BIG FIVE INVENTORY (Versi Indonesia)

  LAMPIRAN

Silahkan isi data diri anda. Data anda akan dijamin kerahasiannya dan hanya dipergunakan untuk kepentingan penelitian

  Usia : _____ Tahun Jenis Kelamin : P / L * Suku : __________________________ Pekerjaan : __________________________

   Apakah anda sudah pernah mengikuti tes kepribadian?

  a. Sudah Pernah

  b. Belum pernah  Bagaimana prosedur pelaksanaan tes tersebut?

  a. Via Online

  b. Melalui biro psikologi

  c. Menggunakan jasa Psikolog (kepentingan konseling dsb)

  d. Mengikuti tes psikotest untuk pekerjaan

  e. Lain-Lain, sebutkan ______________

  BIG FIVE INVENTORY (Versi Indonesia) Berikut adalah beberapa karakteristik yang mungkin atau mungkin tidak menggambarkan diri anda.

  Misalnya pada pernyataan “saya adalah seseorang yang senang menghabiskan waktu dengan orang lain”, maka tuliskan nomor di samping pernyataan yang menyatakan anda setuju atau tidak setuju dengan pernyataan tersebut.

  1 = SANGAT TIDAK SETUJU 3 = NETRAL 4 = SETUJU 2 = TIDAK SETUJU 5 = SANGAT SETUJU

Saya adalah seseorang (yang)… 1. _______ Suka mengobrol

  2. _______ Cenderung mencari kesalahan orang lain. 3. _______ Mengerjakan tugas sampai selesai. 4. _______ Mudah merasa tertekan dan sedih. 5. _______ Memiliki ide-ide yang inovatif. 6. _______ Suka menyendiri. 7. _______ Senang membantu dan tidak egois. 8. _______ Terkadang ceroboh. 9. _______ Dapat menghadapi situasi stress dengan baik. 10. _______ Memiliki rasa ingin tahu terhadap banyak hal. 11. _______ Penuh semangat. 12. _______ Tidak takut berargumentasi dengan orang lain. 1. _______ Suka mengobrol. 2. _______ Cenderung mencari kesalahan orang lain. 3. _______ Mengerjakan tugas sampai selesai. 4. _______ Mudah merasa tertekan dan sedih. 5. _______ Memiliki ide-ide yang inovatif. 6. _______ Suka menyendiri. 7. _______ Senang membantu dan tidak egois. 8. _______ Terkadang ceroboh. 9. _______ Dapat menghadapi situasi stress dengan baik. 10. _______ Memiliki rasa ingin tahu terhadap banyak hal. 11. _______ Penuh semangat. 12. _______ Tidak takut berargumentasi dengan orang lain. 13. _______ Pekerja yang dapat diandalkan. 14. _______ Mudah merasa cemas. 15. _______ Cerdas dan suka berpikir. 16. _______ Penuh antusiasme. 17. _______ Mudah memaafkan. 18. _______ Cenderung tidak teratur atau berantakan. 19. _______ Pencemas. 20. _______ Memiliki imajinasi yang tinggi 21. _______ Cenderung pendiam

  23. _______ Cenderung pemalas 24. _______ Secara emosional stabil, tidak mudah tersinggung 25. _______ Mudah menemukan suatu ide baru.

  26. _______ Percaya diri. 27. _______ Cenderung menjaga jarak dengan orang lain. 28. _______ Mampu bertahan hingga suatu tugas selesai. 29. _______ Suasana hati mudah berubah. 30. _______ Menghargai hal-hal yang indah dan berseni. 31. _______ Terkadang pemalu. 32. _______ Baik dan perhatian hampir terhadap setiap orang. 33. _______ Melakukan sesuatu dengan efisien. 34. _______ Tetap tenang pada situasi yang menegangkan. 35. _______ Lebih menyukai pekerjaan yang rutin. 36. _______ Santai dan mudah bergaul. 37. _______ Terkadang kasar kepada orang lain. 38. _______ Dapat membuat rencana dan menjalankannya. 39. _______ Mudah merasa cemas. 40. _______ Mempertimbangkan gagasan-gagasan yang ada. 41. _______ Kurang memiliki ketertarikan terhadap seni. 42. _______ Senang bekerjasama dengan orang lain. 43. _______ Perhatiannya mudah terganggu.

