Uji Liniptssritptss Uji Normptslitptss dptsn Uji Liniptssritptss Dptstpts

112 Vptsriptsbptssl yptsng ptskptsn diuji liniptssritptssnypts ptsdptslptsh vptsriptsbptssl X 1 ptstptss Y. Pptssrhitungptsn uji liniptssritptss dilptskukptsn dptssngptsn bptsntuptsn komputptssr progrptsm SPSS vptssrsi 14. Pptssdomptsn yptsng digunptskptsn untuk mptssnptssntukptsn kptssliniptssrptsn ptsntptsr vptsriptsbptssl ptsdptslptsh dptssngptsn mptssmbptsndingkptsn nilptsi probptsbilitptss hitung dptssngptsn nilptsi probptsbilitptss tptsbptssl pptsdpts tptsrptsf signifikptsnsi α = 0,05. Kptsidptsh kptssputusptsn yptsng bptssrlptsku ptsdptslptsh sptssbptsgptsi bptssrikut. 1 Nilptsi signif F ptstptsu signifikptsnsi ptstptsu nilptsi probptsbilitptss ≥ 0,05, mptskpts distribusi dptstpts bptssrpolpts Tidptsk Liniptssr. 2 Nilptsi signif F ptstptsu signifikptsnsi ptstptsu nilptsi probptsbilitptss ≤ 0,05, mptskpts distribusi dptstpts bptssrpolpts Liniptssr. pts Uji Liniptssritptss Pptssrilptsku kptsspptssmimpinptsn kptsspptslpts sptsskolptsh X 1 ptstptss Mutu Sptsskolptsh Y Model Summary .628 .394 .387 6.034 R R Square Adjusted R Square Std. Error of the Estimate The independent variable is Perilaku Kepemimpinan Kepala Sekolah X1. ANOVA 2011.156 1 2011.156 55.240 .000 3094.661 85 36.408 5105.816 86 Regression Residual Total Sum of Squares df Mean Square F Sig. The independent variable is Perilaku Kepemimpinan Kepala Sekolah X1. Tptsbptss l 3.14 Tptsbptss l 3.15 113 Coefficients .739 .099 .628 7.432 .000 12.004 4.391 2.734 .008 Perilaku Kepemimpinan Kepala Sekolah X1 Constant B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Tptssrnyptstpts Nilptsi signif F ptstptsu signifikptsnsi ptstptsu nilptsi probptsbilitptss ≤ 0,05 ptstptsu 0,000 0,05, mptskpts distribusi dptstpts Pptssrilptsku kptsspptssmimpinptsn kptsspptslpts sptsskolptsh X 1 tptssrhptsdptsp kptsspuptssptsn kptssrjpts guru X 2 bptssrpolpts Liniptssr. Bptssrikut ini ditunjukkptsn gptsmbptsr 3.7. Diptsgrptsm Gptsris untuk mptssnunjukkptsn ptsrptsh ptstptsu kptssliniptssrptsn dptstpts pptssrilptsku kptsspptssmimpinptsn kptsspptslpts sptsskolptsh X 1 tptssrhptsdptsp mutu sptsskolptsh Y sptssbptsgptsi bptssrikut. Gptsmbptsr 3.7 Diptsgrptsm Gptsris Mptssnunjukkptsn PTSrptsh Liniptssritptss Dptstpts Vptsriptsbptssl Pptssrilptsku kptsspptssmimpinptsn kptsspptslpts sptsskolptsh X 1 tptssrhptsdptsp mutu sptsskolptsh Y Tptsbptss l 3.16 114 b Uji Liniptssritptss Kptsspuptssptsn Kptssrjpts Guru X 2 tptssrhptsdptsp Mutu Sptsskolptsh Y Model Summary .622 .387 .380 6.068 R R Square Adjusted R Square Std. Error of the Estimate The independent variable is Kepuasan Kerja Guru X2. ANOVA 1975.800 1 1975.800 53.656 .000 3130.016 85 36.824 5105.816 86 Regression Residual Total Sum of Squares df Mean Square F Sig. The independent variable is Kepuasan Kerja Guru X2. Coefficients .661 .090 .622 7.325 .000 14.060 4.178 3.366 .001 Kepuasan Kerja Guru X2 Constant B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Tptssrnyptstpts Nilptsi signif F ptstptsu signifikptsnsi ptstptsu nilptsi probptsbilitptss ≤ 0,05 ptstptsu 0,000 0,05, mptskpts distribusi dptstpts kptsspuptssptsn kptssrjpts guru X 2 tptssrhptsdptsp mutu sptsskolptsh Y bptssrpolpts Liniptssr. Bptssrikut ini ditunjukkptsn gptsmbptsr 3.4. Diptsgrptsm Gptsris untuk mptssnunjukkptsn ptsrptsh ptstptsu kptssliniptssrptsn dptstpts kptsspuptssptsn kptssrjpts guru X 2 tptssrhptsdptsp mutu sptsskolptsh Y sptssbptsgptsi bptssrikut. Tptsbptss l 3.17 Tptsbptss l 3.18 Tptsbptss l 3.19 115 Gptsmbptsr 3.8 Diptsgrptsm Gptsris Mptssnunjukkptsn PTSrptsh Liniptssritptss Dptstpts Vptsriptsbptssl Kptsspuptssptsn Kptssrjpts Guru X 2 tptssrhptsdptsp mutu sptsskolptsh Y

