Contoh Analisis Melalui AMOS – Ketika Mediator Moderator dalam Satu Model

  Ū凐 凐 §Ş ⓰ ࿠꼸࿠꼸§滴ᷤ ⓰ 깄࿠§Ş滴 谰§ §憌ᷤ ࿠峄⓰§滴ᷤ ࿠⓰ 凐 §Ë§ 滴凐 ᷤ ⓰ 凐 § ⓰ ⓰횰§谰⓰ 깄§滴凐 ᷤ §§

  § 宼⓰ ꑐ깄§宼࿠ ࿠⓰ 꼸凐§

  ⓰峄깄 ⓰꼸§ 꼸࿠峄凐 凐ၬ࿠§⑔䕴滴§ §nĀĆƧ

  

  § § 깄 ࿠꼸⓰ §࿠ ࿠§횰ᷤ횰 ⓰ ⓰꼸§횰⓰꼸⓰ ⓰ § 凐꼸ᷤ 깄 §⓰ ⓰ ࿠꼸࿠꼸§峄ᷤ ࿠峄⓰§꼸ᷤ 깄⓰ §횰凐 ᷤ §횰ᷤ횰࿠ ࿠峄࿠§ 깄⓰§ ᷤ ࿠꼸§ ⓰ ࿠⓰ ᷤ î§ꑐ⓰࿠ 깄§ ⓰ ࿠⓰ ᷤ §횰ᷤ ࿠⓰ 凐 § ⓰ § ⓰ ࿠⓰ ᷤ §횰凐 ᷤ ⓰ 凐 ö§憌⓰ ࿠§࿠ ࿠§ꑐ⓰ ၬ§峄࿠ ⓰§ ⓰ ⓰꼸§⓰ ⓰ ⓰ §횰凐 ᷤ §⓰ ⓰ ࿠꼸࿠꼸§ ⓰ 깄 § ⓰ ⓰§횰ᷤ ၬၬ깄 ⓰峄⓰ § ⓰ ࿠⓰ ᷤ § ⓰ ᷤ ö§ ⓰ ࿠§꼸ᷤ횰깄⓰§ 峄凐 꼸 깄峄§ꑐ⓰ ၬ§ ࿠ ࿠ ⓰ 峄⓰ §⓰ ⓰ ⓰ §峄凐 꼸 깄峄§ᷤ횰 ࿠ ࿠峄§ꑐ⓰ ၬ§ ࿠ ⓰ ⓰ 峄⓰ § ⓰ ࿠§꼸峄凐 § 凐 ⓰ § ⓰ ⓰깄§ ᷤ ⓰ ⓰§⓰ ⓰ §깄峄깄 ö§ §

