Hi all,
I am trying to run SEM on my dataset (n = 501). There are several issues with my data so the default ML doesn't seem to be an appropriate estimator:
1. My data is mostly categorical and non-normal. In the diagram below, I have indicated the nature of each of my variable (in red). Also, for my final outcome variable (currentbehav), it is a latent variable made up of an ordered variable (Freq of visit) and a binary variable (Fav activity), which I makes me wonder what the nature of the final outcome variable is.
2. I have missing data in several of my variables, a large proportion of it comes from the variable HHincomeperpersonK (household income per person) and favourite activity - young and now. I have checked my missing data, and it is unlikely to be missing completely at random (MCAR), so I think I should use FIML for the model.
I specified the model as follows and ran a SEM on it.
VBNfullmodel <-'
#measurement model
socioecon =~ Gender + Age + Nationality + Ethnicity + Religion + Relationship + HHincomeperpersonK + Housing_type
personality =~ Extraverted_Enthusiastic + Critical_quarrelsome + Dependable_Selfdiscipline + Anxious_easilyupset + Opentonewex_complex + Reserved_quiet + Sympathetic_warm + Disorganised_careless + Calm_emotionallystable + Conventional_uncreative
NEP=~ Limit + Modify + Interfere + Ingenuity + Abuse + NatRes + Rights + Industrial + Laws + Crisis + Spaceship + Rule + Balance + Learn + Eco_crsis
pastexposure =~ Act_after_school_outdoors + Freq_of_visit_young + Fav_activity_Young + Childhood_memory
currentbehav =~ 1*Freq_of_visit_now + 1*Fav_acitivity_Now
#regression
currentbehav ~ NEP
NEP ~ Instrumental + Relational + Intrinsic
Instrumental ~ personality + pastexposure
Relational ~ personality + pastexposure
Intrinsic ~ personality + pastexposure
personality ~ socioecon + pastexposure
pastexposure ~ socioecon
'
The code runs fine with no warning messages using ML as an estimator. However, I think the estimates produced using ML are unlikely to be reliable given that my final outcome variable (currentbehav) is not normal or continuous. After reading some threads from this google group and some SEM textbooks and guides, I decide to use MLR as an estimation method since I have missing data and MLR seems to be able to deal with it using FIML. However, when I changed the estimator to MLR, many warning messages were produced.
fit7 <- sem(VBNfullmodel, data = fulldata, orthogonal = TRUE, "MLR")
I am not sure if MLR is the appropriate estimation method in my case. I checked several model fit indices and found that the model using MLR has better fit index than the model using ML (although i'm not sure if i'm suppose to compare it this way) I am also thinking if my model is too complex, and if I should try to simplify it a little.
I will greatly appreciate any advice/comment. Thank you in advance!
There were 24 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, ... :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
2: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
