Di bawah ini kami sajikan penggalan hasil pengolahan data untuk penulisan skripsi ataupun tesis.
Model Penelitian

REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Emotional /METHOD=ENTER Sense Feel Act /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID) /SAVE RESID.
Regression
Notes |
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| Output Created |
| 13-May-2011 22:13:44 | Comments |
| | Input | Data | E:\olahdata 2011\andreas\data2.sav |
| Active Dataset | DataSet1 |
| Filter | <none> |
| Weight | <none> |
| Split File | <none> |
| N of Rows in Working Data File | 103 | Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics are based on cases with no missing values for any variable used. | Syntax |
| REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Emotional /METHOD=ENTER Sense Feel Act /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID) /SAVE RESID. | Resources | Processor Time | 00 00:00:00.672 |
| Elapsed Time | 00 00:00:00.688 |
| Memory Required | 3220 bytes |
| Additional Memory Required for Residual Plots | 640 bytes | Variables Created or Modified | RES_1 | Unstandardized Residual |
[DataSet1] E:\olahdata 2011\andreas\data2.sav
Descriptive Statistics |
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| Mean | Std. Deviation | N | Emotional | 11.11 | 2.249 | 103 | Sense | 9.59 | 1.511 | 103 | Feel | 9.91 | .864 | 103 | Act | 14.70 | 2.240 | 103 |
Descriptive statistics menggambarkan nilai rata-rata variabel, deviasi standar dan jumlah data variabel dependen dan variabel independen.
Correlations |
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| Emotional | Sense | Feel | Act | Pearson Correlation | Emotional | 1.000 | .088 | .529 | .703 |
| Sense | .088 | 1.000 | .048 | .129 |
| Feel | .529 | .048 | 1.000 | .543 |
| Act | .703 | .129 | .543 | 1.000 | Sig. (1-tailed) | Emotional | . | .188 | .000 | .000 |
| Sense | .188 | . | .317 | .098 |
| Feel | .000 | .317 | . | .000 |
| Act | .000 | .098 | .000 | . | N | Emotional | 103 | 103 | 103 | 103 |
| Sense | 103 | 103 | 103 | 103 |
| Feel | 103 | 103 | 103 | 103 |
| Act | 103 | 103 | 103 | 103 |
Variables Entered/Removedb |
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| Model | Variables Entered | Variables Removed | Method | 1 | Act, Sense, Feel | . | Enter | a. All requested variables entered. b. Dependent Variable: Emotional |
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Model Summaryb |
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| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .725a | .525 | .511 | 1.573 | a. Predictors: (Constant), Act, Sense, Feel b. Dependent Variable: Emotional |
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Koefisen korelasi R = 0.725 menunjukkan tingkat hubungan variabel dependen dengan variabel independen pada tingkat sangat kuat (0.725) untuk slala 0 – 1.
Kuat lemahnya hubungan dua variabel ditunjukkan oleh nilai Pearson Correlation (R) dimana nilai secara umum dibagi menjadi sbb:
0 – 0.25 korelasi sangat lemah 0.25 – 0.50 korelasi moderat 0.50 – 0.75 korelasi kuat 0.75 – 1.00 korelasi sangat kuat
Nilai R square = 0.525 dari tabel di atas menunjukkan bahwa 52.5 % dari varians Emotional dapat dijelaskan oleh perubahan dalam variabel Sense, feel dan Act.
ANOVAb |
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| Model |
| Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 271.010 | 3 | 90.337 | 36.531 | .000a |
| Residual | 244.815 | 99 | 2.473 |
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| Total | 515.825 | 102 |
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| a. Predictors: (Constant), Act, Sense, Feel b. Dependent Variable: Emotional |
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Uji F
Uji F dimaksudkan untuk menguji apakah variabel-variabel independen secara bersama-sama berpengaruh signifikan terhadap variabel dependen .
Hipotesis:
H0: variabel-variabel independen secara bersama-sama tidak berpengaruh signifikan terhadap variabel dependen H1: variabel-variabel independen secara bersama-sama berpengaruh signifikan terhadap variabel dependen
Dasar Pengambilan Keputusan
Jika probalitasnya (nilai sig) > 0.05 atau F hitung < F tabel maka H0 diterima Jika probalitasnya (nilai sig) < 0.05 atau F hitung > F tabel maka H0 ditolak
Keputusan:
Pada tabel di atas nilai sig = 0.000 < 0.05, sehingga H0 ditolak, yang berarti variabel-variabel independen secara bersama-sama berpengaruh signifikan terhadap variabel dependen.