  Şö§§§Űᷤ꼸⓰࿠ § ᷤ ᷤ ࿠ ࿠⓰ §

  ᷤ ࿠ ࿠⓰ §࿠ ࿠§ ᷤ 깄 깄⓰ §깄 깄峄§횰ᷤ ၬ깄 ࿠§ ᷤ ⓰ ⓰ §峄깄⓰ ࿠ ⓰꼸§ ᷤ ⓰ꑐ⓰ ⓰ §× ݧ ᷤ ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸§ ᷤ ⓰ ၬၬ⓰ §×㲠Ýö§ 깄 깄 ၬ⓰ §峄ᷤ 깄⓰ ꑐ⓰§ ࿠횰ᷤ ࿠⓰ 凐 ࿠§凐 ᷤ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ § ×滴凐 Ýö§Ş ࿠ ꑐ⓰§ ᷤ ⓰ꑐ⓰ ⓰ §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ § ᷤ ᷤ ࿠ § ⓰ 깄 깄§ ⓰ 깄§ 峄ᷤ횰깄 ࿠⓰ §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ § 凐ꑐ⓰ ࿠ ⓰꼸 ꑐ⓰ö§§ § Ű࿠§꼸࿠꼸࿠§ ⓰࿠ î§ᷤ횰 ⓰ ࿠§ 깄ၬ⓰§ ᷤ ᷤ ⓰ § ⓰ ⓰횰§ 깄 깄 ၬ⓰ §峄ᷤ ࿠ၬ⓰§ ⓰ ࿠⓰ ᷤ § ᷤ 꼸ᷤ 깄 ö§Ŷ횰 ⓰ ࿠§ 횰ᷤ ᷤ 깄峄⓰ §⓰ ⓰峄⓰ § ᷤ ⓰ꑐ⓰ ⓰ §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §峄ᷤ 깄⓰꼸⓰ §⓰ ⓰깄峄⓰ § ࿠ ⓰峄ö§Ş꼸깄횰꼸࿠ ꑐ⓰§ ⓰ ꑐ⓰§ ᷤ ၬ⓰ §ᷤ횰 ⓰ ࿠î§ ᷤ ⓰ꑐ⓰ ⓰ §ꑐ⓰ ၬ§ ᷤ 峄깄⓰ ࿠ ⓰꼸§⓰峄⓰ §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ ö§Ŷ횰 ⓰ ࿠§ 깄ၬ⓰§횰ᷤ ⓰ ࿠§횰凐 ᷤ ⓰ 凐 § ᷤ ⓰ ⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸ö§ 憌凐 꼸깄횰ᷤ § 깄⓰꼸î§ ⓰ ࿠§峄⓰ ⓰깄§ ࿠ ⓰峄§ ࿠ ⓰ ᷤ ၬ࿠§ ᷤ ၬ⓰ §ᷤ횰 ⓰ ࿠§ ᷤ ⓰ ⓰ § ᷤ ⓰ ၬၬ⓰ § 횰ᷤ ᷤ峄⓰§ ࿠ ⓰峄§⓰峄⓰ § 凐ꑐ⓰ ö§ ⓰ ၬ§ ⓰꼸 ࿠§ ࿠ ⓰峄§⓰峄⓰ §峄ᷤ횰 ⓰ ࿠§峄ᷤ§ 凐峄凐§峄࿠ ⓰§峄⓰ ⓰깄§ ᷤ ⓰ၬ⓰§ 凐峄凐§ ࿠ ⓰峄§ ⓰횰⓰ ö§滴ᷤ꼸峄࿠ 깄 § ࿠⓰§ 깄⓰꼸§ ᷤ ၬ⓰ § ⓰ ၬ⓰§ ⓰ § ᷤ ⓰ꑐ⓰ ⓰ î§ ࿠꼸⓰§ ⓰ ࿠§ ࿠⓰§ ᷤ ⓰ ࿠ §峄ᷤ§ 凐峄凐§ ⓰࿠ ꑐ⓰ö§ § ⓰ ࿠î§꼸ᷤ ᷤ ၬ峄⓰ ꑐ⓰§ ⓰ ࿠⓰ ᷤ §峄࿠ ⓰§⓰ ⓰ ⓰ §꼸ᷤ ⓰ၬ⓰࿠§ ᷤ ࿠峄깄 § ⓰ ࿠⓰ ᷤ §Űᷤ ᷤ ᷤ §§ 姧 凐ꑐ⓰ ࿠ ⓰꼸§ ⓰ ࿠⓰ ᷤ §䳤 ᷤ ᷤ ᷤ § 姧憌깄⓰ ࿠ ⓰꼸§ ᷤ ⓰ꑐ⓰ ⓰ § ⓰ ࿠⓰ ᷤ §滴ᷤ ࿠⓰ 凐 § 姧憌ᷤ 깄⓰꼸⓰ § ⓰ ࿠⓰ ᷤ §滴凐 ᷤ ⓰ 凐 § 姧Ŷ횰 ⓰ ࿠§§ §

  Ťö§§䕴⓰횰 ⓰ §滴凐 ᷤ §

  憌凐 꼸ᷤ §࿠ ࿠§ ࿠ ᷤ ᷤ峄꼸࿠峄⓰ § ⓰ ⓰§ၬ⓰횰 ⓰ § ࿠§⓰ ⓰꼸ö§ ⓰ ⓰ § ⓰ ࿠§ᷤ횰 ⓰ ࿠§峄ᷤ§⓰ ⓰ § ⓰ ⓰ § 깄 깄 ၬ⓰ § ᷤ ⓰ꑐ⓰ ⓰ ð峄ᷤ 깄⓰꼸⓰ §꼸ᷤ ⓰§峄ᷤ 깄⓰꼸⓰ ð 凐ꑐ⓰ ࿠ ⓰꼸§횰ᷤ 깄 깄峄峄⓰ § ⓰ ꨤ⓰§ᷤ횰 ⓰ ࿠§ 횰ᷤ ⓰ ࿠§횰凐 ᷤ ⓰ 凐 § 깄 깄 ၬ⓰ § ᷤ 꼸ᷤ 깄 ö§ §

