psex_RIRS_gr = ' Int_ethn =~ 1*idoc3_y1 + 1*idoc3_y2 + 1*idoc3_y3 Slo_ethn =~ 1*idoc3_y1 + 2*idoc3_y2 + 3*idoc3_y3 Int_ethn ~~ Slo_ethn Int_ethn ~ p1_idoc3 + country_2 + country_3 Slo_ethn ~ p1_idoc3 + country_2 + country_3 p1_idoc3 ~ country_2 + country_3 p1_idoc3 ~ 1 ' psex_RIRS_gr_fit <- growth(psex_RIRS_gr, data = CILS4EU, estimator='ML', missing = "FIML", group = 'p1_sex') Warning message: In lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable �p1_sex� contains missing values summary(psex_RIRS_gr_fit, standardized = TRUE, fit.measures = TRUE, modindices=TRUE) lavaan 0.6-5 ended normally after 136 iterations Estimator ML Optimization method NLMINB Number of free parameters 36 Number of observations per group: 2 1116 1 434 Number of missing patterns per group: 2 16 1 16 Model Test User Model: Test statistic 4.868 Degrees of freedom 8 P-value (Chi-square) 0.772 Test statistic for each group: 2 3.738 1 1.129 Model Test Baseline Model: Test statistic 437.497 Degrees of freedom 28 P-value 0.000 User Model versus Baseline Model: Comparative Fit Index (CFI) 1.000 Tucker-Lewis Index (TLI) 1.027 Loglikelihood and Information Criteria: Loglikelihood user model (H0) -4345.659 Loglikelihood unrestricted model (H1) NA Akaike (AIC) 8763.317 Bayesian (BIC) 8955.774 Sample-size adjusted Bayesian (BIC) 8841.410 Root Mean Square Error of Approximation: RMSEA 0.000 90 Percent confidence interval - lower 0.000 90 Percent confidence interval - upper 0.029 P-value RMSEA <= 0.05 0.998 Standardized Root Mean Square Residual: SRMR 0.010 Parameter Estimates: Information Observed Observed information based on Hessian Standard errors Standard Group 1 [2]: Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn =~ idoc3_y1 1.000 0.844 1.027 idoc3_y2 1.000 0.844 0.984 idoc3_y3 1.000 0.844 1.032 Slo_ethn =~ idoc3_y1 1.000 0.271 0.330 idoc3_y2 2.000 0.543 0.632 idoc3_y3 3.000 0.814 0.995 Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn ~ p1_idoc3 0.159 0.060 2.632 0.008 0.188 0.151 country_2 0.356 0.151 2.354 0.019 0.422 0.191 country_3 0.057 0.174 0.331 0.741 0.068 0.026 Slo_ethn ~ p1_idoc3 0.043 0.028 1.556 0.120 0.159 0.128 country_2 -0.083 0.067 -1.239 0.215 -0.307 -0.139 country_3 0.000 0.078 0.001 0.999 0.000 0.000 p1_idoc3 ~ country_2 -0.059 0.090 -0.654 0.513 -0.059 -0.033 country_3 -0.189 0.107 -1.774 0.076 -0.189 -0.090 Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Int_ethn ~~ .Slo_ethn -0.168 0.065 -2.574 0.010 -0.767 -0.767 Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .p1_idoc3 3.244 0.084 38.685 0.000 3.244 4.046 .idoc3_y1 0.000 0.000 0.000 .idoc3_y2 0.000 0.000 0.000 .idoc3_y3 0.000 0.000 0.000 .Int_ethn 2.320 0.248 9.365 0.000 2.747 2.747 .Slo_ethn -0.079 0.113 -0.692 0.489 -0.290 -0.290 Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .idoc3_y1 