Hi everyone,
I am working with a 4-wave panel dataset, N=4,500. The dataset looks like this:
I want to estimate a Cross lagged panel model with fixed effects.
Using the xtdpdml command, stata gives me the wanted coefficients as output.
However, when transfering the estimation to Mplus (using the mplus option in xtdpdml), my model does not seem to be identified and therefore does not provide the std. dev. and p-values in the output.
I already tried different options (e.g., only estimating the IV and DV without controls, which works perfectly in both programms).
Does anybody have an idea why the model does not converge/ can't be identified in mplus, but in stata?
Best,
Lisa
I am working with a 4-wave panel dataset, N=4,500. The dataset looks like this:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(sum_helpfromchildren srh) byte alter float spouse byte anzkind 0 5 59 1 0 0 4 62 1 0 0 4 65 1 0 0 4 73 0 1 0 3 76 0 1 0 3 79 0 1 0 4 59 0 2 0 4 65 0 2 0 4 68 0 2 0 3 68 0 3 0 4 71 0 3 1 4 74 0 3 0 4 77 0 3 0 4 83 1 2 0 3 86 1 2 0 2 89 1 2 0 4 74 1 1 0 3 77 1 1 0 3 74 1 0 0 3 77 1 0 0 3 80 1 0 0 2 89 1 1 0 1 92 1 4 1 4 95 1 1 0 3 97 1 1 1 1 75 1 3 0 1 78 1 3 0 2 88 0 4 0 2 91 0 4 1 2 84 0 2 1 3 87 0 1 1 3 90 0 2 1 3 93 0 2 0 3 86 0 1 1 3 89 0 1 1 2 92 0 1 0 4 90 0 2 0 4 93 0 2 1 2 85 1 2 0 3 88 1 2 1 1 91 1 2 0 4 82 1 3 0 3 85 1 3 0 3 88 1 3 0 5 91 0 0 0 4 95 0 0 0 4 97 0 0 0 3 84 0 2 0 3 87 0 2 0 3 90 0 2 0 3 93 0 1 0 4 83 1 2 0 3 86 1 1 0 3 70 1 3 0 4 73 1 3 1 4 76 1 3 0 4 84 0 0 0 3 87 0 0 0 3 90 0 0 0 4 71 0 1 0 2 77 0 1 0 1 92 0 2 0 1 95 0 2 0 3 87 0 2 1 4 90 0 2 1 4 93 0 2 0 2 79 1 2 0 2 82 0 2 0 4 74 1 2 0 4 77 1 2 0 4 80 1 2 0 4 76 1 3 0 3 79 1 3 1 3 82 1 3 1 3 93 0 4 1 3 95 0 4 1 4 67 1 1 0 4 70 1 1 0 3 73 1 1 0 4 76 1 1 0 4 70 1 1 1 3 73 1 1 0 3 76 1 1 0 3 77 0 2 1 4 80 0 2 .a 3 83 0 2 1 2 86 0 2 0 3 78 0 3 0 3 81 0 3 0 2 84 0 3 0 3 88 1 3 0 1 91 1 3 0 4 79 1 5 1 4 82 1 5 0 4 70 1 5 0 4 76 1 5 0 4 79 1 5 0 3 88 0 0 0 4 91 0 0 0 4 86 0 2 end label values alter ALTER_17 label def ALTER_17 97 "97. 96-97", modify label values anzkind ANZKIND label def ANZKIND 0 "0. keine", modify
I want to estimate a Cross lagged panel model with fixed effects.
Using the xtdpdml command, stata gives me the wanted coefficients as output.