  Ŷ횰 ⓰ ࿠§ 憌ᷤ 깄⓰꼸⓰ §

  ᷤ ⓰ꑐ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸§ §

  § ⓰ၬᷤ§ §Ć§§

  Ūö§§滴ᷤ ၬ⓰ ࿠峄⓰꼸࿠峄⓰ §䕴⓰횰 ⓰ § ࿠§Ş滴 谰§

  ⓰ ࿠⓰ ᷤ §횰凐 ᷤ ⓰ 凐 §꼸ᷤ䥈⓰ ⓰§꼸 ⓰ ࿠꼸 ࿠峄§ ࿠ꨤ깄 깄 峄⓰ § ⓰ ⓰횰§ ᷤ 깄峄§ ⓰ ࿠⓰ ᷤ §ꑐ⓰ ၬ§ 횰ᷤ 깄 ⓰峄⓰ § ᷤ 峄⓰ ࿠⓰ §⓰ ⓰ ⓰§ ᷤ ࿠峄 凐 § ⓰ §횰凐 ᷤ ⓰ 凐 ꑐ⓰ö§滴࿠꼸⓰ ꑐ⓰§ ᷤ ⓰ ⓰ § ᷤ ⓰ꑐ⓰ ⓰ §ꑐ⓰ ၬ§ ࿠횰凐 ᷤ ⓰ 凐 ࿠§凐 ᷤ §ᷤ횰 ⓰ ࿠î§횰⓰峄⓰§峄࿠ ⓰§횰ᷤ횰 깄⓰ § ⓰ ࿠⓰ ᷤ § ⓰ 깄§ꑐ⓰ ၬ§ 횰ᷤ 깄 ⓰峄⓰ § ᷤ 峄⓰ ࿠⓰ §峄ᷤ 깄⓰§ ⓰ ࿠⓰ ᷤ § ᷤ 꼸ᷤ 깄 §× ᷤ⓰䥈 ᷤ î§ 깄䥈峄ᷤ î§Ë§ ⓰ꑐᷤ꼸î§nĀĀĨÝö§ § Ű⓰ ⓰횰§䥈凐 凐 §࿠ ࿠§ ᷤ 峄⓰ ࿠⓰ §⓰ ⓰ ⓰§ ᷤ ⓰ꑐ⓰ ⓰ § ⓰ §ᷤ횰 ⓰ ࿠§꼸⓰ꑐ⓰§ ⓰횰⓰峄⓰ §Ŷ滴ཀ Ŷ î§ 꼸ᷤ ⓰ ၬ峄⓰ § ᷤ 峄⓰ ࿠⓰ §⓰ ⓰ ⓰§峄ᷤ 깄⓰꼸⓰ § ⓰ §ᷤ횰 ⓰ ࿠§꼸⓰ꑐ⓰§ ⓰횰⓰峄⓰ §Ŷ滴ཀ ⑔Ş谰ö§ ᷤ 깄 ꑐ⓰§ 峄࿠ ⓰§꼸࿠⓰ 峄⓰ § ⓰ ࿠⓰ ᷤ §࿠ ࿠§ ࿠§谰 谰谰§꼸ᷤ ᷤ 깄횰§ ࿠§ ⓰ꨤ⓰§峄ᷤ§Ş滴 谰ö§Ť⓰ၬ⓰࿠횰⓰ ⓰§䥈⓰ ⓰§ 횰ᷤ ၬၬ⓰횰 ⓰ § ⓰ §횰ᷤ횰࿠ ⓰ § ⓰ ࿠⓰ ᷤ § ࿠꼸⓰§ ࿠ ࿠ ⓰ § ࿠§宼࿠ ࿠⓰ 꼸凐§×宼࿠ ࿠⓰ 꼸凐î§nĀĆĆ⓰Ýö§§ § 䕴⓰횰 ⓰ § ⓰꼸࿠ §⓰ ⓰ ࿠꼸࿠꼸§ ࿠§ 凐ၬ ⓰횰§Ş滴 谰§ ⓰ ⓰ § ࿠ ࿠ ⓰ § ⓰ ⓰§ၬ⓰횰 ⓰ § ࿠§ ⓰ꨤ⓰ §࿠ ࿠ö§ §

  § §

  Űö§§滴ᷤ횰 ⓰䥈⓰§ 깄 깄 § Ćö§§Ŷ꼸 ࿠횰⓰꼸࿠§ ⓰ ⓰횰ᷤ ᷤ §

  Ťᷤ ࿠峄깄 §࿠ ࿠§ ⓰꼸࿠ §⓰ ⓰ ࿠꼸࿠꼸§횰ᷤ ⓰ 깄࿠§ 凐ၬ ⓰횰§Ş滴 谰ö§Ş ⓰§ 깄⓰§ ⓰ၬ࿠⓰ §ꑐ⓰ ၬ§峄࿠ ⓰§ ⓰ ⓰꼸§ꑐ⓰࿠ 깄§ ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ ࿠⓰ § ⓰ ⓰횰ᷤ ᷤ § ⓰ § ࿠ ⓰࿠§峄ᷤ ᷤ ⓰ ⓰ §횰凐 ᷤ ö§§ §

   ࿠ 凐 ᷤ꼸࿠꼸§Ćö§ 凐ꑐ⓰ ࿠ ⓰꼸§횰ᷤ ⓰ ࿠§횰凐 ᷤ ⓰ 凐 § ᷤ ⓰ ⓰ § ࿠ ၬ峄⓰ § ᷤ ⓰ꑐ⓰ ⓰ § ᷤ ⓰ ⓰ §

  峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ ö§憌凐 ࿠ 깄꼸࿠§ 凐ꑐ⓰ ࿠ ⓰꼸§ ⓰ ⓰횰§횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ §⓰峄⓰ § ᷤ꼸⓰ § ࿠峄⓰§ ᷤ ⓰ꑐ⓰ ⓰ § ᷤ 꼸ᷤ 깄 § ࿠ ⓰ ᷤ ၬ࿠§ ᷤ ၬ⓰ §ᷤ횰 ⓰ ࿠ö§

  § ⓰꼸࿠ §⓰ ⓰ ࿠꼸࿠꼸§횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ꑐ⓰ ၬ§꼸࿠ၬ ࿠ ࿠峄⓰ §× ©ðĀönĖ›§ ŃĀöĀĜÝö§

  Űᷤ ၬ⓰ § ᷤ횰࿠峄࿠⓰ § ࿠ 凐 ᷤ꼸࿠꼸§Ć§ ᷤ 깄峄 ࿠ö§

  