0.227 0.057 4.001 0.000 0.227 0.336 .idoc3_y2 0.406 0.031 13.066 0.000 0.406 0.551 .idoc3_y3 0.309 0.062 4.946 0.000 0.309 0.461 .p1_idoc3 0.639 0.032 20.251 0.000 0.639 0.995 .Int_ethn 0.674 0.155 4.352 0.000 0.946 0.946 .Slo_ethn 0.071 0.031 2.294 0.022 0.966 0.966 Group 2 [1]: Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn =~ idoc3_y1 1.000 0.363 0.481 idoc3_y2 1.000 0.363 0.451 idoc3_y3 1.000 0.363 0.441 Slo_ethn =~ idoc3_y1 1.000 0.125 0.166 idoc3_y2 2.000 0.251 0.311 idoc3_y3 3.000 0.376 0.457 Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn ~ p1_idoc3 0.226 0.086 2.624 0.009 0.624 0.480 country_2 0.094 0.178 0.525 0.599 0.258 0.120 country_3 -0.258 0.208 -1.242 0.214 -0.710 -0.288 Slo_ethn ~ p1_idoc3 -0.011 0.041 -0.259 0.796 -0.085 -0.065 country_2 -0.081 0.084 -0.960 0.337 -0.643 -0.299 country_3 0.077 0.100 0.765 0.444 0.611 0.248 p1_idoc3 ~ country_2 -0.180 0.128 -1.403 0.161 -0.180 -0.109 country_3 -0.283 0.152 -1.861 0.063 -0.283 -0.149 Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Int_ethn ~~ .Slo_ethn 0.042 0.084 0.498 0.618 1.381 1.381 Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .p1_idoc3 3.387 0.118 28.727 0.000 3.387 4.401 .idoc3_y1 0.000 0.000 0.000 .idoc3_y2 0.000 0.000 0.000 .idoc3_y3 0.000 0.000 0.000 .Int_ethn 2.609 0.337 7.747 0.000 7.187 7.187 .Slo_ethn 0.021 0.161 0.129 0.897 0.166 0.166 Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .idoc3_y1 0.360 0.075 4.803 0.000 0.360 0.632 .idoc3_y2 0.330 0.042 7.858 0.000 0.330 0.509 .idoc3_y3 0.218 0.088 2.479 0.013 0.218 0.322 .p1_idoc3 0.586 0.045 12.948 0.000 0.586 0.990 .Int_ethn 0.079 0.189 0.419 0.675 0.602 0.602 .Slo_ethn 0.011 0.042 0.271 0.787 0.731 0.731 Modification Indices: lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox 1 Int_ethn =~ idoc3_y1 1 1 1 0.938 -0.020 -0.017 -0.020 -0.020 2 Int_ethn =~ idoc3_y2 1 1 1 0.938 0.010 0.008 0.010 0.010 3 Int_ethn =~ idoc3_y3 1 1 1 0.938 -0.020 -0.017 -0.020 -0.020 4 Slo_ethn =~ idoc3_y1 1 1 1 0.133 -0.472 -0.128 -0.156 -0.156 5 Slo_ethn =~ idoc3_y2 1 1 1 0.133 0.236 0.064 0.075 0.075 6 Slo_ethn =~ idoc3_y3 1 1 1 0.133 -0.472 -0.128 -0.157 -0.157 26 idoc3_y1 ~1 1 1 1 1.059 -0.064 -0.064 -0.078 -0.078 27 idoc3_y2 ~1 1 1 1 1.059 0.032 0.032 0.037 0.037 28 idoc3_y3 ~1 1 1 1 1.059 -0.064 -0.064 -0.079 -0.079 33 Int_ethn =~ idoc3_y1 2 2 1 0.673 0.022 0.008 0.011 0.011 34 Int_ethn =~ idoc3_y2 2 2 1 0.526 -0.010 -0.004 -0.004 -0.004 35 Int_ethn =~ idoc3_y3 2 2 1 1.521 0.036 0.013 0.016 0.016 36 Slo_ethn =~ idoc3_y1 2 2 1 16.398 11.817 1.481 1.962 1.962 37 Slo_ethn =~ idoc3_y2 2 2 1 2.499 1.027 0.129 0.160 0.160 58 idoc3_y1 ~1 2 2 1 0.564 0.068 0.068 0.090 0.090 59 idoc3_y2 ~1 2 2 1 0.712 -0.038 -0.038 -0.047 -0.047 60 idoc3_y3 ~1 2 2 1 1.157 0.104 0.104 0.126 0.126 67 idoc3_y1 ~~ p1_idoc3 1 1 1 0.191 0.024 