Code:
. xtdpdml sum_helpfromchildren alter spouse anzkind, pre(srh L.srh) errorinv title(ML-SE
> M of instrumental help on self-rated health) details gof decimals(3) showcmd fiml
The generated sem command is
sem (sum_helpfromchildren2 <- sum_helpfromchildren1@b1
alter2@b2 spouse2@b3 anzkind2@b4 srh2@b5 srh1@b6 Alpha@1
E2@1 ) (sum_helpfromchildren3 <- sum_helpfromchildren2@b1
alter3@b2 spouse3@b3 anzkind3@b4 srh3@b5 srh2@b6 Alpha@1
E3@1 ) (sum_helpfromchildren4 <- sum_helpfromchildren3@b1
alter4@b2 spouse4@b3 anzkind4@b4 srh4@b5 srh3@b6 Alpha@1
), var(e.sum_helpfromchildren2@0
e.sum_helpfromchildren3@0) var(Alpha) cov(Alpha*(E2 E3)@0
_OEx*(E2 E3)@0 E2*(E3)@0 srh3*(E2) srh4*(E2 E3) srh3*(E2))
cformat(%9.3f) var(E2@v1 E3@v1 e.sum_helpfromchildren4@v1)
iterate(250) technique(nr 25 bhhh 25) noxconditional method(mlmv)
(62 observations containing extended missing values excluded)
Endogenous variables
Observed: sum_helpfromchildren2 sum_helpfromchildren3 sum_helpfromchildren4
Exogenous variables
Observed: sum_helpfromchildren1 alter2 spouse2 anzkind2 srh2 srh1 alter3 spouse3 anzkind3 srh3
alter4 spouse4 anzkind4 srh4
Latent: Alpha E2 E3
Fitting saturated model:
Iteration 0: log likelihood = -46981.921
Iteration 1: log likelihood = -44926.541
Iteration 2: log likelihood = -44272.425
Iteration 3: log likelihood = -44151.991
Iteration 4: log likelihood = -44144.762
Iteration 5: log likelihood = -44144.732
Iteration 6: log likelihood = -44144.732
Fitting baseline model:
Iteration 0: log likelihood = -44678.978
Iteration 1: log likelihood = -44677.583
Iteration 2: log likelihood = -44677.582
Fitting conditional model:
(setting technique to nr)
Iteration 0: log likelihood = -53044.686 (not concave)
Iteration 1: log likelihood = -50285.277 (not concave)
Iteration 2: log likelihood = -47735.654 (not concave)
Iteration 3: log likelihood = -45591.374 (not concave)
Iteration 4: log likelihood = -44681.152 (not concave)
Iteration 5: log likelihood = -44240.888
Iteration 6: log likelihood = -44162.472
Iteration 7: log likelihood = -44158.498
Iteration 8: log likelihood = -44158.484
Iteration 9: log likelihood = -44158.312
Iteration 10: log likelihood = -44158.309
Iteration 11: log likelihood = -44158.308
Iteration 12: log likelihood = -44158.308
Iteration 13: log likelihood = -44158.308
Fitting target model:
(setting technique to nr)
Iteration 0: log likelihood = -44158.308
Iteration 1: log likelihood = -44158.291
Iteration 2: log likelihood = -44158.291
Structural equation model Number of obs = 4,479
Estimation method: mlmv
Log likelihood = -44158.291
( 1) - [sum_helpfromchildren2]sum_helpfromchildren1 + [sum_helpfromchildren3]sum_helpfromchildren2 = 0