Regression Weights

Estimate S.E. C.R. P Label Kepuasan <--- Empati 2.390 .497 4.812 *** Kepuasan <--- Pelayanan .427 .070 6.074 *** Kepuasan <--- Emp_Pel -.024 .011 -2.136 .033 Loyalitas <--- Kepuasan .229 .028 8.304 *** Loyalitas <--- Emp_Puas -.007 .004 -2.025 .043 Loyalitas <--- Empati .497 .103 4.842 *** Loyalitas <--- Pelayanan .077 .007 11.835 ***

  § ⓰ၬᷤ§ §n§§

  §

   ࿠ 凐 ᷤ꼸࿠꼸§nö§ 凐ꑐ⓰ ࿠ ⓰꼸§횰ᷤ ⓰ ࿠§횰凐 ᷤ ⓰ 凐 § ᷤ ⓰ ⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ § ᷤ ⓰ ⓰ §

  凐ꑐ⓰ ࿠ ⓰꼸 ꑐ⓰ö§§ ⓰꼸࿠ §⓰ ⓰ ࿠꼸࿠꼸§횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ꑐ⓰ ၬ§꼸࿠ၬ ࿠ ࿠峄⓰ §× ©ðĀöĀĀĨÝö§Űᷤ ၬ⓰ § ᷤ횰࿠峄࿠⓰ § ࿠ 凐 ᷤ꼸࿠꼸§n§ ᷤ 깄峄 ࿠ö§

  §

  

Ū⓰ ⓰ ⓰ §§ĺ§憌⓰ ᷤ ⓰§峄凐ᷤ ࿠꼸࿠ᷤ §࿠ ࿠§ 깄峄⓰ §횰ᷤ 깄 ⓰峄⓰ §峄凐ᷤ ࿠꼸࿠ᷤ §ꑐ⓰ ၬ§ ᷤ 꼸 ⓰ ⓰ ࿠꼸⓰꼸࿠î§횰⓰峄⓰§

⓰ ၬ⓰ §횰ᷤ ࿠ ⓰ § ᷤ꼸⓰ §峄ᷤ䥈࿠ ꑐ⓰§ ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠ö§ ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ ࿠ ၬၬ࿠§ ⓰ ࿠§峄⓰ ⓰깄§꼸 ⓰ ⓰ § ᷤ 凐 § ꑐ⓰§ ࿠ ၬၬ࿠§ꑐ⓰§꼸⓰횰⓰§⓰ ⓰î§ ࿠ ⓰峄§꼸࿠ၬ ࿠ ࿠峄⓰ ö§§ §

  ࿠ ⓰࿠§Ūö § ࿠§⓰ ⓰꼸§횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰࿠§䥈 ࿠ ࿠䥈⓰ § ⓰ ࿠凐§ꑐ⓰ ၬ§ ࿠ ⓰ ⓰ 峄⓰ § ⓰ ࿠§ ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ ꑐ⓰ ၬ§ ࿠ ⓰ၬ࿠§凐 ᷤ §꼸 ⓰ ⓰ §ᷤ 凐 ꑐ⓰§×谰öŶÝö§§谰ᷤ횰⓰峄࿠ § ࿠ ၬၬ࿠§ ࿠ ⓰࿠§Ūö §꼸ᷤ횰⓰峄࿠ §꼸࿠ၬ ࿠ ࿠峄⓰ ö§ 憌⓰ ⓰깄§깄峄깄 ⓰ §꼸⓰횰 ᷤ §峄࿠ ⓰§ ᷤ꼸⓰ î§횰⓰峄⓰§ ࿠⓰꼸⓰ ꑐ⓰§ ࿠ ⓰࿠§Ūö § ࿠§⓰ ⓰꼸§ĆöĴĢ§⓰峄⓰ § 횰ᷤ ၬ ⓰꼸࿠ 峄⓰ § ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ꑐ⓰ ၬ§꼸࿠ၬ ࿠ ࿠峄⓰ § ⓰ ⓰§ ⓰ ⓰ §ĜƠî§꼸ᷤ ⓰ ၬ峄⓰ § ࿠峄⓰§ ࿠§⓰ ⓰꼸§ nöĜĢ§⓰峄⓰ §꼸࿠ၬ ࿠ ࿠峄⓰ § ⓰ ⓰§ ⓰ ⓰ §ĆÅö§ ⓰ ࿠§峄࿠ ⓰§ ࿠ ⓰峄§깄꼸⓰ § ࿠ ၬ깄 ၬ滴 谰§횰ᷤ횰 ⓰ 깄§ 峄࿠ ⓰§횰ᷤ ⓰ 깄࿠§ ࿠ ⓰࿠§ §ꑐ⓰ ၬ§횰ᷤ 깄 깄峄峄⓰ §꼸࿠ၬ ࿠ ࿠峄⓰ § ࿠ ⓰峄 ꑐ⓰ö§ § Ű࿠§ ⓰ꨤ⓰ §࿠ ࿠§ ࿠ ⓰࿠§ᷤ꼸 ࿠횰⓰꼸࿠§ꑐ⓰ ၬ§ ᷤ 꼸 ⓰ ⓰ ࿠꼸⓰꼸࿠ö§憌⓰ ⓰깄§ꑐ⓰ ၬ§깄 꼸 ⓰ ⓰ ࿠ ᷤ § ࿠꼸࿠횰 凐 峄⓰ § ᷤ ၬ⓰ §× ݧ횰⓰峄⓰§ ࿠ ⓰࿠§࿠ ࿠§ ࿠꼸࿠횰 凐 峄⓰ § ᷤ ၬ⓰ §×