Coefficientsa |
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| Model |
| Unstandardized Coefficients |
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| B | Std. Error | 1 | (Constant) | -3.015 | 2.008 |
| Sense | .003 | .104 |
| Feel | .544 | .215 |
| Act | .592 | .083 |
Coefficientsa |
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| Model |
| Standardized Coefficients | t | Sig. | Collinearity Statistics |
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| Beta |
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| Tolerance | VIF | 1 | (Constant) |
| -1.501 | .136 |
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| Sense | .002 | .033 | .974 | .983 | 1.018 |
| Feel | .209 | 2.533 | .013 | .704 | 1.420 |
| Act | .589 | 7.093 | .000 | .694 | 1.440 |
| a. Dependent Variable: Emotional | Uji t dimaksudkan untuk menguji apakah variabel independen secara parsial berpengaruh signifikan terhadap variabel dependen.Hipotesis:H0: variabel independen secara parsial tidak berpengaruh signifikan terhadap variabel dependen H1: variabel independen secara parsial berpengaruh signifikan terhadap variabel dependen Dasar Pengambilan KeputusanJika probalitasnya (nilai sig) > 0.05 atau - t tabel < t hitung < t tabel maka H0 diterimaJika probalitasnya (nilai sig) < 0.05 atau t hitung < - t tabel atau t hitung > t tabel maka H0 ditolakKeputusan:Pada tabel di atas nilai sig variabel Feel dan Act masing-masing 0.013 dan 0.000 dimana keduanya < 0.05 sehingga H0 ditolak, yang berarti variabel independen Feel dan Act secara parsial berpengaruh signifikan terhadap variabel Emotional. Sedangkan Feel tidak berpengaruh nyata kepada Emotional karena nilai sig = 0.974 > 0.05.Dengan demikian persamaan estimasinya adalah :Emotional = -3.015 + 0.03*Sense + 0.544*Feel + 0.592*ActUji MulikolinearitasMultikolinearitas (kolinearitas ganda) berarti adanya hubungan linear yang sempurna di antara variabel-variabel bebas dalam model regresi. Korelasi yang kuat antar variabel bebas menunjukkan adanya multikolinearitas. Jika terdapat korelasi yang sempurna di antara variabel bebas, maka konsekuensinya adalah koefisien-koefisien regresi menjadi tidak dapat ditaksir, nilai standard error setiap regresi menjadi tidak terhingga Ada atau tidak adanya multikolinearitas dapat dilihat dari nilai tolerance yang lebih dari 0.1 atau VIF yang kurang dari 10. Kesimpulan:Berdasarkan nilai VIF yang berada di antara 0.1 dan 10, disimpulkan tidak terjadi multikolinieritas antar variabel independen Sense, Feel dan Act.
Collinearity Diagnosticsa |
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| Model | Dimension | Eigenvalue | Condition Index | Variance Proportions |
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| (Constant) | Sense | Feel | Act | 1 | 1 | 3.965 | 1.000 | .00 | .00 | .00 | .00 |
| 2 | .022 | 13.559 | .00 | .70 | .01 | .21 |
| 3 | .011 | 19.394 | .18 | .22 | .08 | .63 |
| 4 | .003 | 35.785 | .82 | .07 | .91 | .16 | a. Dependent Variable: Emotional |
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Residuals Statisticsa |
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| Minimum | Maximum | Mean | Std. Deviation | N | Predicted Value | 6.63 | 14.24 | 11.11 | 1.630 | 103 | Residual | -3.874 | 3.670 | .000 | 1.549 | 103 | Std. Predicted Value | -2.748 | 1.921 | .000 | 1.000 | 103 | Std. Residual | -2.464 | 2.334 | .000 | .985 | 103 | a. Dependent Variable: Emotional |
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| Charts Uji NormalitasSecara penampakan visual residual berdistribusi normal. NPAR TESTS /K-S(NORMAL)=RES_1 /MISSING ANALYSIS.NPar TestsNotes |
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| Output Created |
| 13-May-2011 22:14:07 | Comments |
| | Input | Data | E:\olahdata 2011\andreas\data2.sav |
| Active Dataset | DataSet1 |
| Filter | <none> |
| Weight | <none> |
| Split File | <none> |
| N of Rows in Working Data File | 103 | Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each test are based on all cases with valid data for the variable(s) used in that test. | Syntax |
| NPAR TESTS /K-S(NORMAL)=RES_1 /MISSING ANALYSIS. | Resources | Processor Time | 00 00:00:00.015 |
| Elapsed Time | 00 00:00:00.015 |
| Number of Cases Alloweda | 196608 | a. Based on availability of workspace memory. |
