SAM - Model-based reliability latent variables = 1?
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Elisa Ugarte
May 23, 2024, 1:29:23 PM5/23/24
to lavaan
Hello,
I've recently tried to apply this function to my data and I'm not sure I am understanding the function or output correctly. I went through the 2023 paper and the forum, but I have yet to understand what is wrong with my model since I do not trust the reliability to be 1. I first ran measurement models (CFA) using std.lv = T, auto.fix.first = F. Indicators are ordinal in the data set. Models had good fit. Then, I ran SAM to include a regression.
If anyone can pinpoint what I'm doing wrong, would appreciate some answers!
Thanks,
Elisa
Here's my code:
model_jptsd <- "
## Measurement
f1H_migration =~ htq1_05 + htq1_06 + htq1_17
f2H_isolation =~ htq1_19 + htq1_21 + htq1_30_34
f3H_victimization =~ htq1_7_10 + htq1_8_9 + htq1_11 + htq1_13_18 + htq1_14 + htq1_16 + htq1_20_24 + htq1_22 + htq1_25_to_29 + htq1_23
f4H_violencemig =~ htq1_04 + htq1_12_36 + htq1_15 + htq1_32 + htq1_35
f5H_witness =~ htq1_39 + htq1_37 + htq1_38
htq1_04 ~~ htq1_15
f2A_abuse =~ ace_4_6 + ace_4_7 + ace_4_8 + ace_5_1 + ace_5_2 + ace_5_3 + ace_5_4 + ace_6_1 + ace_6_3
f3A_sexual =~ ace_2_1R + ace_2_2R + ace_3_1 + ace_3_2 + ace_4_1 + ace_5_5 + ace_5_6 + ace_5_7 + ace_5_8
f4A_comviolence =~ ace_7_1 + ace_7_2 + ace_7_3
ace_3_2 ~~ ace_4_1
ace_2_1R ~~ ace_2_2R
ace_4_6 ~~ ace_4_7
ace_4_7 ~~ ace_4_8
### Structural
htq_ptsd_total ~ f1H_migration + f2H_isolation + f3H_victimization + f4H_violencemig + f5H_witness + f2A_abuse + f3A_sexual + f4A_comviolence + q102b_guess_age
"
fit <- sam(model_jptsd, data = data, cmd = "sem", mm.list = list("f1H_migration", "f2H_isolation" , "f3H_victimization", "f4H_violencemig", "f5H_witness", "f2A_abuse", "f3A_sexual", "f4A_comviolence"), mm.args = list(std.lv = T, auto.fix.first = F))
f2H_isolation =~ htq1_19 + htq1_21 + htq1_30_34
f3H_victimization =~ htq1_7_10 + htq1_8_9 + htq1_11 + htq1_13_18 + htq1_14 + htq1_16 + htq1_20_24 + htq1_22 + htq1_25_to_29 + htq1_23
f4H_violencemig =~ htq1_04 + htq1_12_36 + htq1_15 + htq1_32 + htq1_35
f5H_witness =~ htq1_39 + htq1_37 + htq1_38
htq1_04 ~~ htq1_15
f2A_abuse =~ ace_4_6 + ace_4_7 + ace_4_8 + ace_5_1 + ace_5_2 + ace_5_3 + ace_5_4 + ace_6_1 + ace_6_3
f3A_sexual =~ ace_2_1R + ace_2_2R + ace_3_1 + ace_3_2 + ace_4_1 + ace_5_5 + ace_5_6 + ace_5_7 + ace_5_8
f4A_comviolence =~ ace_7_1 + ace_7_2 + ace_7_3
ace_3_2 ~~ ace_4_1
ace_2_1R ~~ ace_2_2R
ace_4_6 ~~ ace_4_7
ace_4_7 ~~ ace_4_8
### Structural
htq_ptsd_total ~ f1H_migration + f2H_isolation + f3H_victimization + f4H_violencemig + f5H_witness + f2A_abuse + f3A_sexual + f4A_comviolence + q102b_guess_age
"
fit <- sam(model_jptsd, data = data, cmd = "sem", mm.list = list("f1H_migration", "f2H_isolation" , "f3H_victimization", "f4H_violencemig", "f5H_witness", "f2A_abuse", "f3A_sexual", "f4A_comviolence"), mm.args = list(std.lv = T, auto.fix.first = F))
Results
SAM method LOCAL
Mapping matrix M method ML
Number of measurement blocks 8
Estimator measurement part DWLS
Estimator structural part ML
Used Total
Number of observations 2720 2965
Summary Information Measurement + Structural:
Block Latent Nind Chisq Df
1 f1H_migration 3 113.385 0
2 f2H_isolation 3 0.406 0
3 f3H_victimization 10 250.329 35
4 f4H_violencemig 5 198.698 4
5 f5H_witness 3 169.985 0
6 f2A_abuse 9 1041.368 25
7 f3A_sexual 9 732.371 25
8 f4A_comviolence 3 234.394 0
Model-based reliability latent variables:
f1H_migration f2H_isolation f3H_victimization f4H_violencemig f5H_witness f2A_abuse f3A_sexual
1 1 1 1 1 1 1
f4A_comviolence htq_ptsd_total q102b_guess_age
1 1 1
Summary Information Structural part:
chisq df cfi rmsea srmr
0 0 1 0 0
Parameter Estimates:
Parameterization Delta
Standard errors Twostep
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
htq_ptsd_total ~
f1H_migration 0.009 0.015 0.631 0.528 0.009 0.017
f2H_isolation 0.062 0.018 3.510 0.000 0.062 0.117
f3H_victimiztn 0.118 0.011 10.727 0.000 0.118 0.222
f4H_violencemg 0.008 0.011 0.746 0.455 0.008 0.016
f5H_witness 0.052 0.013 3.919 0.000 0.052 0.099
f2A_abuse 0.144 0.008 17.156 0.000 0.143 0.271
f3A_sexual 0.008 0.005 1.401 0.161 0.010 0.020
f4A_comviolenc 0.029 0.012 2.390 0.017 0.029 0.054
q102b_guess_ag 0.008 0.002 4.648 0.000 0.008 0.092
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1H_migration ~~
f2H_isolation 0.199 0.027 7.354 0.000 0.199 0.199
f3H_victimiztn 0.106 0.017 6.321 0.000 0.106 0.106
f4H_violencemg 0.399 0.018 22.220 0.000 0.374 0.374
f5H_witness 0.381 0.017 22.276 0.000 0.381 0.381
f2A_abuse 0.087 0.011 7.890 0.000 0.087 0.087
f3A_sexual -0.056 0.017 -3.242 0.001 -0.040 -0.040
f4A_comviolenc 0.184 0.016 11.430 0.000 0.184 0.184
f2H_isolation ~~
f3H_victimiztn 0.276 0.026 10.742 0.000 0.276 0.276
f4H_violencemg 0.157 0.021 7.362 0.000 0.147 0.147
f5H_witness 0.374 0.031 12.048 0.000 0.374 0.374
f2A_abuse 0.088 0.014 6.148 0.000 0.088 0.088
f3A_sexual 0.089 0.022 4.052 0.000 0.064 0.064
f4A_comviolenc 0.074 0.021 3.599 0.000 0.074 0.074
f3H_victimization ~~
f4H_violencemg 0.141 0.014 9.977 0.000 0.132 0.132
f5H_witness 0.226 0.014 16.053 0.000 0.226 0.226
f2A_abuse 0.144 0.010 14.400 0.000 0.145 0.145
f3A_sexual -0.066 0.014 -4.614 0.000 -0.048 -0.048
f4A_comviolenc -0.008 0.014 -0.527 0.598 -0.008 -0.008
f4H_violencemig ~~
f5H_witness 0.156 0.013 11.795 0.000 0.146 0.146
f2A_abuse -0.054 0.009 -5.645 0.000 -0.050 -0.050
f3A_sexual -0.068 0.016 -4.386 0.000 -0.046 -0.046
f4A_comviolenc -0.077 0.014 -5.349 0.000 -0.072 -0.072
f5H_witness ~~
f2A_abuse 0.124 0.009 13.154 0.000 0.125 0.125
f3A_sexual -0.063 0.015 -4.186 0.000 -0.045 -0.045
f4A_comviolenc 0.150 0.014 10.509 0.000 0.150 0.150
f2A_abuse ~~
f3A_sexual 0.001 0.010 0.136 0.892 0.001 0.001
f4A_comviolenc 0.347 0.012 28.144 0.000 0.349 0.349
f3A_sexual ~~
f4A_comviolenc -0.022 0.015 -1.401 0.161 -0.016 -0.016
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.htq_ptsd_total 1.886 0.011 173.537 0.000 1.886 3.564
q102b_guess_ag 0.000 0.136 0.000 1.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.htq_ptsd_total 0.213 0.010 21.645 0.000 0.213 0.761
f1H_migration 1.000 0.049 20.222 0.000 1.000 1.000
f2H_isolation 1.000 0.159 6.293 0.000 1.000 1.000
f3H_victimiztn 1.000 0.060 16.553 0.000 1.000 1.000
f4H_violencemg 1.136 0.080 14.207 0.000 1.000 1.000
f5H_witness 1.000 0.041 24.510 0.000 1.000 1.000
f2A_abuse 0.992 0.031 32.262 0.000 1.000 1.000
f3A_sexual 1.924 0.216 8.919 0.000 1.000 1.000
f4A_comviolenc 1.000 0.043 23.015 0.000 1.000 1.000
q102b_guess_ag 34.455 0.827 41.655 0.000 34.455 1.000
Mapping matrix M method ML
Number of measurement blocks 8
Estimator measurement part DWLS
Estimator structural part ML
Used Total
Number of observations 2720 2965
Summary Information Measurement + Structural:
Block Latent Nind Chisq Df
1 f1H_migration 3 113.385 0
2 f2H_isolation 3 0.406 0
3 f3H_victimization 10 250.329 35
4 f4H_violencemig 5 198.698 4