   Anda lebih suka pelaksanaan tes seperti apa?

  a. Secara Online (menggunakan internet)

  b. Secara Manual (seperti saat ini)  Tuliskan alasan anda:

____________________________________________________________________

___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________

  TERIMA KASIH

Lampiran 2. Output SPSS Uji Normalitas Kelompok Manual Tests of Normality

  a

  Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. TOT_ALL ,048 302 ,087 ,994 302 ,226

  a. Lilliefors Significance Correction

  Lampiran 2. Output SPSS Uji Normalitas Kelompok Online Tests of Normality a

  Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig.

• *

  TOT_ALL ,041 315 ,200 ,995 315 ,427 *. This is a lower bound of the true significance.

  a. Lilliefors Significance Correction

  

Lampiran 3. Output SPSS (Pearson-Product Moment)

Korelasi Antar Aspek BFI (Kelompok Manual)

  Correlations

  TOT_A TOT_C TOT_E TOT_N TOT_O

  Pearson Correlation 1 ,257 ,236 -,201 ,440 TOT_A Sig. (2-tailed) ,000 ,000 ,000 ,000

  N 302 302 302 302 302

  Pearson Correlation ,257 1 ,200 -,486 ,463 TOT_C Sig. (2-tailed) ,000 ,000 ,000 ,000

  N 302 302 302 302 302

  Pearson Correlation ,236 ,200 1 -,392 ,414 TOT_E Sig. (2-tailed) ,000 ,000 ,000 ,000

  N 302 302 302 302 302

  Pearson Correlation -,201 -,486 -,392 1 -,480 TOT_N Sig. (2-tailed) ,000 ,000 ,000 ,000

  N 302 302 302 302 302

  Pearson Correlation ,440 ,463 ,414 -,480

  1 TOT_O Sig. (2-tailed) ,000 ,000 ,000 ,000 N 302 302 302 302 302 **. Correlation is significant at the 0.01 level (2-tailed).

  Ket: TOT_O = Total Skor Aspek Openness

  Total Skor Aspek Conscientiousness

  TOT_C = TOT_E = Total Skor Aspek Extraversion TOT_A = Total Skor Aspek Agreeableness TOT_N = Total Skor Aspek Neuroticism

  

Lampiran 3. Output SPSS (Pearson-Product Moment)

Korelasi Antar Aspek BFI (Kelompok Online)

  Correlations

  TOT_A TOT_C TOT_E TOT_N TOT_O

  Pearson Correlation 1 ,099 ,312 -,156 ,366 TOT_A Sig. (2-tailed) ,081 ,000 ,006 ,000

  N 315 315 315 315 315

  Pearson Correlation ,099 1 ,148 -,417 ,236 TOT_C Sig. (2-tailed) ,081 ,009 ,000 ,000

  N 315 315 315 315 315

  Pearson Correlation ,312 ,148 1 -,260 ,345 TOT_E Sig. (2-tailed) ,000 ,009 ,000 ,000

  N 315 315 315 315 315

  Pearson Correlation -,156 -,417 -,260 1 -,335 TOT_N Sig. (2-tailed) ,006 ,000 ,000 ,000

  N 315 315 315 315 315

  Pearson Correlation ,366 ,236 ,345 -,335

  1 TOT_O Sig. (2-tailed) ,000 ,000 ,000 ,000 N 315 315 315 315 315 **. Correlation is significant at the 0.01 level (2-tailed).

  Total Skor Aspek Openness

  Ket: TOT_O = TOT_C = Total Skor Aspek Conscientiousness

  Total Skor Aspek Extraversion

  TOT_E = TOT_A = Total Skor Aspek Agreeableness TOT_N = Total Skor Aspek Neuroticism

  Lampiran 4. Output SPSS Reliabilitas (alpha cronbach) setiap Aspek BFI (Kelompok Manual)

Aspek Openness (O) 1. Reliability Statistics

  Cronbach Cronbach N of Items ’s ’s

  Alpha Alpha Based on Standardized

  Items ,853 ,853

  13

  2. Aspek Conscientiousness © Reliability Statistics

  Cronbach ’s Cronbach ’s N of Items Alpha Alpha Based on

  Standardized Items

  ,736 ,734

  7

  3. Aspek Extraversion (E) Reliability Statistics

  Cronbach Cronbach N of Items ’s ’s

  Alpha Alpha Based on Standardized

  Items ,720 ,716

  6

  4. Aspek Agreeableness (A) Reliability Statistics

  Cronbach Cronbach N of Items ’s ’s

  Alpha Alpha Based on Standardized

  Items ,481 ,493

  7

  5. Aspek Neuroticism (N) Reliability Statistics

  Standardized Items

  N of Items ,658 ,664

  Standardized Items

  Cronbach's Alpha Based on

  Cronbach's Alpha

  2. Aspek Conscientiousness (C) Reliability Statistics

  13

  N of Items ,817 ,816

  Cronbach's Alpha Based on

  Cronbach ’s Alpha

  Cronbach's Alpha

  1. Aspek Openness (O) Reliability Statistics

  (Kelompok Online)