  βÝö§ ⓰ §峄⓰ ⓰깄§࿠ ࿠§峄࿠ ⓰§ ࿠꼸⓰§ 횰ᷤ횰 ⓰ ࿠ ၬ峄⓰ § ᷤ꼸⓰ ꑐ⓰§ ࿠ ⓰峄 ꑐ⓰ö§§ §

  

Standardized Regression Weights

Estimate

Kepuasan <--- Empati .494

Kepuasan <--- Pelayanan .754

Kepuasan <--- Emp_Pel -.377

Loyalitas <--- Kepuasan .639

Loyalitas <--- Emp_Puas -.209

Loyalitas <--- Empati .287

Loyalitas <--- Pelayanan .381

§

  §

   ࿠ 凐 ᷤ꼸࿠꼸§Đö§憌ᷤ 깄⓰꼸⓰ §횰ᷤ ⓰ ࿠§횰ᷤ ࿠⓰ 凐 § ᷤ ⓰ ⓰ § ᷤ ⓰ꑐ⓰ ⓰ § ⓰ ⓰횰§횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §

  凐ꑐ⓰ ࿠ ⓰꼸§ ᷤ ⓰ ၬၬ⓰ ö§ ᷤ ⓰ꑐ⓰ ⓰ §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ §峄ᷤ 깄⓰꼸⓰ § ᷤ ⓰ ၬၬ⓰ § ᷤ ᷤ ࿠ § ⓰ 깄 깄§ꑐ⓰ ၬ§꼸ᷤ ⓰ 깄 ꑐ⓰§峄ᷤ 깄⓰꼸⓰ § ᷤ 꼸ᷤ 깄 §횰ᷤ ࿠ ၬ峄⓰ 峄⓰ § 凐ꑐ⓰ ࿠ ⓰꼸ö§

  § §

  ᷤ ⓰ ⓰ §峄ᷤ 깄⓰꼸⓰ §꼸ᷤ ⓰ၬ⓰࿠§횰ᷤ ࿠⓰ 凐 § ⓰ ⓰ § ࿠ ࿠ ⓰ § ⓰ ⓰§ ⓰ၬ࿠⓰ § ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬ§×࿠ ࿠ ᷤ䥈 §ᷤ ᷤ䥈 Ýö§Ū⓰ ⓰§ ⓰䥈⓰ ꑐ⓰§⓰ ⓰ ⓰ §峄凐 凐횰§횰ᷤ횰 ᷤ ࿠峄꼸࿠§ ⓰ ࿠꼸§×峄凐 凐횰§à§ ⓰ ࿠꼸Ýö§Űᷤ ၬ⓰ § ࿠ 꼸࿠ §࿠ ࿠§峄࿠ ⓰§횰ᷤ ⓰ ⓰ 峄⓰ § ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬ§ ᷤ ⓰ꑐ⓰ ⓰ § ᷤ ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸§⓰ ⓰ ⓰ § ©ĀöĀĴĮ§⓰ ⓰깄§

  β©ĀöĖnö§§ §

  

Indirect Effects

Pelayanan Emp_Pel Emp_Puas Empati Kepuasan Kepuasan .000 .000 .000 .000 .000 Loyalitas .098 -.005 .000 .547 .000

  

Standardized Indirect Effects

Pelayanan Emp_Pel Emp_Puas Empati Kepuasan Kepuasan .000 .000 .000 .000 .000 Loyalitas .482 -.241 .000 .315 .000

  ⓰ၬᷤ§ §Đ§§

  谰࿠ၬ ࿠ ࿠峄⓰ ꑐ⓰§ ⓰ 깄 ꑐ⓰§ ⓰ ࿠§횰⓰ ⓰Œ§䳤 ࿠§횰⓰꼸⓰ ⓰ ꑐ⓰滴 谰§ ᷤ ࿠ î§ ࿠ ⓰峄§횰⓰깄§ 횰ᷤ ၬᷤ 깄⓰ 峄⓰ §꼸࿠ၬ ࿠ ࿠峄⓰ § ࿠ ⓰峄 ꑐ⓰ö§Ş ⓰§ ⓰ ⓰ §횰ᷤ ၬၬ깄 ⓰峄⓰ § 凐ၬ ⓰횰§ ⓰ 깄§ꑐ⓰ ၬ§ ⓰ ⓰ § ࿠ ࿠ ⓰ § ࿠§宼࿠ ࿠⓰ 꼸凐§×nĀĆĆ Ýö§ § Ű⓰ ࿠§ ⓰ ᷤ § ࿠§⓰ꨤ⓰ §⓰ꨤ⓰ § ⓰ ࿠§峄࿠ ⓰§횰ᷤ ࿠ ⓰ § ⓰ ꨤ⓰§ ᷤ ⓰ ⓰ § ⓰ ၬ꼸깄 ၬ§ ᷤ ⓰ꑐ⓰ ⓰ § ᷤ ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸§⓰ ⓰ ⓰ §