3: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
4: In lavaan::lavaan(model = "\n#measurement model \nsocioecon =~ Male + Age + Single + HHincomeperpersonK + Housing_type\npersonality =~ Extraverted_Enthusiastic + Critical_quarrelsome + Dependable_Selfdiscipline + Anxious_easilyupset + Opentonewex_complex + Reserved_quiet + Sympathetic_warm + Disorganised_careless + Calm_emotionallystable + Conventional_uncreative\nNEP=~ Limit + Modify + Interfere + Ingenuity + Abuse + NatRes + Rights + Industrial + Laws + Crisis + Spaceship + Rule + Balance + Learn + Eco_crsis\npastexposure =~ Freq_of_visit_youngOrd + Act_after_school_outdoors + NatureAppreciation_Young + Childhood_memory \ncurrentbehav =~ NatureAppreciation_Now + Freq_of_visit_nowOrd\n\n#regression \ncurrentbehav ~ NEP \nNEP ~ Instrumental + Relational + Intrinsic \nInstrumental ~ personality + pastexposure \nRelational ~ personality + pastexposure \nIntrinsic ~ personality + pastexposure \npersonality ~ socioecon + pastexposure\npastexposure ~ socioecon\n", ... :
lavaan WARNING: not all elements of the gradient are (near) zero;
the optimizer may not have found a local solution;
use lavInspect(fit, "optim.gradient") to investigate
5: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, ... :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
6: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
7: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
8: In lavaan(slotOptions = structure(list(model.type = "sem", ... :
lavaan WARNING: not all elements of the gradient are (near) zero;
the optimizer may not have found a local solution;
use lavInspect(fit, "optim.gradient") to investigate
9: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, ... :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
10: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
11: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
12: In lavaan(slotOptions = structure(list(model.type = "sem", ... :
lavaan WARNING: not all elements of the gradient are (near) zero;
the optimizer may not have found a local solution;
use lavInspect(fit, "optim.gradient") to investigate
13: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, ... :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
14: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
15: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
16: In lavaan(slotOptions = structure(list(model.type = "sem", ... :
lavaan WARNING: not all elements of the gradient are (near) zero;
the optimizer may not have found a local solution;
use lavInspect(fit, "optim.gradient") to investigate
17: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
18: In sqrt(ETA2) : NaNs produced
19: In sqrt(ETA2) : NaNs produced
20: In sqrt(ETA2) : NaNs produced
21: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, ... :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
22: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
23: In lav_object_post_check(object) :
lavaan WARNING: some estimated lv variances are negative