  11 Lampiran 4. Output SPSS Reliabilitas (alpha cronbach) setiap Aspek BFI

  N of Items ,834 ,831

  Standardized Items

  Cronbach ’s Alpha Based on

  7

  3. Aspek Extraversion (E) Reliability Statistics

  5. Aspek Neuroticism (N) Reliability Statistics

  Conscientiousness 0,734 4,089 0,664 3,839 Extraversion 0,716 3,529 0,755 3,978

  Opennes 0,853 5,909 0,816 5,799 Neuroticism 0,831 6,536 0,831 6,949

  Reliabilitas Standar Deviasi

  Aspek Kelompok Manual Kelompok Online Reliabilitas

Standar

Deviasi

  11 Lampiran 4. Koefisien Reliabilitas dan Standar Deviasi setiap aspek BFI

  N of Items ,833 ,831

  Standardized Items

  Cronbach's Alpha Based on

  Cronbach's Alpha

  7

  Cronbach's Alpha

  N of Items ,618 ,622

  Standardized Items

  Cronbach's Alpha Based on

  Cronbach's Alpha

  4. Aspek Agreeableness (A) Reliability Statistics

  6

  N of Items ,758 ,755

  Standardized Items

  Cronbach's Alpha Based on

  Agreeableness 0,493 3,026 0,622 3,567

  

Lampiran 5. SPSS SYNTAX Regresi Logistik Ordinal

• Analisis regresi logistik ordinal menggunakan analisi tambahan dan SPSS syntax yang diambil dari Handbook Zumbo (1999)
• SPSS SYNTAX ditulis oleh: Bruno D. Zumbo, PhD. (Profesor Psikologi dan Matematika University of Northern British Columbia)
• Analisis ini diban tu dengan program tambahan bernama “ologit2.inc” yang berada dalam satu folder yang sama dengan file SPSS yang akan dianalisis
• Penulisan syntax disesuaikan dengan penulisan file yang ada pada penelitian ini, dimana perubahan penulisan hanya pada “item” , “total”, dan “grp” berdasarkan nama file yang digunakan pada penelitian ini.
• Kemudian dilakukan berulang sesuai dengan nama file yang akan dianalisis sebanyak 44 kali sesuai dengan jumlah aitem pada BFI versi Indonesia

Lampiran 5. SPSS SYNTAX Regresi Logistik Ordinal

  

(aitem nomor 1 BFI versi Indonesia)

* SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. execute. GET FILE='Aspek_Extraversion.sav'. EXECUTE . compute item= a1.

  compute TOT_E= scale. compute grup= group.

• Regression model with the conditioning variable, total score, in alone. ologit var = item TOT_E /output=all. execute.
• Regression model adding uniform DIF to model. ologit var = item TOT_E grup /contrast grup=indicator /output=all. execute.
• Regression model adding non-uniform DIF to the model. ologit var = item TOT_E grup TOT_E*grup /contrast grup=indicator /output=all. execute.

Lampiran 5. SPSS SYNTAX Regresi Logistik Ordinal

  

(aitem nomor 20 BFI versi Indonesia)

* SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. execute. GET FILE='Aspek_Openness.sav'. EXECUTE . compute item= a20.

  compute TOT_O= scale. compute grup= group.

• Regression model with the conditioning variable, total score, in alone. ologit var = item TOT_O /output=all. execute.
• Regression model adding uniform DIF to model. ologit var = item TOT_O grup /contrast grup=indicator /output=all. execute.
• Regression model adding non-uniform DIF to the model. ologit var = item TOT_O grup TOT_O*grup /contrast grup=indicator /output=all. execute.

Lampiran 5. SPSS SYNTAX Regresi Logistik Ordinal

  

(aitem nomor 26 BFI versi Indonesia)

* SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. execute. GET FILE='Aspek_Openness.sav'. EXECUTE . compute item= a26.

  compute TOT_O= scale. compute grup= group.

• Regression model with the conditioning variable, total score, in alone. ologit var = item TOT_O /output=all. execute.
• Regression model adding uniform DIF to model. ologit var = item TOT_O grup /contrast grup=indicator /output=all. execute.
• Regression model adding non-uniform DIF to the model. ologit var = item TOT_O grup TOT_O*grup /contrast grup=indicator /output=all. execute.