  β©ĀöĐĮĆî§꼸ᷤ ⓰ ၬ峄⓰ § ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬ ꑐ⓰§ ⓰ ࿠§ ⓰ ᷤ § ࿠§⓰ ⓰꼸§࿠ ࿠§⓰ ⓰ ⓰ §

  β©ĀöĖĮnö§Ű⓰ ࿠§꼸࿠ ࿠§峄࿠ ⓰§ ⓰ 깄§ ⓰ ꨤ⓰§ ᷤ ⓰ ⓰ § ⓰ ၬ꼸깄 ၬ ꑐ⓰§횰⓰꼸࿠ § 峄⓰ ⓰ § ࿠ ⓰ ࿠ ၬ§ ᷤ ၬ⓰ § ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬ ꑐ⓰ö§ ⓰ ࿠î§ ᷤ ⓰ꑐ⓰ ⓰ § ᷤ ᷤ ⓰ § ᷤ ⓰ ⓰ § 凐ꑐ⓰ ࿠ ⓰꼸§ ᷤ ࿠ § ⓰ ⓰§ ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬö§

  nö§§䳤 ᷤ峄꼸§憌ᷤ ᷤ ⓰ ⓰ §滴凐 ᷤ §

  Ť⓰ ꑐ⓰峄§꼸ᷤ峄⓰ ࿠§ ᷤ ࿠꼸§࿠ ᷤ峄꼸§峄ᷤ ᷤ ⓰ ⓰ §횰凐 ᷤ § ࿠§ ⓰ ⓰횰§谰Ŷ滴î§峄࿠ ⓰§ ࿠ ࿠ §ꑐ⓰ ၬ§ ⓰ ࿠ ၬ§ 凐 깄 ᷤ §꼸⓰ ⓰§ꑐ⓰࿠ 깄§Ū滴䳤 î§ၬ凐凐 ᷤ꼸꼸§凐 § ࿠ §࿠ ᷤ §×䕴 䳤Ýî§䥈凐횰 ⓰ ⓰ ࿠ ᷤ§ ࿠ §࿠ ᷤ §§×Ū 䳤ݧ ⓰ § 滴谰ŶŞö§谰ᷤ ᷤ ၬ峄⓰ ꑐ⓰§횰ᷤ ၬᷤ ⓰࿠§䥈깄 ࿠ ၬ§ 凐࿠ §࿠ ᷤ峄꼸§峄ᷤ ᷤ ⓰ ⓰ §횰凐 ᷤ § ⓰ ⓰ §횰ᷤ ࿠ ⓰ § ⓰࿠ § 峄峄ö§×nĀĀĴݧ § ⓰꼸࿠ §⓰ ⓰ ࿠꼸࿠꼸§횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰࿠§峄⓰࿠ð峄깄⓰ ⓰ §×Ū滴䳤 ݧ꼸ᷤ ᷤ꼸⓰ §ĀöĜĮĐ§× ŌĀöĀĜÝö§ ࿠ ⓰࿠§ § ࿠§⓰ ⓰꼸§ĀöĀĜ§峄⓰ ⓰깄§ ⓰ ⓰횰§깄 ࿠§ §횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰峄§⓰ ⓰§ ᷤ ⓰§ꑐ⓰ ၬ§꼸࿠ၬ ࿠ ࿠峄⓰ ö§ ⓰ § ⓰ ⓰횰§ 谰Ŷ滴§ 깄ၬ⓰§ ᷤ횰࿠峄࿠⓰ î§ ࿠ ⓰࿠§ § ࿠§⓰ ⓰꼸§ĀöĀĜ§횰ᷤ 깄 깄峄峄⓰ § ࿠ ⓰峄§⓰ ⓰§ ᷤ ⓰§⓰ ⓰ ⓰§ ⓰ ⓰§ꑐ⓰ ၬ§ 峄࿠ ⓰§ ⓰峄⓰࿠§깄 깄峄§횰ᷤ ၬ⓰ ⓰ ࿠꼸࿠꼸§ ᷤ ၬ⓰ §횰凐 ᷤ §ꑐ⓰ ၬ§峄࿠ ⓰§峄ᷤ횰 ⓰ ၬ峄⓰ ö§Űᷤ ၬ⓰ §峄⓰ ⓰§ ⓰࿠ î§횰凐 ᷤ §峄࿠ ⓰§횰ᷤꨤ⓰峄࿠ ࿠§ ⓰ ⓰§峄࿠ ⓰ö§ §

  