( 2) - [sum_helpfromchildren2]sum_helpfromchildren1 + [sum_helpfromchildren4]sum_helpfromchildren3 = 0
( 3) [sum_helpfromchildren2]alter2 - [sum_helpfromchildren4]alter4 = 0
( 4) [sum_helpfromchildren2]spouse2 - [sum_helpfromchildren4]spouse4 = 0
( 5) [sum_helpfromchildren2]anzkind2 - [sum_helpfromchildren4]anzkind4 = 0
( 6) [sum_helpfromchildren2]srh2 - [sum_helpfromchildren4]srh4 = 0
( 7) [sum_helpfromchildren2]srh1 - [sum_helpfromchildren4]srh3 = 0
( 8) [sum_helpfromchildren2]Alpha = 1
( 9) [sum_helpfromchildren2]E2 = 1
(10) [sum_helpfromchildren3]srh2 - [sum_helpfromchildren4]srh3 = 0
(11) [sum_helpfromchildren3]alter3 - [sum_helpfromchildren4]alter4 = 0
(12) [sum_helpfromchildren3]spouse3 - [sum_helpfromchildren4]spouse4 = 0
(13) [sum_helpfromchildren3]anzkind3 - [sum_helpfromchildren4]anzkind4 = 0
(14) [sum_helpfromchildren3]srh3 - [sum_helpfromchildren4]srh4 = 0
(15) [sum_helpfromchildren3]Alpha = 1
(16) [sum_helpfromchildren3]E3 = 1
(17) [sum_helpfromchildren4]Alpha = 1
(18) [var(e.sum_helpfromchildren2)]_cons = 0
(19) [var(e.sum_helpfromchildren3)]_cons = 0
(20) [var(e.sum_helpfromchildren4)]_cons - [var(E3)]_cons = 0
(21) [var(E2)]_cons - [var(E3)]_cons = 0
----------------------------------------------------------------------------------------------------
| OIM
| Coefficient std. err. z P>|z| [95% conf. interval]
-----------------------------------+----------------------------------------------------------------
Structural |
sum_helpfromchildren2 |
sum_helpfromchildren1 | 0.078 0.021 3.82 0.000 0.038 0.119
alter2 | 0.001 0.016 0.06 0.951 -0.030 0.032
spouse2 | 0.000 0.023 0.02 0.984 -0.045 0.046
anzkind2 | 0.021 0.015 1.37 0.170 -0.009 0.052
srh2 | -0.004 0.015 -0.26 0.797 -0.034 0.026
srh1 | -0.001 0.010 -0.07 0.943 -0.020 0.019
Alpha | 1.000 0.000 1.9e+16 0.000 1.000 1.000
E2 | 1.000 0.000 4.2e+15 0.000 1.000 1.000
_cons | 0.010 1.063 0.01 0.992 -2.074 2.094
---------------------------------+----------------------------------------------------------------
sum_helpfromchildren3 |
sum_helpfromchildren2 | 0.078 0.021 3.82 0.000 0.038 0.119
srh2 | -0.001 0.010 -0.07 0.943 -0.020 0.019
alter3 | 0.001 0.016 0.06 0.951 -0.030 0.032
spouse3 | 0.000 0.023 0.02 0.984 -0.045 0.046
anzkind3 | 0.021 0.015 1.37 0.170 -0.009 0.052
srh3 | -0.004 0.015 -0.26 0.797 -0.034 0.026
Alpha | 1.000 (constrained)
E3 | 1.000 (constrained)
_cons | 0.019 1.111 0.02 0.987 -2.159 2.197
---------------------------------+----------------------------------------------------------------
sum_helpfromchildren4 |
sum_helpfromchildren3 | 0.078 0.021 3.82 0.000 0.038 0.119
srh3 | -0.001 0.010 -0.07 0.943 -0.020 0.019