  

Lampiran 6. OUTPUT SPSS REGRESI LOGISTIK ORDINAL

(aitem nomor 1 BFI versi Indonesia)

* SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. 2696 0 set printback off. Warning # 235 The position and length given in a macro SUBSTR function are inconsistent with the string argument. The null string has been used for the result.

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 9,00 1,46 98,54 2,00 58,00 9,40 89,14

  3,00 212,00 34,36 54,78 4,00 255,00 41,33 13,45 5,00 83,00 13,45 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_E 20,9028 3,7694

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1586,962
• 2 Log-Likelihood of full Model: 1357,424 LR-statistic Chisqu. DF Prob. %-Reduct 229,539 1,000 ,000 ,145 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_E ,340862 ,024205 14,082573 ,000000 1,406160 3,614062 Const.1 -2,061691 ,533522 -3,864301 ,000111 ,127239 1,000000

  Const.2 -4,415937 ,463650 -9,524297 ,000000 ,012083 1,000000 Const.3 -6,861216 ,507986 -13,506702 ,000000 ,001048 1,000000 Const.4 -9,476386 ,572099 -16,564229 ,000000 ,000077 1,000000 Results assuming a latent continuous variable

• R-Square (%): 33,41

  Standardized regression weights of the latent variable: TOT_E ,5780

• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 9,00 1,46 98,54 2,00 58,00 9,40 89,14

  3,00 212,00 34,36 54,78 4,00 255,00 41,33 13,45 5,00 83,00 13,45 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_E 20,9028 3,7694 grup 1,5105 ,5003

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4

  ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1586,962
• 2 Log-Likelihood of full Model: 1342,911 LR-statistic Chisqu. DF Prob. %-Reduct 244,051 2,000 ,000 ,154 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_E ,350785 ,024577 14,273133 ,000000 1,420182 3,751796 grup ,592560 ,156501 3,786313 ,000153 1,808612 1,345081 Const.1 -3,120986 ,608063 -5,132670 ,000000 ,044114

  1,000000 Const.2 -5,485358 ,550728 -9,960193 ,000000 ,004147 1,000000 Const.3 -7,949376 ,593213 -13,400552 ,000000 ,000353 1,000000 Const.4 -10,622946 ,660560 -16,081721 ,000000 ,000024 1,000000 Results assuming a latent continuous variable

• R-Square (%): 35,19 Standardized regression weights of the latent variable: TOT_E ,5868 grup ,1316
• END MATRIX -----

  Matrix

  Run MATRIX procedure:

  LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL) Interaction term TOT_E*grup int1.1 TOT_E grup

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 9,00 1,46 98,54 2,00 58,00 9,40 89,14

  3,00 212,00 34,36 54,78 4,00 255,00 41,33 13,45 5,00 83,00 13,45 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_E 20,9028 3,7694 grup 1,5105 ,5003 int1.1 31,4554 11,8501

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1586,962
• 2 Log-Likelihood of full Model: 1341,544 LR-statistic Chisqu. DF Prob. %-Reduct 245,418 3,000 ,000 ,155 Estimations, standard errors, and effects

• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_E ,275015 ,069154 3,976850 ,000070 1,316550 2,819696 grup -,415258 ,876012 -,474033 ,635477 ,660170 ,812409 int1.1 ,048493 ,041507 1,168320 ,242678 1,049688 1,776506

  Const.1 -1,547198 1,477573 -1,047121 ,295044 ,212844 1,000000 Const.2 -3,904620 1,457056 -2,679801 ,007367 ,020149 1,000000 Const.3 -6,364859 1,475075 -4,314939 ,000016 ,001721 1,000000 Const.4 -9,048418 1,495064 -6,052195 ,000000 ,000118 1,000000 Results assuming a latent continuous variable

• R-Square (%): 35,37 Standardized regression weights of the latent variable: TOT_E ,4595 grup -,0921 int1.1 ,2547
• END MATRIX -----

  Lampiran 6. OUTPUT SPSS REGRESI LOGISTIK ORDINAL (aitem nomor 2 BFI versi Indonesia) * SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .

• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. 2696 0 set printback off. Warning # 235 The position and length given in a macro SUBSTR function are inconsistent with the string argument. The null string has been used for the result.