5 f5H_witness 3 169.985 0
6 f2A_abuse 9 1041.368 25
7 f3A_sexual 9 732.371 25
8 f4A_comviolence 3 234.394 0
Model-based reliability latent variables:
f1H_migration f2H_isolation f3H_victimization f4H_violencemig f5H_witness f2A_abuse f3A_sexual
1 1 1 1 1 1 1
f4A_comviolence htq_ptsd_total q102b_guess_age
1 1 1
Summary Information Structural part:
chisq df cfi rmsea srmr
0 0 1 0 0
Parameter Estimates:
Parameterization Delta
Standard errors Twostep
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
htq_ptsd_total ~
f1H_migration 0.009 0.015 0.631 0.528 0.009 0.017
f2H_isolation 0.062 0.018 3.510 0.000 0.062 0.117
f3H_victimiztn 0.118 0.011 10.727 0.000 0.118 0.222
f4H_violencemg 0.008 0.011 0.746 0.455 0.008 0.016
f5H_witness 0.052 0.013 3.919 0.000 0.052 0.099
f2A_abuse 0.144 0.008 17.156 0.000 0.143 0.271
f3A_sexual 0.008 0.005 1.401 0.161 0.010 0.020
f4A_comviolenc 0.029 0.012 2.390 0.017 0.029 0.054
q102b_guess_ag 0.008 0.002 4.648 0.000 0.008 0.092
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1H_migration ~~
f2H_isolation 0.199 0.027 7.354 0.000 0.199 0.199
f3H_victimiztn 0.106 0.017 6.321 0.000 0.106 0.106
f4H_violencemg 0.399 0.018 22.220 0.000 0.374 0.374
f5H_witness 0.381 0.017 22.276 0.000 0.381 0.381
f2A_abuse 0.087 0.011 7.890 0.000 0.087 0.087
f3A_sexual -0.056 0.017 -3.242 0.001 -0.040 -0.040
f4A_comviolenc 0.184 0.016 11.430 0.000 0.184 0.184
f2H_isolation ~~
f3H_victimiztn 0.276 0.026 10.742 0.000 0.276 0.276
f4H_violencemg 0.157 0.021 7.362 0.000 0.147 0.147
f5H_witness 0.374 0.031 12.048 0.000 0.374 0.374
f2A_abuse 0.088 0.014 6.148 0.000 0.088 0.088
f3A_sexual 0.089 0.022 4.052 0.000 0.064 0.064
f4A_comviolenc 0.074 0.021 3.599 0.000 0.074 0.074
f3H_victimization ~~
f4H_violencemg 0.141 0.014 9.977 0.000 0.132 0.132
f5H_witness 0.226 0.014 16.053 0.000 0.226 0.226
f2A_abuse 0.144 0.010 14.400 0.000 0.145 0.145
f3A_sexual -0.066 0.014 -4.614 0.000 -0.048 -0.048
f4A_comviolenc -0.008 0.014 -0.527 0.598 -0.008 -0.008
f4H_violencemig ~~
f5H_witness 0.156 0.013 11.795 0.000 0.146 0.146
f2A_abuse -0.054 0.009 -5.645 0.000 -0.050 -0.050
f3A_sexual -0.068 0.016 -4.386 0.000 -0.046 -0.046
f4A_comviolenc -0.077 0.014 -5.349 0.000 -0.072 -0.072
f5H_witness ~~
f2A_abuse 0.124 0.009 13.154 0.000 0.125 0.125
f3A_sexual -0.063 0.015 -4.186 0.000 -0.045 -0.045
f4A_comviolenc 0.150 0.014 10.509 0.000 0.150 0.150
f2A_abuse ~~
f3A_sexual 0.001 0.010 0.136 0.892 0.001 0.001
f4A_comviolenc 0.347 0.012 28.144 0.000 0.349 0.349
f3A_sexual ~~
f4A_comviolenc -0.022 0.015 -1.401 0.161 -0.016 -0.016
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.htq_ptsd_total 1.886 0.011 173.537 0.000 1.886 3.564
q102b_guess_ag 0.000 0.136 0.000 1.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.htq_ptsd_total 0.213 0.010 21.645 0.000 0.213 0.761
f1H_migration 1.000 0.049 20.222 0.000 1.000 1.000
f2H_isolation 1.000 0.159 6.293 0.000 1.000 1.000
f3H_victimiztn 1.000 0.060 16.553 0.000 1.000 1.000
f4H_violencemg 1.136 0.080 14.207 0.000 1.000 1.000
f5H_witness 1.000 0.041 24.510 0.000 1.000 1.000
f2A_abuse 0.992 0.031 32.262 0.000 1.000 1.000
f3A_sexual 1.924 0.216 8.919 0.000 1.000 1.000
f4A_comviolenc 1.000 0.043 23.015 0.000 1.000 1.000
q102b_guess_ag 34.455 0.827 41.655 0.000 34.455 1.000
Yves Rosseel
Jun 2, 2024, 9:56:41 AM6/2/24
to lav...@googlegroups.com
Hi,
sam() does not work well (yet) if you use std.lv = TRUE as a local
argument within mm.args(), while the 'global' model is using std.lv = FALSE.