0.024 0.063 0.063 69 idoc3_y2 ~~ p1_idoc3 1 1 1 0.191 -0.012 -0.012 -0.024 -0.024 70 idoc3_y3 ~~ p1_idoc3 1 1 1 0.191 0.024 0.024 0.054 0.054 85 idoc3_y1 ~~ p1_idoc3 2 2 1 0.116 -0.026 -0.026 -0.057 -0.057 87 idoc3_y2 ~~ p1_idoc3 2 2 1 0.188 0.016 0.016 0.037 0.037 88 idoc3_y3 ~~ p1_idoc3 2 2 1 0.040 -0.016 -0.016 -0.046 -0.046 Warning message: In lav_start_check_cov(lavpartable = lavpartable, start = START) : lavaan WARNING: starting values imply a correlation larger than 1; variables involved are: Int_ethn Slo_ethn [in block 2] psex_RIRS_gr_fit2 <- growth(psex_RIRS_gr, data = CILS4EU, estimator='ML', missing = "FIML", group = 'p1_sex', group.equal = c("lv.covariances")) Warning message: In lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable �p1_sex� contains missing values summary(psex_RIRS_gr_fit2, standardized = TRUE, fit.measures = TRUE, modindices=TRUE) lavaan 0.6-5 ended normally after 129 iterations Estimator ML Optimization method NLMINB Number of free parameters 36 Number of equality constraints 1 Row rank of the constraints matrix 1 Number of observations per group: 2 1116 1 434 Number of missing patterns per group: 2 16 1 16 Model Test User Model: Test statistic 8.806 Degrees of freedom 9 P-value (Chi-square) 0.455 Test statistic for each group: 2 5.277 1 3.529 Model Test Baseline Model: Test statistic 437.497 Degrees of freedom 28 P-value 0.000 User Model versus Baseline Model: Comparative Fit Index (CFI) 1.000 Tucker-Lewis Index (TLI) 1.001 Loglikelihood and Information Criteria: Loglikelihood user model (H0) -4347.628 Loglikelihood unrestricted model (H1) NA Akaike (AIC) 8765.256 Bayesian (BIC) 8952.366 Sample-size adjusted Bayesian (BIC) 8841.180 Root Mean Square Error of Approximation: RMSEA 0.000 90 Percent confidence interval - lower 0.000 90 Percent confidence interval - upper 0.040 P-value RMSEA <= 0.05 0.991 Standardized Root Mean Square Residual: SRMR 0.014 Parameter Estimates: Information Observed Observed information based on Hessian Standard errors Standard Group 1 [2]: Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn =~ idoc3_y1 1.000 0.726 0.883 idoc3_y2 1.000 0.726 0.855 idoc3_y3 1.000 0.726 0.878 Slo_ethn =~ idoc3_y1 1.000 0.190 0.231 idoc3_y2 2.000 0.379 0.447 idoc3_y3 3.000 0.569 0.689 Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn ~ p1_idoc3 0.162 0.060 2.708 0.007 0.223 0.179 country_2 0.343 0.149 2.302 0.021 0.472 0.214 country_3 0.050 0.171 0.295 0.768 0.069 0.027 Slo_ethn ~ p1_idoc3 0.042 0.027 1.518 0.129 0.220 0.176 country_2 -0.080 0.066 -1.200 0.230 -0.419 -0.190 country_3 0.000 0.077 0.006 0.996 0.002 0.001 p1_idoc3 ~ country_2 -0.059 0.090 -0.651 0.515 -0.059 -0.033 country_3 -0.189 0.107 -1.775 0.076 -0.189 -0.090 Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Int_ethn ~~ .Slo_thn (.p7.) -0.087 