Model NPAR CMIN DF P CMIN/DF

Default model 20 .583 1 .445 .583 Saturated model

21 .000

Independence model 6 2568.090 15 .000 171.206

  § 䳤 ᷤ峄꼸§ ⓰࿠ ꑐ⓰§ 깄ၬ⓰§횰ᷤ횰࿠ ࿠峄࿠§ ࿠ ⓰࿠§ꑐ⓰ ၬ§ ࿠ ⓰ ⓰ 峄⓰ î§䕴 䳤§ ⓰ §Ū 䳤§ ࿠§⓰ ⓰꼸§ĀöĴ§꼸ᷤ ⓰ ၬ峄⓰ § 滴谰Ŷާ ࿠§ ⓰ꨤ⓰ §ĀöĀĮö§ 滴谰Ŷާ⓰ ⓰ ⓰ § ࿠ ⓰࿠§ ᷤ꼸࿠ 깄§⓰ ࿠⓰꼸§꼸⓰횰 ⓰ §⓰ ⓰깄§ ᷤ횰 깄⓰ ၬ⓰ î§ ⓰ ࿠§峄࿠ ⓰§ ⓰ ⓰ 峄⓰ §꼸ᷤ꼸ᷤ ࿠峄࿠ §횰깄 ၬ峄࿠ § ⓰ ࿠⓰ ð ⓰ ࿠⓰ § ࿠§ ⓰ ⓰횰§ ⓰ ⓰§ꑐ⓰ ၬ§峄࿠ ⓰§ 깄⓰ ၬî§ ⓰ ࿠⓰꼸§ ࿠ ⓰峄§ ࿠ ࿠ ⓰ 峄⓰ § ⓰ ⓰횰§횰凐 ᷤ ö§Űᷤ ၬ⓰ §峄ᷤ꼸࿠횰 깄 ⓰ §࿠ ࿠§횰凐 ᷤ §࿠ ࿠§ ᷤ ⓰ ð ᷤ ⓰ § ࿠ § ᷤ ၬ⓰ § ⓰ ⓰ö§ §

  

Model CFI RMR GFI AGFI RMSEA

Default model 1.000 .715 .999 .986 .000

Saturated model 1.000 .000 1.000

Independence model .000 4349.690 .289 .005 .754

  § 䳤 ᷤ峄꼸§ ࿠§ ⓰ꨤ⓰ §࿠ ࿠§꼸⓰ ၬ⓰ § ᷤ ࿠ ၬ§깄 깄峄§횰ᷤ횰 ⓰ ࿠ ၬ峄⓰ §⓰ ⓰ §횰凐 ᷤ ð횰凐 ᷤ §ꑐ⓰ ၬ§ 峄࿠ ⓰§峄ᷤ횰 ⓰ ၬ峄⓰ ö§滴࿠꼸⓰ ꑐ⓰§ ⓰ ࿠§ ⓰ ࿠⓰ ᷤ ð ⓰ ࿠⓰ ᷤ § ࿠§⓰ ⓰꼸î§峄࿠ ⓰§횰ᷤ ၬᷤ횰 ⓰ ၬ峄⓰ § 횰凐 ᷤ ð횰凐 ᷤ §ꑐ⓰ ၬ§ ᷤ ᷤ ⓰ö§ ⓰ §峄⓰ ⓰깄§꼸ᷤ횰깄⓰§횰凐 ᷤ §࿠ 깄§ ࿠ î§횰⓰ ⓰峄⓰ §ꑐ⓰ ၬ§ ⓰ ࿠ ၬ§ ⓰࿠峄Œ§§憌࿠ ⓰§ ࿠꼸⓰§횰ᷤ ࿠ ⓰ §횰ᷤ ⓰ 깄࿠§⓰峄⓰࿠峄ᷤ§࿠ 凐 횰⓰ ࿠凐 §䥈 ࿠ ᷤ ࿠凐 §§×Ş䳤Ūݧ §

  

Model AIC BCC BIC CAIC

Default model 40.583 41.542 114.659 134.659

Saturated model 42.000 43.007 119.779 140.779

Independence model 2580.090 2580.378 2602.313 2608.313

  § ⓰ ᷤ § ࿠§ ⓰ꨤ⓰ §࿠ ࿠§횰ᷤ ⓰ ࿠§ ᷤ 凐횰⓰ §깄 깄峄§횰ᷤ ᷤ 깄峄⓰ §횰⓰ ⓰§ ࿠§⓰ ⓰ ⓰§ 깄⓰§횰凐 ᷤ §§ ꑐ⓰ ၬ§ ࿠ ⓰ ࿠ ၬ峄⓰ §ꑐ⓰ ၬ§ ⓰ ࿠ ၬ§횰ᷤ ၬၬ⓰횰 ⓰ 峄⓰ §횰凐 ᷤ ö§滴࿠꼸⓰ ꑐ⓰§횰凐 ᷤ §Ş§ ⓰ §Ťî§ 횰凐 ᷤ §Ť§횰ᷤ횰࿠ ࿠峄࿠§ ࿠ ⓰࿠§Ş䳤Ū§ ᷤ ࿠ § ࿠ ၬၬ࿠§ ࿠ ⓰ ࿠ ၬ§횰凐 ᷤ §Şî§횰⓰峄⓰§峄ᷤ꼸࿠횰 깄 ⓰ ꑐ⓰§ 꼸ᷤ ⓰ၬ⓰࿠§ ᷤ ࿠峄깄 §× ࿠ ᷤî§nĀĆĆÝö§

  ⓰ၬᷤ§ §Ė§§

  § §

  

