SEM model bad fit

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Vera

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Jul 8, 2020, 7:14:05 AM7/8/20

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Dear all,

I am performing structural equation modeling, but I can't quite get the model to be a good fit. If anyone has the time to look at my model I would really appreciate it!

Here is the model:

#define measurement part

SMC =~ S_M9 + S_M1 + S_M2 + S_M3 + S_M4

+ S_M5 + S_M6 + S_M7 + S_M8 + S_M10

+ S_M11 + S_M12 + S_M13 + S_M14 + S_M15

+ S_M16 + S_M17 + S_M18 + S_M19 + S_M20

+ S_M21 + S_M22 + S_M23 + S_M24 + S_M25

+ S_M26 + S_M27 + S_M28 + S_M29 + S_M30

am =~ Motivation7 + Motivation2 + Motivation11 + Motivation13 + Motivation15 + Motivation4 + Motivation8 + Motivation16

cm =~ Motivation12 + Motivation1 + Motivation3 + Motivation5 +Motivation14 + Motivation9 + Motivation10 + Motivation6

rs =~ Needs9 + Needs3 + Needs15 + Needs21

rf =~ Needs22 + Needs4 + Needs10 + Needs16

cs =~ Needs19 + Needs1 + Needs7 + Needs13

cf =~ Needs20 + Needs8 + Needs2 + Needs14

as =~ Needs17 + Needs5 + Needs11 + Needs23

af =~ Needs24 + Needs6 + Needs12 + Needs18

academicperformance =~ Final_grade + Grade_2 + Grade_1 + Past_performance_mathematics

#define structural part

rs ~ SMC

rf ~ SMC

cs ~ SMC

cf ~ SMC

as ~ SMC

af ~ SMC

am ~ rs + rf + cs + as + af + cf

cm ~ rs + rf + cs + as + af + cf

academicperformance ~ am + cm

#residual covariances

S_M22 ~~ S_M23

S_M9 ~~ S_M10

S_M28 ~~ S_M29

S_M19 ~~ S_M20

S_M21 ~~ S_M24

S_M2 ~~ S_M15

S_M24 ~~ S_M27

S_M25 ~~ S_M27

S_M7 ~~ S_M8

S_M29 ~~ S_M30

S_M15 ~~ S_M16

S_M11 ~~ S_M12

S_M3 ~~ S_M6

S_M24 ~~ S_M25

S_M3 ~~ S_M14

S_M3 ~~ S_M4

S_M28 ~~ S_M30

S_M9 ~~ S_M16

S_M12 ~~ S_M18

S_M14 ~~ S_M16

Motivation5 ~~ Motivation12

Motivation1 ~~ Motivation14

Motivation6 ~~ Motivation10

Motivation6 ~~ Motivation9

Motivation9 ~~ Motivation10

Motivation8 ~~ Motivation13

Motivation13 ~~ Motivation16

Motivation8 ~~ Motivation16

Motivation7 ~~ Motivation15

Motivation4 ~~ Motivation13

Needs15 ~~ Needs21

Needs18 ~~ Needs6

Needs2 ~~ Needs8

Needs7 ~~ Needs13

And the output:

lavaan 0.6-5 ended normally after 472 iterations

Estimator ML

Optimization method NLMINB

Number of free parameters 202

Number of observations 220

Model Test User Model:

Test statistic 4307.523

Degrees of freedom 2573

P-value (Chi-square) 0.000

Model Test Baseline Model:

Test statistic 11932.346

Degrees of freedom 2701

P-value 0.000

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.812

Tucker-Lewis Index (TLI) 0.803

Loglikelihood and Information Criteria:

Loglikelihood user model (H0) -24202.188

Loglikelihood unrestricted model (H1) NA

Akaike (AIC) 48808.375

Bayesian (BIC) 49493.888

Sample-size adjusted Bayesian (BIC) 48853.749

Root Mean Square Error of Approximation:

RMSEA 0.055

90 Percent confidence interval - lower 0.052

90 Percent confidence interval - upper 0.058

P-value RMSEA <= 0.05 0.001

Standardized Root Mean Square Residual:

SRMR 0.106

Parameter Estimates:

Information Expected

Information saturated (h1) model Structured

Standard errors Standard

Latent Variables:

Estimate Std.Err z-value P(>|z|) Std.lv Std.all

SMC =~

S_M9 1.000 1.427 0.812

S_M1 0.642 0.069 9.331 0.000 0.916 0.593

S_M2 0.553 0.072 7.725 0.000 0.789 0.505

S_M3 0.608 0.065 9.359 0.000 0.868 0.597

S_M4 0.594 0.056 10.579 0.000 0.848 0.658

S_M5 0.265 0.047 5.647 0.000 0.379 0.379

S_M6 0.704 0.059 11.994 0.000 1.005 0.725

S_M7 0.870 0.068 12.871 0.000 1.241 0.765

S_M8 0.688 0.053 12.994 0.000 0.981 0.771

S_M10 0.813 0.036 22.466 0.000 1.160 0.845

S_M11 0.597 0.049 12.242 0.000 0.853 0.737

S_M12 0.553 0.054 10.275 0.000 0.789 0.644

S_M13 0.313 0.050 6.299 0.000 0.447 0.420

S_M14 0.692 0.055 12.612 0.000 0.987 0.754

S_M15 0.650 0.062 10.562 0.000 0.928 0.658

S_M16 0.742 0.049 15.063 0.000 1.060 0.803

S_M17 0.261 0.046 5.622 0.000 0.373 0.378

S_M18 0.595 0.053 11.314 0.000 0.850 0.694

S_M19 0.314 0.045 6.898 0.000 0.448 0.456

S_M20 0.318 0.041 7.663 0.000 0.453 0.501

S_M21 0.480 0.058 8.325 0.000 0.685 0.538

S_M22 0.204 0.039 5.177 0.000 0.292 0.350

S_M23 0.231 0.041 5.615 0.000 0.330 0.377

S_M24 0.284 0.048 5.959 0.000 0.406 0.399

S_M25 0.192 0.057 3.366 0.001 0.274 0.231

S_M26 0.646 0.058 11.219 0.000 0.922 0.689

S_M27 0.134 0.048 2.764 0.006 0.191 0.191

S_M28 0.149 0.045 3.293 0.001 0.212 0.226

S_M29 0.195 0.046 4.272 0.000 0.278 0.291

S_M30 0.210 0.044 4.734 0.000 0.300 0.321

am =~

Motivation7 1.000 0.921 0.804

Motivation2 0.932 0.069 13.446 0.000 0.858 0.829

Motivation11 0.944 0.068 13.809 0.000 0.869 0.849

Motivation13 0.599 0.093 6.406 0.000 0.551 0.440

Motivation15 0.971 0.067 14.577 0.000 0.894 0.778

Motivation4 0.579 0.057 10.228 0.000 0.533 0.663

Motivation8 0.508 0.067 7.566 0.000 0.467 0.510

Motivation16 0.486 0.071 6.844 0.000 0.447 0.466

cm =~

Motivation12 1.000 1.013 0.663

Motivation1 0.739 0.119 6.188 0.000 0.749 0.525

Motivation3 0.839 0.112 7.482 0.000 0.850 0.692

Motivation5 0.935 0.082 11.416 0.000 0.948 0.714

Motivation14 0.605 0.108 5.599 0.000 0.613 0.467

Motivation9 0.750 0.119 6.305 0.000 0.760 0.550

Motivation10 0.787 0.113 6.972 0.000 0.797 0.624

Motivation6 0.587 0.097 6.017 0.000 0.594 0.520

rs =~

Needs9 1.000 1.065 0.899