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| [DataSet1] E:\olahdata 2011\andreas\data2.savOne-Sample Kolmogorov-Smirnov Test |
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| Unstandardized Residual | N |
| 103 | Normal Parametersa,b | Mean | .0000000 |
| Std. Deviation | 1.54924112 | Most Extreme Differences | Absolute | .125 |
| Positive | .044 |
| Negative | -.125 | Kolmogorov-Smirnov Z |
| 1.269 | Asymp. Sig. (2-tailed) |
| .080 | a. Test distribution is Normal. b. Calculated from data. |
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| Uji normalitas (uji Kolmogorov- Smirnov)Uji normalitas adalah untuk melihat apakah nilai residual terdistribusi normal atau tidak. Model regresi yang baik adalah memiliki nilai residual yang terdistribusi normal. Jadi uji normalitas bukan dilakukan pada masing-masing variabel tetapi pada nilai residualnya. Hipotesis:H0: data berdistribusi normalH1: data tidak berdistribusi normal Dasar Pengambilan KeputusanJika probalitasnya (nilai sig) > 0.05 maka H0 diterimaJika probalitasnya (nilai sig) < 0.05 maka H0 ditolakKeputusan:Pada tabel di atas nilai sig = 0.080 > 0.05, sehingga H0 diterima, yang berarti data residual berdistribusi normal.COMPUTE AbsRes1=abs(RES_1).EXECUTE.REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT AbsRes1 /METHOD=ENTER Sense Feel Act /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).RegressionNotes |
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| Output Created |
| 13-May-2011 22:15:00 | Comments |
| | Input | Data | E:\olahdata 2011\andreas\data2.sav |
| Active Dataset | DataSet1 |
| Filter | <none> |
| Weight | <none> |
| Split File | <none> |
| N of Rows in Working Data File | 103 | Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics are based on cases with no missing values for any variable used. | Syntax |
| REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT AbsRes1 /METHOD=ENTER Sense Feel Act /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID). | Resources | Processor Time | 00 00:00:00.625 |
| Elapsed Time | 00 00:00:00.625 |
| Memory Required | 3260 bytes |
| Additional Memory Required for Residual Plots | 640 bytes | [DataSet1] E:\olahdata 2011\andreas\data2.savDescriptive Statistics |
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| Mean | Std. Deviation | N | AbsRes1 | 1.2064 | .96463 | 103 | Sense | 9.59 | 1.511 | 103 | Feel | 9.91 | .864 | 103 | Act | 14.70 | 2.240 | 103 |
Correlations |
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| AbsRes1 | Sense | Feel | Act | Pearson Correlation | AbsRes1 | 1.000 | -.237 | .182 | -.046 |
| Sense | -.237 | 1.000 | .048 | .129 |
| Feel | .182 | .048 | 1.000 | .543 |
| Act | -.046 | .129 | .543 | 1.000 | Sig. (1-tailed) | AbsRes1 | . | .008 | .033 | .321 |
| Sense | .008 | . | .317 | .098 |
| Feel | .033 | .317 | . | .000 |
| Act | .321 | .098 | .000 | . | N | AbsRes1 | 103 | 103 | 103 | 103 |
| Sense | 103 | 103 | 103 | 103 |
| Feel | 103 | 103 | 103 | 103 |
| Act | 103 | 103 | 103 | 103 |
Variables Entered/Removedb |
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| Model | Variables Entered | Variables Removed | Method | 1 | Act, Sense, Feel | . | Enter | a. All requested variables entered. b. Dependent Variable: AbsRes1 |
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Model Summaryb |
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| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .338a | .115 | .088 | .92135 | a. Predictors: (Constant), Act, Sense, Feel b. Dependent Variable: AbsRes1 |
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ANOVAb |
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| Model |
| Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 10.873 | 3 | 3.624 | 4.269 | .007a |
| Residual | 84.040 | 99 | .849 |
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| Total | 94.913 | 102 |
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| a. Predictors: (Constant), Act, Sense, Feel b. Dependent Variable: AbsRes1 |
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Coefficientsa |
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| Model |
| Unstandardized Coefficients |
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| B | Std. Error | 1 | (Constant) | .525 | 1.177 |
