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SEM dengan SmatPLS

SEM dengan SmatPLS. Evaluasi Goodness of Fit Model SEM pada SmartPLS. Model SEM (Structural Equation Model) dalam PLS, terdiri dari : Model Pengukuran (Outer Model) Model Struktural (Inner Model). Evaluasi Outer Model. Model Pengukuran dengan indikator refleksif :

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SEM dengan SmatPLS

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  1. SEMdenganSmatPLS

  2. Evaluasi Goodness of Fit Model SEM padaSmartPLS Model SEM (Structural Equation Model) dalam PLS, terdiridari: • Model Pengukuran (Outer Model) • Model Struktural (Inner Model)

  3. Evaluasi Outer Model Model Pengukurandenganindikatorrefleksif: • Tingkat validitassuatuindikatorthdvariabelygdiukurnyadptdiujimenggunakanvaliditaskonsep (construct validity). • Validitaskonsep/variabeldptdievaluasidariconvergent validity dandiscriminant validity. • Reliabelitasuntuk block indicator dievaluasidengancomposite realibility

  4. ValiditasIndikatorReplektif • Convergent validity terpenuhijikaskorygdiperolehdariduainstrumenberbedaygmengukurkonsepygsama, menunjukkankorelasitinggi. • Suatuindikatordisebut valid (construct) jikanilaikorelasiatauouter loading minimal 0.50.

  5. ValiditasIndikatorReplektif • Discriminant validity terpenuhijika, berdasarkanteori, duavariabeldiprediksitidakberkorelasidanskorygdiperoleh dg mengukurnyabenar-benarsecaraempirismembuktikanhaltersebut. • Suatuindikatordisebut valid diskriminanjikacross loading indikatortsblebihbesarkevariabelygdiukurnyadaripadakevariabel lain.

  6. ValiditasIndikatorFormatif • Outer model dg indikatorformatif, validitasnyadievaluasiberdasarkan substantive content-nya, yaitu dg membandingkanbesarnya relative weight danberdasarkansignifikansidariukuran weight tersebut.

  7. Reliabelitas Model Pengukuran • Dapatdiukur dg composite reliabelitydannilai AVE (Average Variance Extracted). • Composite reliabelitysekumpulanindikatorygmengukursuatuvariabeldapatdievaluasi dg konsistensi internal dan alpha Croncbach. • AVE adalahsuatuukuranreliabelitaskompo-nenyglebihkonservatifdarireliabelitaskomposit. • Model pengukurandisebutreliabeljikanilai composite reliabelityatau AVE minimal 0.50.

  8. Evaluasi Model Struktural • Nilai R-square, yaitupersentasevariansvariabel latent dependent ygdapatdijelaskanolehseluruhvariabel latent independent-nya, menggunakanukuran Stone-Geisser Q-square. • Besarnyakoefisienjalurstruktural (minimal 0.30 dandipenuhisyaratsignifikansinyadenganuji student t).

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