Needs3 0.700 0.067 10.521 0.000 0.745 0.679

Needs15 0.848 0.068 12.443 0.000 0.903 0.823

Needs21 0.722 0.071 10.201 0.000 0.769 0.697

rf =~

Needs22 1.000 1.037 0.678

Needs4 0.819 0.083 9.845 0.000 0.850 0.777

Needs10 0.834 0.081 10.302 0.000 0.866 0.830

Needs16 0.761 0.075 10.118 0.000 0.789 0.806

cs =~

Needs19 1.000 1.018 0.689

Needs1 0.567 0.102 5.559 0.000 0.578 0.493

Needs7 0.543 0.098 5.537 0.000 0.553 0.522

Needs13 0.630 0.104 6.057 0.000 0.642 0.587

cf =~

Needs20 1.000 1.080 0.881

Needs8 0.893 0.113 7.883 0.000 0.965 0.613

Needs2 0.722 0.089 8.075 0.000 0.779 0.629

Needs14 0.776 0.092 8.475 0.000 0.838 0.642

as =~

Needs17 1.000 0.879 0.689

Needs5 0.920 0.089 10.279 0.000 0.809 0.823

Needs11 0.837 0.084 9.904 0.000 0.736 0.778

Needs23 0.851 0.087 9.744 0.000 0.749 0.762

af =~

Needs24 1.000 1.084 0.779

Needs6 0.841 0.087 9.648 0.000 0.912 0.734

Needs12 0.886 0.082 10.813 0.000 0.960 0.763

Needs18 0.953 0.086 11.058 0.000 1.033 0.835

academicperformance =~

Final_grade 1.000 52.821 0.977

Grade_2 0.256 0.008 33.867 0.000 13.537 0.954

Grade_1 0.118 0.005 23.237 0.000 6.239 0.869

Pst_prfrmnc_mt -0.006 0.012 -0.479 0.632 -0.299 -0.033

Regressions:

Estimate Std.Err z-value P(>|z|) Std.lv Std.all

rs ~

SMC 0.240 0.055 4.400 0.000 0.322 0.322

rf ~

SMC 0.041 0.054 0.760 0.447 0.057 0.057

cs ~

SMC 0.226 0.061 3.713 0.000 0.317 0.317

cf ~

SMC -0.044 0.057 -0.773 0.440 -0.059 -0.059

as ~

SMC 0.180 0.047 3.790 0.000 0.291 0.291

af ~

SMC -0.025 0.057 -0.435 0.664 -0.033 -0.033

am ~

rs -0.095 0.056 -1.704 0.088 -0.110 -0.110

rf -0.010 0.057 -0.178 0.859 -0.011 -0.011

cs 0.513 0.088 5.822 0.000 0.567 0.567

as 0.126 0.069 1.823 0.068 0.120 0.120

af 0.130 0.056 2.342 0.019 0.153 0.153

cf -0.261 0.059 -4.408 0.000 -0.307 -0.307

cm ~

rs -0.074 0.077 -0.961 0.336 -0.077 -0.077

rf -0.040 0.078 -0.511 0.610 -0.041 -0.041

cs 0.090 0.089 1.010 0.312 0.091 0.091

as -0.081 0.094 -0.858 0.391 -0.070 -0.070

af 0.222 0.079 2.814 0.005 0.237 0.237

cf 0.118 0.077 1.537 0.124 0.126 0.126

academicperformance ~

am 1.007 4.127 0.244 0.807 0.018 0.018

cm -3.233 4.047 -0.799 0.424 -0.062 -0.062

Covariances:

Estimate Std.Err z-value P(>|z|) Std.lv Std.all

.S_M22 ~~

.S_M23 0.412 0.051 8.002 0.000 0.412 0.651

.S_M9 ~~

.S_M10 0.424 0.068 6.224 0.000 0.424 0.565

.S_M28 ~~

.S_M29 0.458 0.065 7.098 0.000 0.458 0.549

.S_M19 ~~

.S_M20 0.333 0.053 6.332 0.000 0.333 0.486

.S_M21 ~~

.S_M24 0.360 0.070 5.176 0.000 0.360 0.360

.S_M2 ~~

.S_M15 0.482 0.101 4.752 0.000 0.482 0.335

.S_M24 ~~

.S_M27 0.281 0.061 4.614 0.000 0.281 0.306

.S_M25 ~~

.S_M27 0.346 0.080 4.310 0.000 0.346 0.305

.S_M7 ~~

.S_M8 0.231 0.067 3.454 0.001 0.231 0.273

.S_M29 ~~

.S_M30 0.412 0.062 6.675 0.000 0.412 0.509

.S_M15 ~~

.S_M16 0.223 0.058 3.868 0.000 0.223 0.267

.S_M11 ~~

.S_M12 0.182 0.053 3.413 0.001 0.182 0.249

.S_M3 ~~

.S_M6 0.203 0.076 2.681 0.007 0.203 0.183

.S_M24 ~~

.S_M25 0.217 0.070 3.116 0.002 0.217 0.201

.S_M3 ~~

.S_M14 0.205 0.068 3.010 0.003 0.205 0.204

.S_M4 0.288 0.079 3.652 0.000 0.288 0.254

.S_M28 ~~

.S_M30 0.331 0.059 5.596 0.000 0.331 0.410

.S_M9 ~~

.S_M16 0.133 0.046 2.866 0.004 0.133 0.165

.S_M12 ~~

.S_M18 0.162 0.058 2.789 0.005 0.162 0.195

.S_M14 ~~

.S_M16 0.092 0.047 1.971 0.049 0.092 0.136

.Motivation12 ~~

.Motivation5 0.508 0.121 4.186 0.000 0.508 0.478

.Motivation1 ~~

.Motivation14 0.616 0.118 5.243 0.000 0.616 0.438

.Motivation10 ~~

.Motivation6 0.476 0.090 5.271 0.000 0.476 0.489

.Motivation9 ~~

.Motivation6 0.554 0.101 5.513 0.000 0.554 0.493

.Motivation10 0.365 0.102 3.570 0.000 0.365 0.318

.Motivation13 ~~

.Motivation8 0.345 0.065 5.310 0.000 0.345 0.389

.Motivation16 0.348 0.069 5.023 0.000 0.348 0.363

.Motivation8 ~~

.Motivation16 0.194 0.049 3.959 0.000 0.194 0.290

.Motivation7 ~~

.Motivation15 0.124 0.046 2.718 0.007 0.124 0.252

.Motivation13 ~~

.Motivation4 0.135 0.044 3.108 0.002 0.135 0.200

.Needs15 ~~

.Needs21 0.238 0.058 4.128 0.000 0.238 0.482

.Needs6 ~~

.Needs18 0.229 0.076 2.995 0.003 0.229 0.398

.Needs8 ~~

.Needs2 0.247 0.106 2.327 0.020 0.247 0.206

.Needs7 ~~

.Needs13 0.174 0.073 2.377 0.017 0.174 0.218

Variances:

Estimate Std.Err z-value P(>|z|) Std.lv Std.all

.S_M9 1.049 0.113 9.277 0.000 1.049 0.340

.S_M1 1.544 0.152 10.127 0.000 1.544 0.648

.S_M2 1.822 0.178 10.259 0.000 1.822 0.745

.S_M3 1.362 0.134 10.171 0.000 1.362 0.644

.S_M4 0.944 0.095 9.981 0.000 0.944 0.567

.S_M5 0.854 0.082 10.377 0.000 0.854 0.856

.S_M6 0.910 0.093 9.749 0.000 0.910 0.474

.S_M7 1.089 0.115 9.493 0.000 1.089 0.414

.S_M8 0.659 0.070 9.462 0.000 0.659 0.406

.S_M10 0.538 0.061 8.801 0.000 0.538 0.286

.S_M11 0.612 0.063 9.690 0.000 0.612 0.457

.S_M12 0.879 0.088 10.017 0.000 0.879 0.586

.S_M13 0.936 0.090 10.346 0.000 0.936 0.824

.S_M14 0.739 0.077 9.583 0.000 0.739 0.431

.S_M15 1.131 0.112 10.083 0.000 1.131 0.568

.S_M16 0.617 0.066 9.307 0.000 0.617 0.354

.S_M17 0.836 0.081 10.378 0.000 0.836 0.857

.S_M18 0.779 0.079 9.865 0.000 0.779 0.519

.S_M19 0.764 0.074 10.313 0.000 0.764 0.792

.S_M20 0.614 0.060 10.265 0.000 0.614 0.749

.S_M21 1.148 0.112 10.217 0.000 1.148 0.710

.S_M22 0.611 0.059 10.396 0.000 0.611 0.878

.S_M23 0.656 0.063 10.378 0.000 0.656 0.858

.S_M24 0.871 0.083 10.520 0.000 0.871 0.841

.S_M25 1.329 0.127 10.451 0.000 1.329 0.947

.S_M26 0.941 0.095 9.888 0.000 0.941 0.525

.S_M27 0.968 0.092 10.463 0.000 0.968 0.964

.S_M28 0.834 0.080 10.452 0.000 0.834 0.949

.S_M29 0.836 0.080 10.427 0.000 0.836 0.915

.S_M30 0.782 0.075 10.412 0.000 0.782 0.897

.Motivation7 0.465 0.057 8.118 0.000 0.465 0.354

.Motivation2 0.334 0.042 7.880 0.000 0.334 0.312

.Motivation11 0.294 0.040 7.434 0.000 0.294 0.280

.Motivation13 1.269 0.123 10.323 0.000 1.269 0.807

.Motivation15 0.521 0.062 8.432 0.000 0.521 0.395

.Motivation4 0.362 0.038 9.584 0.000 0.362 0.560

.Motivation8 0.621 0.062 10.080 0.000 0.621 0.740

.Motivation16 0.721 0.071 10.165 0.000 0.721 0.783

.Motivation12 1.307 0.170 7.684 0.000 1.307 0.560

.Motivation1 1.473 0.158 9.303 0.000 1.473 0.724

.Motivation3 0.786 0.103 7.601 0.000 0.786 0.521

.Motivation5 0.863 0.123 7.040 0.000 0.863 0.490

.Motivation14 1.346 0.140 9.599 0.000 1.346 0.782

.Motivation9 1.329 0.148 8.986 0.000 1.329 0.697

.Motivation10 0.995 0.119 8.391 0.000 0.995 0.610

.Motivation6 0.953 0.104 9.185 0.000 0.953 0.730

.Needs9 0.270 0.073 3.689 0.000 0.270 0.192

.Needs3 0.651 0.071 9.122 0.000 0.651 0.539

.Needs15 0.389 0.062 6.238 0.000 0.389 0.323

.Needs21 0.625 0.075 8.366 0.000 0.625 0.514

.Needs22 1.262 0.139 9.057 0.000 1.262 0.540

.Needs4 0.475 0.060 7.872 0.000 0.475 0.397

.Needs10 0.339 0.051 6.658 0.000 0.339 0.312

.Needs16 0.336 0.046 7.257 0.000 0.336 0.350

.Needs19 1.149 0.175 6.561 0.000 1.149 0.526

.Needs1 1.036 0.114 9.130 0.000 1.036 0.757

.Needs7 0.816 0.095 8.604 0.000 0.816 0.727

.Needs13 0.784 0.098 7.995 0.000 0.784 0.656

.Needs20 0.337 0.106 3.166 0.002 0.337 0.224

.Needs8 1.548 0.177 8.765 0.000 1.548 0.624

.Needs2 0.928 0.108 8.608 0.000 0.928 0.604

.Needs14 1.000 0.115 8.709 0.000 1.000 0.587

.Needs17 0.857 0.097 8.862 0.000 0.857 0.526

.Needs5 0.313 0.047 6.637 0.000 0.313 0.323

.Needs11 0.353 0.046 7.659 0.000 0.353 0.395

.Needs23 0.406 0.051 7.951 0.000 0.406 0.420

.Needs24 0.760 0.106 7.162 0.000 0.760 0.393

.Needs6 0.713 0.098 7.302 0.000 0.713 0.462

.Needs12 0.664 0.088 7.552 0.000 0.664 0.418

.Needs18 0.464 0.085 5.482 0.000 0.464 0.303

.Final_grade 130.515 44.467 2.935 0.003 130.515 0.045

.Grade_2 17.981 3.287 5.471 0.000 17.981 0.089

.Grade_1 12.622 1.343 9.399 0.000 12.622 0.245

.Pst_prfrmnc_mt 83.347 7.947 10.488 0.000 83.347 0.999

SMC 2.038 0.283 7.208 0.000 1.000 1.000

.am 0.451 0.081 5.562 0.000 0.532 0.532

.cm 0.932 0.201 4.647 0.000 0.908 0.908

.rs 1.016 0.136 7.496 0.000 0.896 0.896

.rf 1.073 0.200 5.369 0.000 0.997 0.997

.cs 0.933 0.205 4.543 0.000 0.899 0.899

.cf 1.163 0.172 6.752 0.000 0.997 0.997

.as 0.707 0.131 5.409 0.000 0.915 0.915

.af 1.174 0.186 6.300 0.000 0.999 0.999

.academcprfrmnc 2778.698 280.627 9.902 0.000 0.996 0.996

Thank you in advance!

fabiusg...@gmail.com

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Aug 2, 2020, 7:01:27 PM8/2/20

to lavaan

Could it be a problem that your data is ordinally distributed? I would use a WLSM estimator in that case.

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