| Sense | -.146 | .061 |
| Feel | .320 | .126 |
| Act | -.075 | .049 |
Coefficientsa |
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| Model |
| Standardized Coefficients | t | Sig. | Collinearity Statistics |
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| Beta |
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| Tolerance | VIF | 1 | (Constant) |
| .446 | .656 |
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| Sense | -.229 | -2.396 | .018 | .983 | 1.018 |
| Feel | .287 | 2.548 | .012 | .704 | 1.420 |
| Act | -.173 | -1.524 | .131 | .694 | 1.440 |
| a. Dependent Variable: AbsRes1 | Uji HeteroskedastisitasHeteroskedastisitas adalah kondisi dimana seluruh faktor gangguan tidak memiliki varian yang sama. Heteroskedastisitas akan menyebabkan penaksiran koefisien-koefisien regresi menjadi tidak efisien.Pendeteksian ada tidaknya heteroskedastisitas mengunakan uji Glejser yang meregresikan nilai absolute residual terhadap variabel independen.Hipotesis:H0: tidak terjadi heteroskedastisitasH1: terjadi heteroskedastisitasDasar Pengambilan KeputusanJika probalitasnya (nilai sig) > 0.05 maka H0 diterimaJika probalitasnya (nilai sig) < 0.05 maka H0 ditolakKeputusan:Pada tabel di atas nilai sig variabel Act 0.131 > 0.05, sehingga H0 diterima, yang berarti tidak terjadi heteroskedastisitas pada variabel tersebut. Sedangkan nilai sig variabel Feel dan Act, masing-masing adalah 0.018 dan 0.012 dimana keduanya < 0.05, sehingga disimpulkan terjadi heteroskedastisitas pada kedua variabel tersebut.Bila signifikansi diturunkan menjadi 0.01 maka disimpulkan tidak terjadi heteroskedastisitas pada kedua variabel tersebut.Collinearity Diagnosticsa |
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| Model | Dimension | Eigenvalue | Condition Index | Variance Proportions |
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| (Constant) | Sense | Feel | Act | 1 | 1 | 3.965 | 1.000 | .00 | .00 | .00 | .00 |
| 2 | .022 | 13.559 | .00 | .70 | .01 | .21 |
| 3 | .011 | 19.394 | .18 | .22 | .08 | .63 |
| 4 | .003 | 35.785 | .82 | .07 | .91 | .16 | a. Dependent Variable: AbsRes1 |
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Residuals Statisticsa |
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| Minimum | Maximum | Mean | Std. Deviation | N | Predicted Value | .4943 | 2.2291 | 1.2064 | .32649 | 103 | Residual | -1.41192 | 2.62438 | .00000 | .90770 | 103 | Std. Predicted Value | -2.181 | 3.132 | .000 | 1.000 | 103 | Std. Residual | -1.532 | 2.848 | .000 | .985 | 103 | a. Dependent Variable: AbsRes1 |
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| Charts 
ReliabilityNotes |
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| Output Created |
| 13-May-2011 21:18:22 | Comments |
| | Input | Data | E:\olahdata 2011\andreas\data2.sav |
| Active Dataset | DataSet1 |
| Filter | <none> |
| Weight | <none> |
| Split File | <none> |
| N of Rows in Working Data File | 103 |
| Matrix Input |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics are based on all cases with valid data for all variables in the procedure. | Syntax |
| RELIABILITY /VARIABLES=SE1 SE2 SE3 /SCALE('Sense') ALL /MODEL=ALPHA /STATISTICS=SCALE /SUMMARY=TOTAL. | Resources | Processor Time | 00 00:00:00.016 |
| Elapsed Time | 00 00:00:00.016 | [DataSet1] E:\olahdata 2011\andreas\data2.savScale: SenseCase Processing Summary |
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| N | % | Cases | Valid | 103 | 100.0 |
| Excludeda | 0 | .0 |
| Total | 103 | 100.0 | a. Listwise deletion based on all variables in the procedure. |
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Reliability Statistics |
| Cronbach's Alpha | N of Items | .797 | 3 | Reliabilitas adalah indeks yang menunjukkan sejauh mana suatu alat pengukur dapat dipercaya atau dapat diandalkan atau menunjukkan konsistensi suatu alat pengukur di dalam mengukur gejala yang sama.Hasil perhitungan di atas menunjukkan bahwa instrumen untuk Sense memiliki angka reliabilitas yang sangat tinggi (Cronbach’s Alpha = 0.797), karena menurut Nunnaly (1967) dan Hinkle (2004) ataupun indeks yang biasa digunakan dalam penelitian sosial, apabila angka Cronbach’s Alpha (α) diatas 0,70 menunjukkan bahwa konstruk atau variabel adalah reliabel. |
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