So remove the mm.args=list() argument altogether, and directly use
std.lv = TRUE instead.
Still, at least a warning should be given. I opened an issue about this
on github so I don't forget.
Yves.
On 5/23/24 7:29 PM, 'Elisa Ugarte' via lavaan wrote:
> Hello,
> I've recently tried to apply this function to my data and I'm not sure I
> am understanding the function or output correctly. I went through the
> 2023 paper and the forum, but I have yet to understand what is wrong
> with my model since I do not trust the reliability to be 1. I first ran
> measurement models (CFA) using std.lv = T, auto.fix.first = F.
> Indicators are ordinal in the data set. Models had good fit. Then, I ran
> SAM to include a regression.
> If anyone can pinpoint what I'm doing wrong, would appreciate some answers!
> Thanks,
> Elisa
>
>
> *Here's my code:*
> *Results*
> --
> You received this message because you are subscribed to the Google
> Groups "lavaan" group.
> To unsubscribe from this group and stop receiving emails from it, send
> an email to lavaan+un...@googlegroups.com
> <mailto:lavaan+un...@googlegroups.com>.
> To view this discussion on the web visit
> https://groups.google.com/d/msgid/lavaan/ee650b00-ab84-4f22-aad3-54435dfa1501n%40googlegroups.com <https://groups.google.com/d/msgid/lavaan/ee650b00-ab84-4f22-aad3-54435dfa1501n%40googlegroups.com?utm_medium=email&utm_source=footer>.
--
Yves Rosseel
Department of Data Analysis, Ghent University
Please consider donating to the lavaan project:
https://lavaan.org/about/donate.html
sam() does not work well (yet) if you use std.lv = TRUE as a local
argument within mm.args(), while the 'global' model is using std.lv = FALSE.
So remove the mm.args=list() argument altogether, and directly use
std.lv = TRUE instead.
Still, at least a warning should be given. I opened an issue about this
on github so I don't forget.
Yves.
On 5/23/24 7:29 PM, 'Elisa Ugarte' via lavaan wrote:
> Hello,
> I've recently tried to apply this function to my data and I'm not sure I
> am understanding the function or output correctly. I went through the
> 2023 paper and the forum, but I have yet to understand what is wrong
> with my model since I do not trust the reliability to be 1. I first ran
> measurement models (CFA) using std.lv = T, auto.fix.first = F.
> Indicators are ordinal in the data set. Models had good fit. Then, I ran
> SAM to include a regression.
> If anyone can pinpoint what I'm doing wrong, would appreciate some answers!
> Thanks,
> Elisa
>
>
> You received this message because you are subscribed to the Google
> Groups "lavaan" group.
> To unsubscribe from this group and stop receiving emails from it, send
> an email to lavaan+un...@googlegroups.com
> <mailto:lavaan+un...@googlegroups.com>.
> To view this discussion on the web visit
> https://groups.google.com/d/msgid/lavaan/ee650b00-ab84-4f22-aad3-54435dfa1501n%40googlegroups.com <https://groups.google.com/d/msgid/lavaan/ee650b00-ab84-4f22-aad3-54435dfa1501n%40googlegroups.com?utm_medium=email&utm_source=footer>.
--
Yves Rosseel
Department of Data Analysis, Ghent University
Please consider donating to the lavaan project:
https://lavaan.org/about/donate.html
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