alter4 | 0.001 0.016 0.06 0.951 -0.030 0.032
spouse4 | 0.000 0.023 0.02 0.984 -0.045 0.046
anzkind4 | 0.021 0.015 1.37 0.170 -0.009 0.052
srh4 | -0.004 0.015 -0.26 0.797 -0.034 0.026
Alpha | 1.000 (constrained)
_cons | 0.021 1.159 0.02 0.986 -2.251 2.292
-----------------------------------+----------------------------------------------------------------
mean(sum_helpfromchildren1)| 0.078 0.004 18.36 0.000 0.069 0.086
mean(alter2)| 66.066 0.155 426.37 0.000 65.763 66.370
mean(spouse2)| 0.746 0.007 113.39 0.000 0.733 0.758
mean(anzkind2)| 2.021 0.017 119.25 0.000 1.987 2.054
mean(srh2)| 3.482 0.013 264.47 0.000 3.457 3.508
mean(srh1)| 3.571 0.013 279.59 0.000 3.546 3.596
mean(alter3)| 69.068 0.155 445.80 0.000 68.764 69.372
mean(spouse3)| 0.718 0.007 104.78 0.000 0.705 0.732
mean(anzkind3)| 2.019 0.017 119.38 0.000 1.986 2.052
mean(srh3)| 3.440 0.013 258.61 0.000 3.414 3.466
mean(alter4)| 72.060 0.155 464.13 0.000 71.756 72.365
mean(spouse4)| 0.680 0.008 89.04 0.000 0.665 0.695
mean(anzkind4)| 2.009 0.017 116.86 0.000 1.975 2.043
mean(srh4)| 3.380 0.015 230.17 0.000 3.351 3.409
-----------------------------------+----------------------------------------------------------------
var(e.sum_helpfromchildren2)| 0.000 (constrained)
var(e.sum_helpfromchildren3)| 0.000 (constrained)
var(e.sum_helpfromchildren4)| 0.083 0.002 0.079 0.087
var(sum_helpfromchildren1)| 0.072 0.002 0.069 0.075
var(alter2)| 107.538 2.272 103.175 112.086
var(spouse2)| 0.189 0.004 0.181 0.197
var(anzkind2)| 1.270 0.027 1.218 1.325
var(srh2)| 0.714 0.016 0.683 0.746
var(srh1)| 0.675 0.015 0.646 0.705
var(alter3)| 107.505 2.272 103.143 112.051
var(spouse3)| 0.202 0.004 0.193 0.211
var(anzkind3)| 1.252 0.027 1.200 1.307
var(srh3)| 0.677 0.016 0.647 0.710
var(alter4)| 107.877 2.282 103.495 112.444
var(spouse4)| 0.218 0.005 0.208 0.229
var(anzkind4)| 1.241 0.028 1.187 1.298
var(srh4)| 0.674 0.018 0.639 0.711
var(Alpha)| 0.015 0.009 0.005 0.049
var(E2)| 0.083 0.002 0.079 0.087
var(E3)| 0.083 0.002 0.079 0.087
-----------------------------------+----------------------------------------------------------------
cov(sum_helpfromchildren1,alter2)| 0.295 0.044 6.74 0.000 0.209 0.381
cov(sum_helpfromchildren1,spouse2)| -0.007 0.002 -3.81 0.000 -0.011 -0.003
cov(sum_helpfromchildren1,anzkind2)| 0.030 0.005 6.30 0.000 0.021 0.040
cov(sum_helpfromchildren1,srh2)| -0.018 0.004 -4.77 0.000 -0.025 -0.010
cov(sum_helpfromchildren1,srh1)| -0.024 0.004 -6.74 0.000 -0.031 -0.017
cov(sum_helpfromchildren1,alter3)| 0.295 0.044 6.75 0.000 0.210 0.381
cov(sum_helpfromchildren1,spouse3)| -0.008 0.002 -3.97 0.000 -0.012 -0.004
cov(sum_helpfromchildren1,anzkind3)| 0.030 0.005 6.20 0.000 0.020 0.039