0.052 -1.662 0.097 -0.675 -0.675 Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .p1_idoc3 3.243 0.084 38.681 0.000 3.243 4.046 .idoc3_y1 0.000 0.000 0.000 .idoc3_y2 0.000 0.000 0.000 .idoc3_y3 0.000 0.000 0.000 .Int_ethn 2.324 0.245 9.497 0.000 3.203 3.203 .Slo_ethn -0.078 0.112 -0.691 0.489 -0.409 -0.409 Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .idoc3_y1 0.288 0.050 5.738 0.000 0.288 0.426 .idoc3_y2 0.400 0.031 12.907 0.000 0.400 0.555 .idoc3_y3 0.357 0.061 5.842 0.000 0.357 0.522 .p1_idoc3 0.639 0.032 20.250 0.000 0.639 0.995 .Int_ethn 0.489 0.124 3.948 0.000 0.928 0.928 .Slo_ethn 0.034 0.026 1.311 0.190 0.935 0.935 Group 2 [1]: Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn =~ idoc3_y1 1.000 0.642 0.854 idoc3_y2 1.000 0.642 0.783 idoc3_y3 1.000 0.642 0.791 Slo_ethn =~ idoc3_y1 1.000 0.277 0.369 idoc3_y2 2.000 0.554 0.676 idoc3_y3 3.000 0.832 1.025 Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Int_ethn ~ p1_idoc3 0.221 0.088 2.507 0.012 0.345 0.265 country_2 0.085 0.185 0.461 0.645 0.133 0.062 country_3 -0.267 0.215 -1.238 0.216 -0.415 -0.168 Slo_ethn ~ p1_idoc3 -0.008 0.042 -0.189 0.850 -0.029 -0.022 country_2 -0.078 0.087 -0.895 0.371 -0.282 -0.131 country_3 0.081 0.104 0.780 0.436 0.292 0.118 p1_idoc3 ~ country_2 -0.180 0.128 -1.406 0.160 -0.180 -0.109 country_3 -0.284 0.152 -1.868 0.062 -0.284 -0.150 Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Int_ethn ~~ .Slo_thn (.p7.) -0.087 0.052 -1.662 0.097 -0.535 -0.535 Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .p1_idoc3 3.387 0.118 28.735 0.000 3.387 4.402 .idoc3_y1 0.000 0.000 0.000 .idoc3_y2 0.000 0.000 0.000 .idoc3_y3 0.000 0.000 0.000 .Int_ethn 2.627 0.346 7.591 0.000 4.092 4.092 .Slo_ethn 0.014 0.166 0.082 0.935 0.049 0.049 Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .idoc3_y1 0.271 0.056 4.856 0.000 0.271 0.479 .idoc3_y2 0.341 0.042 8.197 0.000 0.341 0.507 .idoc3_y3 0.137 0.073 1.886 0.059 0.137 0.208 .p1_idoc3 0.586 0.045 12.951 0.000 0.586 0.990 .Int_ethn 0.361 0.129 2.793 0.005 0.876 0.876 .Slo_ethn 0.073 0.028 2.563 0.010 0.945 0.945 Modification Indices: lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox 1 Int_ethn =~ idoc3_y1 1 1 1 1.050 -0.021 -0.015 -0.018 -0.018 2 Int_ethn =~ idoc3_y2 1 1 1 1.357 0.012 0.009 0.010 0.010 3 Int_ethn =~ idoc3_y3 1 1 1 1.606 -0.026 -0.019 -0.023 -0.023 4 Slo_ethn =~ idoc3_y1 1 1 1 2.056 -1.536 -0.291 -0.354 -0.354 5 Slo_ethn =~ idoc3_y2 1 1 1 0.268 -0.319 -0.061 -0.071 -0.071 6 Slo_ethn =~ idoc3_y3 1 1 1 1.684 1.089 0.207 0.250 0.250 23 country_2 ~~ country_2 1 1 1 0.000 0.000 0.000 0.000 0.000 24 country_2 ~~ country_3 1 1 1 0.000 0.000 0.000 NA 0.000 26 idoc3_y1 ~1 1 1 1 1.185 -0.069 -0.069 -0.084 -0.084 27 idoc3_y2 ~1 1 1 1 1.185 0.034 0.034 0.041 0.041 28 idoc3_y3 ~1 1 1 1 1.185 -0.069 -0.069 -0.083 -0.083 33 Int_ethn =~ idoc3_y1 2 2 1 0.465 0.018 0.011 0.015 0.015 34 Int_ethn =~ idoc3_y2 2 2 1 0.690 -0.011 -0.007 -0.008 -0.008 35 Int_ethn =~ idoc3_y3 2 2 1 0.873 0.024 0.015 0.019 0.019 36 Slo_ethn =~ idoc3_y1 2 2 1 2.819 0.857 0.237 0.316 0.316 37 Slo_ethn =~ idoc3_y2 2 2 1 0.207 -0.236 -0.065 -0.080 -0.080 38 Slo_ethn =~ idoc3_y3 2 2 1 0.922 -0.875 -0.243 -0.299 -0.299 55 country_2 ~~ country_2 2 2 1 0.000 0.000 0.000 0.000 0.000 56 country_2 ~~ country_3 2 2 1 0.000 0.000 0.000 NA 0.000 57 country_3 ~~ country_3 2 2 1 0.000 0.000 0.000 0.000 0.000 58 idoc3_y1 ~1 2 2 1 0.561 0.065 0.065 0.086 0.086 59 idoc3_y2 ~1 2 2 1 0.561 -0.032 -0.032 -0.040 -0.040 60 idoc3_y3 ~1 2 2 1 0.561 0.065 0.065 0.080 0.080 66 idoc3_y1 ~~ idoc3_y2 1 1 1 3.605 0.128 0.128 0.378 0.378 67 idoc3_y1 ~~ idoc3_y3 1 1 1 3.605 -0.096 -0.096 -0.300 -0.300 68 idoc3_y1 ~~ p1_idoc3 1 1 1 0.170 0.023 0.023 0.054 0.054 69 idoc3_y2 ~~ idoc3_y3 1 1 1 3.605 0.385 0.385 1.018 1.018 70 idoc3_y2 ~~ p1_idoc3 1 1 1 0.170 -0.012 -0.012 -0.023 -0.023 71 idoc3_y3 ~~ p1_idoc3 1 1 1 0.170 0.023 0.023 0.048 0.048 72 Int_ethn ~ Slo_ethn 1 1 1 3.605 -5.715 -1.494 -1.494 -1.494 73 Slo_ethn ~ Int_ethn 1 1 1 3.605 -0.393 -1.505 -1.505 -1.505 76 country_2 ~ Int_ethn 1 1 1 0.000 0.001 0.001 0.001 0.001 77 country_2 ~ Slo_ethn 1 1 1 0.000 -0.009 -0.002 -0.004 -0.004 78 country_2 ~ p1_idoc3 1 1 1 0.000 -0.647 -0.647 -1.145 -1.145 79 country_2 ~ country_3 1 1 1 0.000 0.000 0.000 0.000 0.000 83 country_3 ~ country_2 1 1 1 0.000 0.000 0.000 0.000 0.000 84 idoc3_y1 ~~ idoc3_y2 2 2 1 3.605 -0.128 -0.128 -0.422 -0.422 85 idoc3_y1 ~~ idoc3_y3 2 2 1 3.605 0.096 0.096 0.500 0.500 86 idoc3_y1 ~~ p1_idoc3 2 2 1 0.037 -0.014 -0.014 -0.035 -0.035 87 idoc3_y2 ~~ idoc3_y3 2 2 1 3.605 -0.385 -0.385 -1.780 -1.780 88 idoc3_y2 ~~ p1_idoc3 2 2 1 0.037 0.007 0.007 0.016 0.016 89 idoc3_y3 ~~ p1_idoc3 2 2 1 0.037 -0.014 -0.014 -0.050 -0.050 90 Int_ethn ~ Slo_ethn 2 2 1 3.605 2.650 1.144 1.144 1.144 91 Slo_ethn ~ Int_ethn 2 2 1 3.605 0.533 1.234 1.234 1.234 94 country_2 ~ Int_ethn 2 2 1 0.000 -0.016 -0.010 -0.022 -0.022 95 country_2 ~ Slo_ethn 2 2 1 0.000 -0.011 -0.003 -0.007 -0.007 96 country_2 ~ p1_idoc3 2 2 1 0.000 -0.288 -0.288 -0.476 -0.476 97 country_2 ~ country_3 2 2 1 0.000 0.000 0.000 0.000 0.000 98 country_3 ~ Int_ethn 2 2 1 0.000 -0.006 -0.004 -0.009 -0.009 99 country_3 ~ Slo_ethn 2 2 1 0.000 -0.005 -0.001 -0.003 -0.003 100 country_3 ~ p1_idoc3 2 2 1 0.000 0.009 0.009 0.018 0.018 101 country_3 ~ country_2 2 2 1 0.000 0.000 0.000 0.000 0.000 anova(psex_RIRS_gr_fit, psex_RIRS_gr_fit2) Chi-Squared Difference Test Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq) psex_RIRS_gr_fit 8 8763.3 8955.8 4.8675 psex_RIRS_gr_fit2 9 8765.3 8952.4 8.8061 3.9385 1 0.04719 * --- Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1