title: "Could not compute standard errors" and "The variance-covariance matrix of the estimated parameters does not appear to be positive definite!"
body: <p>I've been stuck on this assignment for days. Can someone please help me understand what I'm doing wrong? I've included a picture of the path diagram and labels for each step to make my thought process clear. I'm new to SEM and the model would be <a href="https://i.stack.imgur.com/Xd3Gh.png" rel="nofollow noreferrer">the presented here</a>.</p>
  <p>First, I conducted the multiple group assessment:</p>
  <pre><code>mod.mg1a = &quot;
  
  #Variances
  
  crit ~~ crit
  warm ~~ warm
  
  trust ~~ trust
  
  never ~~ never
  moral ~~ moral
  han ~~ han
  good ~~ good
  
  #Regression/Causal Path
  
  trust ~ warm
  trust ~ crit
  
  #Factor Loadings
  
  trust =~ never + moral + han + good
  
  &quot;
  fit.mg1a = lavaan(mod.mg1a,data=mach,group=&quot;gen&quot;)
  summary(fit.mg1a)
  
  mod.mg1b = &quot;
  #Variances
  
  crit ~~ crit
  warm ~~ warm
  
  trust ~~ trust
  
  never ~~ never
  moral ~~ moral
  han ~~ han
  good ~~ good
  
  #Regression/Causal Path
  
  trust ~ warm
  trust ~ crit
  
  #Factor Loadings
  
  trust =~ never + moral + han + good
  
  &quot;
  fit.mg1b = lavaan(mod.mg1b,data=mach,group=&quot;gen&quot;)
  summary(fit.mg1b)
  
  anova(fit.mg1a, fit.mg1b)
  </code></pre>
  <p>This return the following warnings:</p>
  <blockquote>
  <pre><code>***Warning in lav_partable_check(lavpartable, categorical = lavoptions$categorical,  :
    lavaan WARNING: automatically added intercepts are set to zero:
      [warm crit warm crit]
  Warning 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.***
  
  ----------
  
  ***Warning in lavTestLRT(object = object, ..., model.names = NAMES) :
    lavaan WARNING: some models have the same degrees of freedom***
  </code></pre>
  </blockquote>
  <p>The following assess the comparison with the model with all parameters constrained to be equal:</p>
  <pre><code>    fit.con = lavaan(mod.mg1a,data=mach, group=&quot;gen&quot;, group.equal=c(
        &quot;loadings&quot;,&quot;lv.covariances&quot;,&quot;intercepts&quot;,&quot;means&quot;,&quot;residuals&quot;,
        &quot;lv.variances&quot;,&quot;regressions&quot;))
      
  </code></pre>
  <p>which returns:</p>
  <blockquote>
  <pre><code>***Warning in lav_partable_check(lavpartable, categorical = lavoptions$categorical,  :
    lavaan WARNING: automatically added intercepts are set to zero:
      [warm crit warm crit]
  Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
    lavaan WARNING:
      The variance-covariance matrix of the estimated parameters (vcov)
      does not appear to be positive definite! The smallest eigenvalue
      (= 7.438519e-15) is close to zero. This may be a symptom that the
      model is not identified.***
  </code></pre>
  </blockquote>
  <p>Grateful for any guidance.</p>