cov(sum_helpfromchildren1,srh3)| -0.017 0.004 -4.38 0.000 -0.024 -0.009
cov(sum_helpfromchildren1,alter4)| 0.293 0.044 6.68 0.000 0.207 0.379
cov(sum_helpfromchildren1,spouse4)| -0.008 0.002 -3.73 0.000 -0.013 -0.004
cov(sum_helpfromchildren1,anzkind4)| 0.028 0.005 5.60 0.000 0.018 0.037
cov(sum_helpfromchildren1,srh4)| -0.018 0.004 -4.15 0.000 -0.026 -0.009
cov(sum_helpfromchildren1,Alpha)| 0.015 0.005 2.99 0.003 0.005 0.025
cov(alter2,spouse2)| -0.584 0.069 -8.49 0.000 -0.719 -0.449
cov(alter2,anzkind2)| 0.796 0.176 4.52 0.000 0.451 1.141
cov(alter2,srh2)| -1.292 0.138 -9.35 0.000 -1.563 -1.021
cov(alter2,srh1)| -1.040 0.133 -7.85 0.000 -1.300 -0.780
cov(alter2,alter3)| 107.512 2.272 47.32 0.000 103.059 111.965
cov(alter2,spouse3)| -0.599 0.072 -8.30 0.000 -0.740 -0.457
cov(alter2,anzkind3)| 0.734 0.176 4.16 0.000 0.388 1.080
cov(alter2,srh3)| -1.258 0.142 -8.84 0.000 -1.537 -0.979
cov(alter2,alter4)| 107.640 2.276 47.29 0.000 103.179 112.101
cov(alter2,spouse4)| -0.870 0.083 -10.53 0.000 -1.032 -0.708
cov(alter2,anzkind4)| 0.618 0.181 3.42 0.001 0.264 0.972
cov(alter2,srh4)| -1.595 0.162 -9.82 0.000 -1.913 -1.276
cov(alter2,Alpha)| 0.272 1.728 0.16 0.875 -3.114 3.658
cov(spouse2,anzkind2)| 0.036 0.007 4.78 0.000 0.021 0.050
cov(spouse2,srh2)| 0.022 0.006 3.73 0.000 0.010 0.033
cov(spouse2,srh1)| 0.020 0.006 3.61 0.000 0.009 0.031
cov(spouse2,alter3)| -0.583 0.069 -8.48 0.000 -0.718 -0.449
cov(spouse2,spouse3)| 0.179 0.004 44.40 0.000 0.171 0.187
cov(spouse2,anzkind3)| 0.031 0.007 4.17 0.000 0.017 0.046
cov(spouse2,srh3)| 0.020 0.006 3.36 0.001 0.008 0.031
cov(spouse2,alter4)| -0.583 0.069 -8.46 0.000 -0.718 -0.448
cov(spouse2,spouse4)| 0.162 0.004 38.93 0.000 0.154 0.170
cov(spouse2,anzkind4)| 0.035 0.008 4.54 0.000 0.020 0.050
cov(spouse2,srh4)| 0.015 0.007 2.22 0.026 0.002 0.028
cov(spouse2,Alpha)| -0.010 0.010 -0.92 0.359 -0.030 0.011
cov(anzkind2,srh2)| -0.002 0.015 -0.12 0.908 -0.031 0.028
cov(anzkind2,srh1)| 0.005 0.014 0.36 0.722 -0.023 0.033
cov(anzkind2,alter3)| 0.790 0.176 4.49 0.000 0.445 1.136
cov(anzkind2,spouse3)| 0.045 0.008 5.76 0.000 0.030 0.061
cov(anzkind2,anzkind3)| 1.206 0.026 45.80 0.000 1.154 1.257
cov(anzkind2,srh3)| -0.003 0.015 -0.18 0.857 -0.033 0.028
cov(anzkind2,alter4)| 0.787 0.177 4.46 0.000 0.441 1.133
cov(anzkind2,spouse4)| 0.043 0.009 4.92 0.000 0.026 0.061
cov(anzkind2,anzkind4)| 1.181 0.026 44.81 0.000 1.130 1.233
cov(anzkind2,srh4)| -0.012 0.017 -0.70 0.483 -0.046 0.022
cov(anzkind2,Alpha)| 0.010 0.023 0.42 0.673 -0.036 0.055
cov(srh2,srh1)| 0.381 0.013 30.15 0.000 0.356 0.406
cov(srh2,alter3)| -1.289 0.138 -9.33 0.000 -1.560 -1.018
cov(srh2,spouse3)| 0.026 0.006 4.29 0.000 0.014 0.038