24: In lavaan(slotOptions = structure(list(model.type = "sem", ... :
lavaan WARNING: not all elements of the gradient are (near) zero;
the optimizer may not have found a local solution;
use lavInspect(fit, "optim.gradient") to investigate
Convergence information:
The model converged on 5 imputed data sets
Standard errors could not be computed for data set(s) 1, 2, 3, 5
Try fitting the model to the individual data set(s) to diagnose problems. If they cannot be fixed, try inspecting the imputations. It may be necessary to reimpute the data with some restrictions imposed.
Heywood cases detected for data set(s) 1, 2, 3, 5
These are not necessarily a cause for concern, unless a pooled estimate is also a Heywood case.
Rubin's (1987) rules were used to pool point and SE estimates across 5 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
Parameter Estimates:
Information Expected
Information saturated (h1) model Unstructured
Standard Errors Standard
Latent Variables:
Estimate Std.Err t-value df P(>|t|)
socioecon =~
Male 1.000
Age -76.581 329.209 -0.233 NA NA
Single -0.050 14.970 -0.003 NA NA
HHincomprprsnK -0.056 14.907 -0.004 NA NA
Housing_type 0.012 8.383 0.001 NA NA
personality =~
Extrvrtd_Enths 1.000
Criticl_qrrlsm -0.224 12.788 -0.017 NA NA
Dpndbl_Slfdscp 0.902 18.849 0.048 NA NA
Anxious_slypst -0.583 15.102 -0.039 NA NA
Opentnwx_cmplx 0.704 16.742 0.042 NA NA
Reserved_quiet -0.676 16.591 -0.041 NA NA
Sympathetc_wrm 0.679 16.116 0.042 NA NA
Disrgnsd_crlss -0.623 15.382 -0.040 NA NA
Clm_mtnllystbl 0.732 16.293 0.045 NA NA
Convntnl_ncrtv -0.229 13.001 -0.018 NA NA
NEP =~
Limit 1.000
Modify -0.688 14.088 -0.049 NA NA
Interfere 0.891 15.369 0.058 NA NA
Ingenuity -0.690 13.512 -0.051 NA NA
Abuse 1.414 19.562 0.072 NA NA
NatRes -0.111 11.328 -0.010 NA NA
Rights 0.907 15.603 0.058 NA NA
Industrial -1.060 16.042 -0.066 NA NA
Laws 0.954 15.641 0.061 NA NA
Crisis -1.202 17.186 -0.070 NA NA
Spaceship 0.831 14.965 0.056 NA NA
Rule -0.852 14.985 -0.057 NA NA
Balance 0.792 14.606 0.054 NA NA
Learn -0.717 13.498 -0.053 NA NA
Eco_crsis 1.531 21.218 0.072 NA NA
pastexposure =~
Frq_f_vst_yngO 1.000
Act_ftr_schl_t 1.218 27.356 0.045 NA NA
NtrApprctn_Yng -0.258 23.408 -0.011 NA NA
Childhood_mmry 1.052 22.516 0.047 NA NA
currentbehav =~
NatrApprctn_Nw 1.000
Frq_f_vst_nwOr 0.400 31.514 0.013 NA NA
Regressions:
Estimate Std.Err t-value df P(>|t|)
currentbehav ~
NEP 0.403 18.462 0.022 NA NA
NEP ~
Instrumental -347.255 48.513 -7.158 NA NA
Relational 320.378 26.176 12.239 NA NA
Intrinsic -0.658 33.511 -0.020 NA NA
Instrumental ~
personality -4.797 195.095 -0.025 NA NA
pastexposure 6.307 220.417 0.029 NA NA
Relational ~
personality -5.134 195.332 -0.026 NA NA
pastexposure 6.769 221.153 0.031 NA NA
Intrinsic ~
personality -4.646 215.469 -0.022 NA NA
pastexposure 6.022 243.228 0.025 NA NA
personality ~
socioecon 0.008 1.093 0.007 NA NA
pastexposure 1.186 24.030 0.049 NA NA
pastexposure ~
socioecon 0.030 2.828 0.010 NA NA
Intercepts:
Estimate Std.Err t-value df P(>|t|)
.Male 0.000
.Age 21.294 30.361 0.701 NA NA
.Single 0.000
