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    <title>topic Re: Structural Equation Modelling in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/619132#M29804</link>
    <description>Does this usage note from SAS help?&lt;BR /&gt;&lt;BR /&gt;&lt;A href="http://support.sas.com/kb/22/529.html" target="_blank"&gt;http://support.sas.com/kb/22/529.html&lt;/A&gt;</description>
    <pubDate>Wed, 22 Jan 2020 12:59:54 GMT</pubDate>
    <dc:creator>plf515</dc:creator>
    <dc:date>2020-01-22T12:59:54Z</dc:date>
    <item>
      <title>Structural Equation Modelling</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/618699#M29784</link>
      <description>&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;I am working to fit an SEM model in which the dependent variable is binary. I am looking for literature on PROC CALIS for fitting SEM models where the dependent variable is binary. I look forward to any advise.&lt;/P&gt;</description>
      <pubDate>Mon, 20 Jan 2020 20:56:48 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/618699#M29784</guid>
      <dc:creator>Naviava1973</dc:creator>
      <dc:date>2020-01-20T20:56:48Z</dc:date>
    </item>
    <item>
      <title>Re: Structural Equation Modelling</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/619132#M29804</link>
      <description>Does this usage note from SAS help?&lt;BR /&gt;&lt;BR /&gt;&lt;A href="http://support.sas.com/kb/22/529.html" target="_blank"&gt;http://support.sas.com/kb/22/529.html&lt;/A&gt;</description>
      <pubDate>Wed, 22 Jan 2020 12:59:54 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/619132#M29804</guid>
      <dc:creator>plf515</dc:creator>
      <dc:date>2020-01-22T12:59:54Z</dc:date>
    </item>
    <item>
      <title>Re: Structural Equation Modelling</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/625268#M30093</link>
      <description>Hi All,&lt;BR /&gt;I am building an SEM model using PROC CALIS PATH analysis.&lt;BR /&gt;My dependent variable is HIV Status i.e either positive or negative.&lt;BR /&gt;Some of my other variables are categorical/binary e.g condom use (0/1), pregnant_ever (0/1)&lt;BR /&gt;In the model I put together, it seems to run but with the following error that suggests a poor fit "WARNING: Standard errors and t values might not be accurate with the use of the Moore-Penrose inverse."&lt;BR /&gt;I was wondering if someone can advise me on the best way to fix this error...or point me to some relevant literature fitting SEM PATH with a categorical dependent variable.&lt;BR /&gt;&lt;BR /&gt;26 ods results on;&lt;BR /&gt;27 ods graphics on;&lt;BR /&gt;28 proc calis data=new_sib method=wls plots=pathdiagram;&lt;BR /&gt;29 path&lt;BR /&gt;36&lt;BR /&gt;37 IPV &amp;lt;--- violence,&lt;BR /&gt;38 client &amp;lt;--- violence,&lt;BR /&gt;39 police &amp;lt;--- violence,&lt;BR /&gt;40 childhood &amp;lt;--- violence,&lt;BR /&gt;41&lt;BR /&gt;42 ptsd &amp;lt;--- mental,&lt;BR /&gt;43 depression &amp;lt;--- mental,&lt;BR /&gt;44&lt;BR /&gt;49 binge_drinking &amp;lt;--- mental,&lt;BR /&gt;50 hiv &amp;lt;--- binge_drinking,&lt;BR /&gt;51 hiv &amp;lt;--- age_firstsw,&lt;BR /&gt;52 hiv &amp;lt;--- education,&lt;BR /&gt;53 hiv &amp;lt;--- born,&lt;BR /&gt;54 hiv &amp;lt;--- condom_use,&lt;BR /&gt;56 hiv &amp;lt;--- pregnant_ever;&lt;BR /&gt;57&lt;BR /&gt;2 The SAS System 17:13 Saturday, February 15, 2020&lt;BR /&gt;65&lt;BR /&gt;66 pathdiagram&lt;BR /&gt;67 diagram=standard;&lt;BR /&gt;68 fitindex on(only) = [chisq df probchi rmsea cn srmsr bentlercfi agfi] noindextype;&lt;BR /&gt;69&lt;BR /&gt;70 run;&lt;BR /&gt;&lt;BR /&gt;WARNING: 74 of 3005 observations in data set WORK.NEW_SIB omitted due to missing values.&lt;BR /&gt;NOTE: Convergence criterion (GCONV=1E-8) satisfied.&lt;BR /&gt;NOTE: The Moore-Penrose inverse is used in computing the covariance matrix for parameter estimates.&lt;BR /&gt;WARNING: Standard errors and t values might not be accurate with the use of the Moore-Penrose inverse.&lt;BR /&gt;NOTE: PROCEDURE CALIS used (Total process time):&lt;BR /&gt;real time 2.01 seconds&lt;BR /&gt;cpu time 1.14 seconds&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;71 ods graphics on;&lt;BR /&gt;72 ods results off;&lt;BR /&gt;&lt;BR /&gt;Regards&lt;BR /&gt;&lt;BR /&gt;Kennedy Otwombe&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;------&lt;BR /&gt;This email has been scanned for spam and malware by The Email Laundry.</description>
      <pubDate>Mon, 17 Feb 2020 14:17:00 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Structural-Equation-Modelling/m-p/625268#M30093</guid>
      <dc:creator>Naviava1973</dc:creator>
      <dc:date>2020-02-17T14:17:00Z</dc:date>
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