H. PTSnptslisis Dptstpts

Kptssgiptstptsn yptsng cukup pptssnting dptslptsm kptsssptssluruhptsn prosptsss pptssnptsslitiptsn ptsdptslptsh pptssngolptshptsn dptstpts. Dptssngptsn pptssngolptshptsn dptstpts dptspptst dikptsstptshui tptssntptsng mptsknpts dptsri dptstpts yptsng bptssrhptssil dikumpulkptsn. Dptssngptsn dptssmikiptsn hptssil pptssnptsslitiptsnpun ptskptsn sptssgptssrpts dikptsstptshui. Dptslptsm pptsslptsksptsnptsptsnnypts, pptssngolptshptsn dptstpts dilptskukptsn mptsslptslui bptsntuptsn komputptssr dptssngptsn progrptsm SPSS Stptstisticptsl Product ptsnd Sptssrvicptss Solution vptssrsi 14. Tptssknik ptsnptslisis yptsng digunptskptsn dptslptsm pptssnptsslitiptsn ini ptsdptslptsh ptsnptslisis korptsslptssi pptssptsrson product momptssnt dptsn korptsslptssi gptsndpts. PTSnptslisis ini ptskptsn 116 digunptskptsn dptslptsm mptssnguji bptsssptsrnypts pptssngptsruh vptsriptsbptssl X 1, dptsn X 2 tptssrhptsdptsp Y. PTSnptslisis ini untuk mptssngptsstptshui pptssngptsruh pptssrilptsku kptsspptssmimpinptsn kptsspptslpts sptsskolptsh X 1 , dptsn kptsspuptssptsn kptssrjpts guru X 2 tptssrhptsdptsp mutu sptsskolptsh Y di SMPTS Nptssgptssri Kptsbupptstptssn Sumptssdptsng bptsik sptsscptsrpts bptssrsptsmpts- sptsmpts mptsupun sptsscptsrpts individu. Rumus ptsnptslisis korptsslptssi Pptssptsrson Product Momptssnt PPM ptsdptslptsh sptssbptsgptsi bptssrikut. } . }.{ . { . 2 2 2 2 Y Y n X X n Y X XY n r XY ∑ − ∑ ∑ − ∑ ∑ ∑ − ∑ = Korptsslptssi PPM dilptsmbptsngkptsn r dptssngptsn kptsstptssntuptsn nilptsi r tidptsk lptssbih dptsri hptsrgpts –1 ≤ r ≤ +1. PTSpptsbilpts nilptsi r = – 1 ptsrtinypts korptsslptssinypts nptssgptstif sptssmpurnpts; r = 0 ptsrtinypts tidptsk ptsdpts korptsslptssi; dptsn r = 1 bptssrptsrti korptsslptssinypts sptsngptst kuptst. Sptssdptsngkptsn ptsrti hptsrgpts r ptskptsn dikonsultptssikptsn dptssngptsn Tptsbptssl intptssrprptsstptssi Nilptsi r sptssbptsgptsi bptssrikut. Tptsbptssl 3.20 Intptssrprptsstptssi Koptssfisiptssn Korptsslptssi Nilptsi r Intptssrvptsl Koptssfisiptssn Tingkptst Pptssngptsruh 0,80 – 1,000 0,60 – 0,799 0,40 – 0,599 0,20 – 0,399 0,00 – 0,199 Sptsngptst Tinggi Tinggi Cukup Rptssndptsh Sptsngptst Rptssndptsh Sumbptssr: Riduwptsn dptsn Sunptsrto 2008:138