.HHincomprprsnK 2.118 26.974 0.079 NA NA
.Housing_type 0.000
.Extrvrtd_Enths 0.000
.Criticl_qrrlsm 0.000
.Dpndbl_Slfdscp 0.000
.Anxious_slypst 0.000
.Opentnwx_cmplx 0.000
.Reserved_quiet 0.000
.Sympathetc_wrm 0.000
.Disrgnsd_crlss 0.000
.Clm_mtnllystbl 0.000
.Convntnl_ncrtv 0.000
.Limit 0.000
.Modify 0.000
.Interfere 0.000
.Ingenuity 0.000
.Abuse 0.000
.NatRes 0.000
.Rights 0.000
.Industrial 0.000
.Laws 0.000
.Crisis 0.000
.Spaceship 0.000
.Rule 0.000
.Balance 0.000
.Learn 0.000
.Eco_crsis 0.000
.Frq_f_vst_yngO 0.000
.Act_ftr_schl_t 0.000
.NtrApprctn_Yng 0.000
.Childhood_mmry 0.000
.NatrApprctn_Nw 0.000
.Frq_f_vst_nwOr 0.000
.Instrumental 3.959 9.471 0.418 NA NA
.Relational 3.868 10.055 0.385 NA NA
.Intrinsic 4.172 10.493 0.398 NA NA
socioecon 0.000
.personality 0.000
.NEP 0.000
.pastexposure 0.000
.currentbehav 0.000
Thresholds:
Estimate Std.Err t-value df P(>|t|)
Male|t1 -0.065 14.224 -0.005 NA NA
Single|t1 -0.865 16.401 -0.053 NA NA
Housing_typ|t1 -2.597 51.544 -0.050 NA NA
Housing_typ|t2 -1.833 26.250 -0.070 NA NA
Housing_typ|t3 -1.782 25.388 -0.070 NA NA
Housing_typ|t4 -1.758 25.001 -0.070 NA NA
Housing_typ|t5 -0.986 16.889 -0.058 NA NA
Housing_typ|t6 -0.960 16.746 -0.057 NA NA
Housing_typ|t7 -0.693 15.433 -0.045 NA NA
Housing_typ|t8 0.107 14.254 0.007 NA NA
Housing_typ|t9 0.826 16.067 0.051 NA NA
Housng_typ|t10 0.878 16.322 0.054 NA NA
Housng_typ|t11 0.893 16.401 0.054 NA NA
Housng_typ|t12 0.902 16.482 0.055 NA NA
Extrvrtd_Ent|1 -1.775 26.250 -0.068 NA NA
Extrvrtd_Ent|2 -0.734 15.694 -0.047 NA NA
Extrvrtd_Ent|3 -0.225 14.343 -0.016 NA NA
Extrvrtd_Ent|4 0.053 14.219 0.004 NA NA
Extrvrtd_Ent|5 0.627 15.274 0.041 NA NA
Extrvrtd_Ent|6 1.775 26.250 0.068 NA NA
Crtcl_qrrlsm|1 -1.393 20.553 -0.068 NA NA
Crtcl_qrrlsm|2 -0.670 15.433 -0.043 NA NA
Crtcl_qrrlsm|3 -0.189 14.304 -0.013 NA NA
Crtcl_qrrlsm|4 0.184 14.299 0.013 NA NA
Crtcl_qrrlsm|5 1.038 17.375 0.060 NA NA
Crtcl_qrrlsm|6 1.912 29.136 0.066 NA NA
Dpndbl_Slfds|1 -2.145 35.572 -0.060 NA NA
Dpndbl_Slfds|2 -1.608 23.391 -0.069 NA NA
Dpndbl_Slfds|3 -1.055 17.496 -0.060 NA NA
Dpndbl_Slfds|4 -0.608 15.210 -0.040 NA NA
Dpndbl_Slfds|5 0.148 14.269 0.010 NA NA
Dpndbl_Slfds|6 1.448 21.198 0.068 NA NA
Anxs_slypst|t1 -1.556 22.621 -0.069 NA NA
Anxs_slypst|t2 -0.794 15.967 -0.050 NA NA
Anxs_slypst|t3 -0.350 14.532 -0.024 NA NA
Anxs_slypst|t4 0.018 14.213 0.001 NA NA
Anxs_slypst|t5 0.850 16.246 0.052 NA NA
Anxs_slypst|t6 1.523 22.168 0.069 NA NA
Opntnwx_cmpl|1 -2.879 79.970 -0.036 NA NA
Opntnwx_cmpl|2 -2.015 31.714 -0.064 NA NA
Opntnwx_cmpl|3 -1.238 18.977 -0.065 NA NA
Opntnwx_cmpl|4 -0.682 15.482 -0.044 NA NA
Opntnwx_cmpl|5 0.230 14.349 0.016 NA NA
Opntnwx_cmpl|6 1.196 18.603 0.064 NA NA
Reserved_qt|t1 -1.826 27.255 -0.067 NA NA
Reserved_qt|t2 -1.156 18.267 -0.063 NA NA
Reserved_qt|t3 -0.708 15.585 -0.045 NA NA
Reserved_qt|t4 -0.307 14.459 -0.021 NA NA
Reserved_qt|t5 0.376 14.584 0.026 NA NA
Reserved_qt|t6 1.318 19.744 0.067 NA NA
Sympthtc_wrm|1 -2.327 42.365 -0.055 NA NA
Sympthtc_wrm|2 -1.800 26.732 -0.067 NA NA
Sympthtc_wrm|3 -1.318 19.744 -0.067 NA NA
Sympthtc_wrm|4 -0.754 15.780 -0.048 NA NA
