0.281
.q16 0.380 0.380 0.380
.q44 0.226 0.226 0.226
.q57 0.323 0.323 0.323
.q06 0.371 0.371 0.371
.q15 0.251 0.251 0.251
.q34 0.292 0.292 0.292
.q43 0.141 0.141 0.141
.q46 0.162 0.162 0.162
.q56 0.206 0.206 0.206
.q31 0.287 0.287 0.287
.q33 0.370 0.370 0.370
.q36 0.538 0.538 0.538
.q38 0.338 0.338 0.338
.q40 0.233 0.233 0.233
.q55 0.305 0.305 0.305
.q48 0.381 0.381 0.381
.q01 0.487 0.487 0.487
.q08 0.352 0.352 0.352
.q09 0.445 0.445 0.445
.q29 0.410 0.410 0.410
.q39 0.520 0.520 0.520
.q41 0.412 0.412 0.412
.q07 0.357 0.357 0.357
.q27 0.360 0.360 0.360
.q32 0.277 0.277 0.277
.q35 0.434 0.434 0.434
.q53 0.319 0.319 0.319
.q28 0.502 0.502 0.502
.q30 0.378 0.378 0.378
.q37 0.448 0.448 0.448
.q42 0.421 0.421 0.421
.q54 0.508 0.508 0.508
.q60 0.554 0.554 0.554
f1 0.247 0.009 27.957 0.000 1.000 1.000
f2 0.502 0.009 56.717 0.000 1.000 1.000
f3 0.679 0.007 93.757 0.000 1.000 1.000
f4 0.727 0.006 119.899 0.000 1.000 1.000
f5 0.725 0.007 110.361 0.000 1.000 1.000
f6 0.629 0.007 87.101 0.000 1.000 1.000
f7 0.713 0.008 87.161 0.000 1.000 1.000
f8 0.513 0.009 56.849 0.000 1.000 1.000
f9 0.643 0.008 83.129 0.000 1.000 1.000
f10 0.498 0.010 52.240 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
q10 1.000 1.000 1.000
q12 1.000 1.000 1.000
q22 1.000 1.000 1.000
q23 1.000 1.000 1.000
q11 1.000 1.000 1.000
q20 1.000 1.000 1.000
q24 1.000 1.000 1.000
q26 1.000 1.000 1.000
q47 1.000 1.000 1.000
q49 1.000 1.000 1.000
q19 1.000 1.000 1.000
q21 1.000 1.000 1.000
q25 1.000 1.000 1.000
q52 1.000 1.000 1.000
q50 1.000 1.000 1.000
q58 1.000 1.000 1.000
q04 1.000 1.000 1.000
q17 1.000 1.000 1.000
q18 1.000 1.000 1.000
q45 1.000 1.000 1.000
q51 1.000 1.000 1.000
q59 1.000 1.000 1.000
q02 1.000 1.000 1.000
q05 1.000 1.000 1.000
q13 1.000 1.000 1.000
q14 1.000 1.000 1.000
q16 1.000 1.000 1.000
q44 1.000 1.000 1.000
q57 1.000 1.000 1.000
q06 1.000 1.000 1.000
q15 1.000 1.000 1.000
q34 1.000 1.000 1.000
q43 1.000 1.000 1.000
q46 1.000 1.000 1.000
q56 1.000 1.000 1.000
q31 1.000 1.000 1.000
q33 1.000 1.000 1.000
q36 1.000 1.000 1.000
q38 1.000 1.000 1.000
q40 1.000 1.000 1.000
q55 1.000 1.000 1.000
q48 1.000 1.000 1.000
q01 1.000 1.000 1.000
q08 1.000 1.000 1.000
q09 1.000 1.000 1.000
q29 1.000 1.000 1.000
q39 1.000 1.000 1.000
q41 1.000 1.000 1.000
q07 1.000 1.000 1.000
q27 1.000 1.000 1.000
q32 1.000 1.000 1.000
q35 1.000 1.000 1.000
q53 1.000 1.000 1.000
q28 1.000 1.000 1.000
q30 1.000 1.000 1.000
q37 1.000 1.000 1.000
q42 1.000 1.000 1.000
q54 1.000 1.000 1.000
q60 1.000 1.000 1.000
Patrick (Malone Quantitative)
Jul 2, 2020, 11:07:55 AM7/2/20
to lav...@googlegroups.com
Stan,
Don't rely on chi-squared/df. It's never been established as having a
widely usable cutoff and can be considered obsolete.