cov(srh2,anzkind3)| -0.001 0.015 -0.06 0.949 -0.031 0.029
cov(srh2,srh3)| 0.400 0.013 29.82 0.000 0.374 0.427
cov(srh2,alter4)| -1.302 0.139 -9.38 0.000 -1.574 -1.030
cov(srh2,spouse4)| 0.026 0.007 3.78 0.000 0.013 0.040
cov(srh2,anzkind4)| 0.007 0.015 0.48 0.633 -0.023 0.037
cov(srh2,srh4)| 0.368 0.014 25.47 0.000 0.339 0.396
cov(srh2,Alpha)| -0.024 0.024 -1.00 0.317 -0.072 0.023
cov(srh1,alter3)| -1.037 0.133 -7.82 0.000 -1.296 -0.777
cov(srh1,spouse3)| 0.023 0.006 3.90 0.000 0.011 0.035
cov(srh1,anzkind3)| 0.008 0.014 0.59 0.556 -0.020 0.037
cov(srh1,srh3)| 0.338 0.013 27.00 0.000 0.313 0.362
cov(srh1,alter4)| -1.053 0.133 -7.93 0.000 -1.313 -0.793
cov(srh1,spouse4)| 0.021 0.007 3.25 0.001 0.009 0.034
cov(srh1,anzkind4)| 0.006 0.015 0.38 0.701 -0.023 0.035
cov(srh1,srh4)| 0.327 0.014 24.03 0.000 0.300 0.353
cov(srh1,Alpha)| -0.021 0.020 -1.05 0.292 -0.059 0.018
cov(alter3,spouse3)| -0.598 0.072 -8.29 0.000 -0.740 -0.457
cov(alter3,anzkind3)| 0.728 0.176 4.13 0.000 0.383 1.074
cov(alter3,srh3)| -1.256 0.142 -8.83 0.000 -1.535 -0.977
cov(alter3,alter4)| 107.591 2.275 47.29 0.000 103.132 112.051
cov(alter3,spouse4)| -0.870 0.083 -10.53 0.000 -1.032 -0.708
cov(alter3,anzkind4)| 0.613 0.181 3.39 0.001 0.259 0.967
cov(alter3,srh4)| -1.595 0.162 -9.82 0.000 -1.913 -1.276
cov(alter3,Alpha)| 0.272 1.727 0.16 0.875 -3.113 3.657
cov(spouse3,anzkind3)| 0.041 0.008 5.20 0.000 0.025 0.056
cov(spouse3,srh3)| 0.025 0.006 4.04 0.000 0.013 0.036
cov(spouse3,alter4)| -0.599 0.072 -8.29 0.000 -0.741 -0.457
cov(spouse3,spouse4)| 0.183 0.004 40.61 0.000 0.174 0.192
cov(spouse3,anzkind4)| 0.046 0.008 5.72 0.000 0.030 0.061
cov(spouse3,srh4)| 0.018 0.007 2.73 0.006 0.005 0.032
cov(spouse3,Alpha)| -0.013 0.011 -1.20 0.232 -0.034 0.008
cov(anzkind3,srh3)| -0.007 0.015 -0.46 0.643 -0.037 0.023
cov(anzkind3,alter4)| 0.726 0.177 4.10 0.000 0.379 1.072
cov(anzkind3,spouse4)| 0.039 0.009 4.49 0.000 0.022 0.057
cov(anzkind3,anzkind4)| 1.189 0.027 44.62 0.000 1.137 1.242
cov(anzkind3,srh4)| -0.007 0.017 -0.42 0.671 -0.041 0.026
cov(anzkind3,Alpha)| 0.011 0.023 0.50 0.620 -0.033 0.056
cov(srh3,alter4)| -1.262 0.143 -8.85 0.000 -1.541 -0.982
cov(srh3,spouse4)| 0.024 0.007 3.62 0.000 0.011 0.038
cov(srh3,anzkind4)| -0.008 0.016 -0.52 0.602 -0.039 0.022
cov(srh3,srh4)| 0.401 0.014 27.82 0.000 0.373 0.429
cov(srh3,Alpha)| -0.025 0.025 -1.03 0.302 -0.074 0.023
cov(srh3,E2)| 0.003 0.007 0.47 0.637 -0.011 0.018
cov(alter4,spouse4)| -0.870 0.083 -10.51 0.000 -1.032 -0.708
cov(alter4,anzkind4)| 0.610 0.181 3.37 0.001 0.255 0.964
cov(alter4,srh4)| -1.604 0.163 -9.85 0.000 -1.923 -1.285
cov(alter4,Alpha)| 0.273 1.730 0.16 0.874 -3.117 3.664
cov(spouse4,anzkind4)| 0.048 0.009 5.43 0.000 0.031 0.065