Sympthtc_wrm|5 0.033 14.215 0.002 NA NA
Sympthtc_wrm|6 1.186 18.516 0.064 NA NA
Dsrgnsd_crls|1 -1.448 21.198 -0.068 NA NA
Dsrgnsd_crls|2 -0.689 15.507 -0.044 NA NA
Dsrgnsd_crls|3 -0.168 14.285 -0.012 NA NA
Dsrgnsd_crls|4 0.184 14.299 0.013 NA NA
Dsrgnsd_crls|5 0.917 16.610 0.055 NA NA
Dsrgnsd_crls|6 1.800 26.732 0.067 NA NA
Clm_mtnllyst|1 -2.198 37.360 -0.059 NA NA
Clm_mtnllyst|2 -1.507 21.957 -0.069 NA NA
Clm_mtnllyst|3 -0.822 16.102 -0.051 NA NA
Clm_mtnllyst|4 -0.387 14.606 -0.027 NA NA
Clm_mtnllyst|5 0.425 14.689 0.029 NA NA
Clm_mtnllyst|6 1.462 21.376 0.068 NA NA
Cnvntnl_ncrt|1 -1.707 25.001 -0.068 NA NA
Cnvntnl_ncrt|2 -1.004 17.148 -0.059 NA NA
Cnvntnl_ncrt|3 -0.403 14.640 -0.028 NA NA
Cnvntnl_ncrt|4 0.204 14.320 0.014 NA NA
Cnvntnl_ncrt|5 1.064 17.558 0.061 NA NA
Cnvntnl_ncrt|6 2.145 35.572 0.060 NA NA
Limit|t1 -140.285 36.388 -3.855 NA NA
Limit|t2 -139.830 29.494 -4.741 NA NA
Limit|t3 -139.164 25.370 -5.485 NA NA
Limit|t4 -138.036 24.206 -5.703 NA NA
Modify|t1 93.793 25.938 3.616 NA NA
Modify|t2 94.852 20.740 4.573 NA NA
Modify|t3 95.273 20.441 4.661 NA NA
Modify|t4 96.573 24.610 3.924 NA NA
Interfere|t1 -125.305 38.394 -3.264 NA NA
Interfere|t2 -124.468 26.086 -4.771 NA NA
Interfere|t3 -124.016 23.868 -5.196 NA NA
Interfere|t4 -122.793 22.971 -5.346 NA NA
Ingenuity|t1 94.294 25.122 3.753 NA NA
Ingenuity|t2 95.240 20.640 4.614 NA NA
Ingenuity|t3 95.855 20.317 4.718 NA NA
Ingenuity|t4 96.993 24.368 3.980 NA NA
Abuse|t1 -198.013 57.783 -3.427 NA NA
Abuse|t2 -197.300 37.372 -5.279 NA NA
Abuse|t3 -196.749 32.341 -6.084 NA NA
Abuse|t4 -195.377 29.751 -6.567 NA NA
NatRes|t1 13.611 27.603 0.493 NA NA
NatRes|t2 14.406 19.014 0.758 NA NA
NatRes|t3 14.977 16.958 0.883 NA NA
NatRes|t4 16.153 17.994 0.898 NA NA
Rights|t1 -127.403 37.452 -3.402 NA NA
Rights|t2 -126.769 27.593 -4.594 NA NA
Rights|t3 -126.370 24.979 -5.059 NA NA
Rights|t4 -125.341 22.979 -5.454 NA NA
Industrial|t1 145.995 25.448 5.737 NA NA
Industrial|t2 146.904 24.650 5.960 NA NA
Industrial|t3 147.300 25.139 5.859 NA NA
Industrial|t4 148.070 28.428 5.209 NA NA
Laws|t1 -134.262 49.717 -2.701 NA NA
Laws|t2 -133.831 35.920 -3.726 NA NA
Laws|t3 -133.113 26.692 -4.987 NA NA
Laws|t4 -131.765 23.392 -5.633 NA NA
Crisis|t1 165.647 27.179 6.095 NA NA
Crisis|t2 166.557 26.585 6.265 NA NA
Crisis|t3 167.091 27.430 6.092 NA NA
Crisis|t4 167.873 31.815 5.277 NA NA
Spaceship|t1 -116.915 36.868 -3.171 NA NA
Spaceship|t2 -116.253 26.553 -4.378 NA NA
Spaceship|t3 -115.710 23.379 -4.949 NA NA
Spaceship|t4 -114.328 22.509 -5.079 NA NA
Rule|t1 117.243 22.770 5.149 NA NA
Rule|t2 117.992 22.281 5.296 NA NA
Rule|t3 118.424 22.901 5.171 NA NA
Rule|t4 119.133 26.202 4.547 NA NA
Balance|t1 -112.160 62.598 -1.792 NA NA
Balance|t2 -110.969 26.827 -4.136 NA NA
Balance|t3 -110.519 23.636 -4.676 NA NA
Balance|t4 -109.109 21.834 -4.997 NA NA
Learn|t1 97.905 25.311 3.868 NA NA
Learn|t2 98.689 21.204 4.654 NA NA
Learn|t3 99.476 20.556 4.839 NA NA
Learn|t4 100.536 24.049 4.181 NA NA
Eco_crsis|t1 -214.119 58.570 -3.656 NA NA
Eco_crsis|t2 -213.406 38.578 -5.532 NA NA