Perbandingan dua model berdasarkan nilai AIC

§

Selisih nilai AIC dua Model Kesimpulan

Di bawah 2.5 Tidak ada beda ketepatan model

Antara 2.5 hingga 6 Model A lebih fit jika N>256

Antara 6 hingga 9 Model A lebih fit jika N>64

Di atas 10 Model A lebih fit

  § §

   ᷤ ᷤ ᷤ 䥈ᷤ꼸§

  ⓰࿠ î§ ö§ ö ⓰䥈峄î§宼ö§Ūö⓰ ࿠ ö§ öî§Ë§Ş ᷤ 꼸凐 î§ ö§Ŷö§×nĀĀĴÝö§滴깄 ࿠ ⓰ ࿠⓰ ᷤ§Ű⓰ ⓰§Ş ⓰ ꑐ꼸࿠꼸§ ×Ĩ §ᷤ öÝö§⑔ ᷤ §谰⓰ ᷤ§ ࿠ ᷤ î§ ĺ§ ᷤ ࿠䥈ᷤ§ ⓰ ö§

  § ࿠ ᷤî§ ö§滴ö§×nĀĆĆÝö§ ᷤၬ⓰ ࿠ ᷤ§Ť࿠ 凐횰࿠⓰ § ᷤၬ ᷤ꼸꼸࿠凐 ö§Ū⓰횰 ࿠ ၬᷤĺ§Ū⓰횰 ࿠ ၬᷤ§⑔ ࿠ ᷤ 꼸࿠ ꑐ§

  ᷤ꼸꼸ö§ § ᷤ⓰䥈 ᷤ î§憌ö§ öî§ 깄䥈峄ᷤ î§Űö§Űöî§Ë§ ⓰ꑐᷤ꼸ö§ ö§×nĀĀĨÝö§Ş ᷤ꼸꼸࿠ ၬ§滴凐 ᷤ ⓰ ᷤ §滴ᷤ ࿠⓰ ࿠凐 §

  ꑐ 凐 ᷤ꼸ᷤ꼸ĺ§ ᷤ凐 ꑐî§滴ᷤ 凐 꼸î§⓰ § ᷤ꼸䥈 ࿠ ࿠凐 꼸ö§滴깄 ࿠ ⓰ ࿠⓰ ᷤ§Ťᷤ ⓰ ࿠凐 ⓰ §

   ᷤ꼸ᷤ⓰ 䥈 î§Ėn×ĆÝî§ĆĮĜö§ 凐࿠ĺĆĀöĆĀĮĀúĀĀnĨĐĆĨĀĨĀĆĐĖĆĐĆĢ§

  § 宼࿠ ࿠⓰ 꼸凐î§宼ö§×nĀĆĆ⓰Ýö§ ࿠ 꼸§滴ᷤ ၬၬ⓰횰 ⓰ §滴凐 ᷤ § ⓰ ⓰횰§Ş滴 谰ö§Ű࿠꼸峄깄꼸࿠§滴ᷤ 凐 凐 凐ၬ࿠§

   ᷤ ᷤ ࿠ ࿠⓰ ö§Ť 凐ၬî§ö§ ᷤ ࿠ᷤ ᷤ § 깄 ᷤ§Ĝî§nĀĆĀî§ 凐횰§

  <a href="http://wahyupsy.blog.ugm.ac.id/2011/02/18/tips-menggambar-model-"> ĺúúꨤ⓰ ꑐ깄 꼸ꑐö 凐ၬö깄ၬ횰ö⓰䥈ö࿠ únĀĆĆúĀnúĆĮú ࿠ 꼸ð횰ᷤ ၬၬ⓰횰 ⓰ ð횰凐 ᷤ ð</a> ⓰ ⓰횰ð⓰횰凐꼸ú§

  § 宼࿠ ࿠⓰ 꼸凐î§宼ö§×nĀĆĆ Ýö§滴ᷤ ၬ ࿠ 깄 ၬ§谰࿠ၬ ࿠ ࿠峄⓰ 꼸࿠§ ᷤ ⓰ ⓰ § ࿠ ⓰峄§ ⓰ ၬ꼸깄 ၬ§ 凐ၬ ⓰횰§

  Ş滴 谰ö§Ű࿠꼸峄깄꼸࿠§滴ᷤ 凐 凐 凐ၬ࿠§ ᷤ ᷤ ࿠ ࿠⓰ ö§Ť 凐ၬî§ö§ ᷤ ࿠ᷤ ᷤ § 깄 ᷤ§Ĝî§nĀĆĆî§ 凐횰§ <a href="http://wahyupsy.blog.ugm.ac.id/2011/03/09/menghitung-signifikansi-"> ĺúúꨤ⓰ ꑐ깄 꼸ꑐö 凐ၬö깄ၬ횰ö⓰䥈ö࿠ únĀĆĆúĀĐúĀĴú횰ᷤ ၬ ࿠ 깄 ၬð꼸࿠ၬ ࿠ ࿠峄⓰ 꼸࿠ð</a> ᷤ ⓰ ⓰ ð ࿠ ⓰峄ð ⓰ ၬ꼸깄 ၬð 凐ၬ ⓰횰ð⓰횰凐꼸ú§

  § ⓰ၬᷤ§ §Ĝ§§