cov(spouse4,srh4)| 0.019 0.007 2.61 0.009 0.005 0.033
cov(spouse4,Alpha)| -0.016 0.015 -1.06 0.290 -0.045 0.013
cov(anzkind4,srh4)| -0.006 0.017 -0.37 0.714 -0.040 0.027
cov(anzkind4,Alpha)| 0.012 0.022 0.58 0.564 -0.030 0.055
cov(srh4,Alpha)| -0.024 0.030 -0.81 0.417 -0.083 0.034
cov(srh4,E2)| 0.002 0.010 0.20 0.844 -0.017 0.021
cov(srh4,E3)| 0.006 0.007 0.74 0.459 -0.009 0.020
----------------------------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(23) = 27.12 Prob > chi2 = 0.2510
Highlights: ML-SEM of instrumental help on self-rated health
--------------------------------------------------------------------------------------
| OIM
sum_helpfromchildren | Coefficient std. err. z P>|z| [95% conf. interval]
---------------------+----------------------------------------------------------------
sum_helpfromchildren |
sum_helpfromchildren |
L1. | 0.078 0.021 3.82 0.000 0.038 0.119
|
alter | 0.001 0.016 0.06 0.951 -0.030 0.032
spouse | 0.000 0.023 0.02 0.984 -0.045 0.046
anzkind | 0.021 0.015 1.37 0.170 -0.009 0.052
|
srh |
--. | -0.004 0.015 -0.26 0.797 -0.034 0.026
L1. | -0.001 0.010 -0.07 0.943 -0.020 0.019
--------------------------------------------------------------------------------------
# of units = 4479. # of periods = 4. First dependent variable is from period 2.
Constants are free to vary across time periods
LR test of model vs. saturated: chi2(23) = 27.12, Prob > chi2 = 0.2510
IC Measures: BIC = 89552.43 AIC = 88610.58
Wald test of all coeff = 0: chi2(6) = 16.59, Prob > chi2 = 0.0109
----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Likelihood ratio |
chi2_ms(23) | 27.117 model vs. saturated
p > chi2 | 0.251
chi2_bs(45) | 1065.699 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.006 Root mean squared error of approximation
90% CI, lower bound | 0.000
upper bound | 0.014
pclose | 1.000 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Information criteria |
AIC | 88610.582 Akaike's information criterion
BIC | 89552.434 Bayesian information criterion
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 0.996 Comparative fit index
TLI | 0.992 Tucker–Lewis index
---------------------+------------------------------------------------------
Size of residuals |
CD | 0.198 Coefficient of determination
----------------------------------------------------------------------------
Note: SRMR is not reported because of missing values.
I already tried different options (e.g., only estimating the IV and DV without controls, which works perfectly in both programms).
Does anybody have an idea why the model does not converge/ can't be identified in mplus, but in stata?
Best,
Lisa
Comment