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?:abstract
  • "The presented Monte-Carlo simulation studies prove the advantage of the robust RMSEA, CFI and TLI using medium sized and great samples(n >= 200 / 300). My robust_gof.ado computes the robust fit indices using the individual data set, the Satorra-Bentler-rescaled Likelihood-Ratio-chi2 test statistics (TSB) and scaling factors cM and cB. For small sample sizes I recommend the Swain-correction of TML and my swain_gof.adopresented at the German Stata Users Group Meeting last year in Konstanz." Die ALLBUS-Daten aus dem Jahr 2016 dienen als Hauptdatensatz für die Analyse. (xsd:string)
?:author
?:comment
  • https://www.stata.com/meeting/germany19/slides/germany19_Langer.pdf. (ALLBUS) (xsd:string)
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  • ALLBUS-Bibliography (xsd:string)
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  • Aufgenommen: 34. Fassung, Oktober 2019 (xsd:gyear)
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  • 2019 (xsd:gyear)
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  • 2019 (xsd:gyear)
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  • 42 (xsd:string)
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  • english (xsd:string)
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?:name
  • How to use Stata's sem command with nonnormal data? A new nonnormality correction for the RMSEA, CFI and TLI (xsd:string)
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  • techreport (xsd:string)
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  • 42, 2019 (xsd:string)
  • Bibsonomy (xsd:string)
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  • ALLBUS (xsd:string)
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  • 2019 (xsd:string)
  • ALLBUS (xsd:string)
  • ALLBUS2016 (xsd:string)
  • ALLBUS_input2019 (xsd:string)
  • ALLBUS_pro (xsd:string)
  • ALLBUS_version34 (xsd:string)
  • FDZ_ALLBUS (xsd:string)
  • GA (xsd:string)
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  • techreport (xsd:string)
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  • 42 (xsd:string)
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?:uploadDate
  • 15.11.2019 (xsd:gyear)
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