Hope that helps,
Pat
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Patrick S. Malone, Ph.D., Malone Quantitative
NEW Service Models: http://malonequantitative.com
He/Him/His
Don't rely on chi-squared/df. It's never been established as having a
widely usable cutoff and can be considered obsolete.
Hope that helps,
Pat
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Patrick S. Malone, Ph.D., Malone Quantitative
NEW Service Models: http://malonequantitative.com
He/Him/His
Brian Peña Calero
Jul 2, 2020, 11:54:09 AM7/2/20
to lavaan
Stan,
Most of the time when there are more than 3 factors in the model, χ2 / df will be higher than the classic cuts. It is not a valid measure to validate the fit of the model.
You have a 10 factor model. The fit it shows is excellent.
You have a 10 factor model. The fit it shows is excellent.
Stan Silver
Jul 2, 2020, 12:00:16 PM7/2/20
to lavaan
Thanks for your reply.
But robust CFI is only 0.937,and P-value RMSEA <= 0.05 is 0.000,is that a problem?
Stan Silver
Jul 2, 2020, 12:00:56 PM7/2/20
to lavaan
Is that going to be a problem
...
Patrick (Malone Quantitative)
Jul 2, 2020, 12:38:38 PM7/2/20
to lav...@googlegroups.com
It's hard to get an excellent fit with such a large model, and the
p-value for RMSEA reflects the large sample size--focus on the RMSEA
point estimate in this case because the confidence interval width is
essentially zero.
Most likely the lack of fit you do have is from omitted
cross-loadings, probably small. Frankly, with 60 items, 1700 df and a
simple structure, I'd be quite pleased with what you got.
p-value for RMSEA reflects the large sample size--focus on the RMSEA
point estimate in this case because the confidence interval width is
essentially zero.
Most likely the lack of fit you do have is from omitted
cross-loadings, probably small. Frankly, with 60 items, 1700 df and a
simple structure, I'd be quite pleased with what you got.
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Stan Silver
Jul 2, 2020, 1:20:31 PM7/2/20
to lavaan
Hi Patrick,
Allow me ask a further question about `cross-loadings` you mentioned.
How to set `cross-loadings` in lavaan model?
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Patrick (Malone Quantitative)
Jul 2, 2020, 1:38:06 PM7/2/20
to lav...@googlegroups.com
The direct way would be to just add an item with a suspected
cross-loading to the measurement on a second factor. If that doesn't
mean much to you, hit an SEM textbook--it's pretty fundamental.
But don't let it be a fishing expedition for cross-loadings. As Brian
and I have both said, you've got a good fit.
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cross-loading to the measurement on a second factor. If that doesn't
mean much to you, hit an SEM textbook--it's pretty fundamental.
But don't let it be a fishing expedition for cross-loadings. As Brian
and I have both said, you've got a good fit.
> To view this discussion on the web visit https://groups.google.com/d/msgid/lavaan/ded39a04-1279-404a-b811-4d2e705a5be7o%40googlegroups.com.
Paul Joseph
Jul 2, 2020, 5:24:30 PM7/2/20
to lav...@googlegroups.com
Hi Patrick my estimation below gave me negative variance in one of the variables. what those that mean? How do i interpret threshold please. the mediation result with categorical variables is attached. thank you
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Brian Peña Calero
Jul 2, 2020, 9:20:41 PM7/2/20
to lavaan
It's hard difficult you can get a p-value of RMSEA > .05. The sample size influence directly in this and X2 value (and p-value of this).
If you want really to improve your values fits, you can try some of this ideas/options:
- Try to exclude some items with loadings below of 0.5 or 0.6. Be careful don't affect the sense and kernel of construct factor.
- Get the modification indices of your cfa model with this command: `lavaan::modindices(fit, sort. = TRUE, maximum.number = 20)` . This output allow you see cross-loadings on items or suggestions of correlations between error variance.
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