Eco_crsis|t3 -212.793 33.414 -6.368 NA NA
Eco_crsis|t4 -211.629 31.236 -6.775 NA NA
Frq_f_vst_yO|1 -1.646 23.978 -0.069 NA NA
Frq_f_vst_yO|2 -1.417 20.863 -0.068 NA NA
Frq_f_vst_yO|3 -1.213 18.785 -0.065 NA NA
Frq_f_vst_yO|4 -0.873 16.361 -0.053 NA NA
Frq_f_vst_yO|5 -0.367 14.563 -0.025 NA NA
Frq_f_vst_yO|6 0.476 14.810 0.032 NA NA
Frq_f_vst_yO|7 1.315 19.744 0.067 NA NA
Act_ftr_sch_|1 -0.350 14.532 -0.024 NA NA
NtrApprctn_Y|1 1.152 17.892 0.064 NA NA
Chldhd_mmry|t1 -1.271 19.286 -0.066 NA NA
Chldhd_mmry|t2 -0.225 14.343 -0.016 NA NA
Chldhd_mmry|t3 0.664 15.410 0.043 NA NA
Chldhd_mmry|t4 1.686 24.638 0.068 NA NA
NtrApprctn_N|1 -54.388 22.170 -2.453 NA NA
Frq_f_vst_nO|1 -23.758 21.431 -1.109 NA NA
Frq_f_vst_nO|2 -23.557 19.857 -1.186 NA NA
Frq_f_vst_nO|3 -23.278 18.292 -1.273 NA NA
Frq_f_vst_nO|4 -22.770 16.845 -1.352 NA NA
Frq_f_vst_nO|5 -21.994 17.097 -1.286 NA NA
Frq_f_vst_nO|6 -21.977 17.133 -1.283 NA NA
Frq_f_vst_nO|7 -21.237 20.666 -1.028 NA NA
Frq_f_vst_nO|8 -21.226 20.752 -1.023 NA NA
Frq_f_vst_nO|9 -20.540 30.383 -0.676 NA NA
Variances:
Estimate Std.Err t-value df P(>|t|)
.Male 1.188
.Age 128.455 525.868 0.244 NA NA
.Single 1.000
.HHincomprprsnK 3.035 30.456 0.100 NA NA
.Housing_type 1.000
.Extrvrtd_Enths 0.684
.Criticl_qrrlsm 0.984
.Dpndbl_Slfdscp 0.743
.Anxious_slypst 0.892
.Opentnwx_cmplx 0.843
.Reserved_quiet 0.855
.Sympathetc_wrm 0.854
.Disrgnsd_crlss 0.877
.Clm_mtnllystbl 0.831
.Convntnl_ncrtv 0.983
.Limit 0.740
.Modify 0.877
.Interfere 0.794
.Ingenuity 0.876
.Abuse 0.481
.NatRes 0.997
.Rights 0.786
.Industrial 0.708
.Laws 0.764
.Crisis 0.625
.Spaceship 0.821
.Rule 0.812
.Balance 0.837
.Learn 0.867
.Eco_crsis 0.392
.Frq_f_vst_yngO 0.785
.Act_ftr_schl_t 0.681
.NtrApprctn_Yng 0.978
.Childhood_mmry 0.762
.NatrApprctn_Nw 1.005
.Frq_f_vst_nwOr 1.002
.Instrumental 0.007 6.717 0.001 NA NA
.Relational 0.021 7.507 0.003 NA NA
.Intrinsic 0.136 6.648 0.020 NA NA
socioecon -0.188 154.491 -0.001 NA NA
.personality 0.014 1.747 0.008 NA NA
.NEP -1.099 5.630 -0.195 NA NA
.pastexposure 0.215 6.448 0.033 NA NA
.currentbehav -0.047 47.790 -0.001 NA NA
Scales y*:
Estimate Std.Err t-value df P(>|t|)
Male 1.000
Single 1.000
Housing_type 1.000
Extrvrtd_Enths 1.000
Criticl_qrrlsm 1.000
Dpndbl_Slfdscp 1.000
Anxious_slypst 1.000
Opentnwx_cmplx 1.000
Reserved_quiet 1.000
Sympathetc_wrm 1.000
Disrgnsd_crlss 1.000
Clm_mtnllystbl 1.000
Convntnl_ncrtv 1.000
Limit 1.000
Modify 1.000
Interfere 1.000
Ingenuity 1.000
Abuse 1.000
NatRes 1.000
Rights 1.000
Industrial 1.000
Laws 1.000
Crisis 1.000
Spaceship 1.000
Rule 1.000
Balance 1.000
Learn 1.000
Eco_crsis 1.000
Frq_f_vst_yngO 1.000
Act_ftr_schl_t 1.000
NtrApprctn_Yng 1.000
Childhood_mmry 1.000
NatrApprctn_Nw 1.000
Frq_f_vst_nwOr 1.000
...
I have changed the estimator to "WLSMV" but it seems that there are more warnings now.
I also took out a few variables from socio-demo factors because they are not so important and changed freq_of_visit to a dummy coded variableVBNlessSEmodel <-'#measurement modelsocioecon =~ Male + Age + Single + HHincomeperpersonK + Housing_type