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 2,00 ,32 99,68 2,00 42,00 6,81 92,87

  3,00 159,00 25,77 67,10 4,00 260,00 42,14 24,96 5,00 154,00 24,96 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_C 23,7634 4,0011

• Estimation Section ******************** Running Iteration No.:

  1

  Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1556,704
• 2 Log-Likelihood of full Model: 1337,696 LR-statistic Chisqu. DF Prob. %-Reduct 219,008 1,000 ,000 ,141 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,314726 ,023200 13,566042 ,000000 1,369883 3,522779 Const.1 -,979867 ,848158 -1,155289 ,247972 ,375361 1,000000

  Const.2 -4,295383 ,510387 -8,415939 ,000000 ,013631 1,000000 Const.3 -6,569303 ,538332 -12,203071 ,000000 ,001403 1,000000 Const.4 -8,912143 ,592457 -15,042678 ,000000 ,000135 1,000000 Results assuming a latent continuous variable

• R-Square (%): 32,52 Standardized regression weights of the latent variable: TOT_C ,5703
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 2,00 ,32 99,68 2,00 42,00 6,81 92,87

  3,00 159,00 25,77 67,10 4,00 260,00 42,14 24,96 5,00 154,00 24,96 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_C 23,7634 4,0011 grup 1,5105 ,5003

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero

• 2 Log-Likelihood of Model with Constants only: 1556,704
• 2 Log-Likelihood of full Model: 1337,564 LR-statistic Chisqu. DF Prob. %-Reduct 219,141 2,000 ,000 ,141 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,313794 ,023337 13,446302 ,000000 1,368608 3,509676 grup -,056505 ,155426 -,363549 ,716195 ,945062 ,972127 Const.1 -,871583 ,898677 -,969850 ,332121 ,418289

  1,000000 Const.2 -4,187573 ,589835 -7,099573 ,000000 ,015183 1,000000 Const.3 -6,462811 ,612231 -10,556163 ,000000 ,001560 1,000000 Const.4 -8,805916 ,659795 -13,346434 ,000000 ,000150 1,000000 Results assuming a latent continuous variable

• R-Square (%): 32,55 Standardized regression weights of the latent variable: TOT_C ,5685 grup -,0128
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL) Interaction term TOT_C*grup int1.1 TOT_C grup

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 2,00 ,32 99,68 2,00 42,00 6,81 92,87

  3,00 159,00 25,77 67,10 4,00 260,00 42,14 24,96 5,00 154,00 24,96 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_C 23,7634 4,0011 grup 1,5105 ,5003 int1.1 35,6094 12,6493

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1556,704
• 2 Log-Likelihood of full Model: 1336,714 LR-statistic Chisqu. DF Prob. %-Reduct 219,990 3,000 ,000 ,141

  Estimations, standard errors, and effects

• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,258995 ,063668 4,067927 ,000047 1,295628 2,818677 grup -,942914 ,975610 -,966486 ,333801 ,389491 ,623919 int1.1 ,037660 ,040917 ,920399 ,357364 1,038378 1,610224

  Const.1 ,449922 1,691900 ,265927 ,790295 1,568191 1,000000 Const.2 -2,876197 1,538123 -1,869939 ,061492 ,056349 1,000000 Const.3 -5,164827 1,531798 -3,371742 ,000747 ,005714 1,000000 Const.4 -7,507427 1,551444 -4,838995 ,000001 ,000549 1,000000 Results assuming a latent continuous variable

• R-Square (%): 32,72 Standardized regression weights of the latent variable: TOT_C ,4686 grup -,2133 int1.1 ,2154
• END MATRIX -----

  Lampiran 6. OUTPUT SPSS REGRESI LOGISTIK ORDINAL (aitem nomor 3 BFI versi Indonesia) * SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.

• Run this entire syntax command file. include file='ologit2.inc'. 2696 0 set printback off. Warning # 235 The position and length given in a macro SUBSTR function are inconsistent with the string argument. The null string has been used for the result.

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 4,00 ,65 99,35 2,00 22,00 3,57 95,79

  3,00 126,00 20,42 75,36 4,00 281,00 45,54 29,82 5,00 184,00 29,82 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_C 23,7634 4,0011

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4

  Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1474,597
• 2 Log-Likelihood of full Model: 1193,015 LR-statistic Chisqu. DF Prob. %-Reduct 281,582 1,000 ,000 ,191 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,380724 ,025840 14,733753 ,000000 1,463344 4,587424 Const.1 -2,875815 ,705651 -4,075408 ,000046 ,056370 1,000000

  Const.2 -4,959111 ,554488 -8,943580 ,000000 ,007019 1,000000 Const.3 -7,473970 ,578388 -12,922060 ,000000 ,000568 1,000000 Const.4 -10,231690 ,651131 -15,713719 ,000000 ,000036 1,000000 Results assuming a latent continuous variable

• R-Square (%): 41,36 Standardized regression weights of the latent variable: TOT_C ,6431
• END MATRIX -----

  Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 4,00 ,65 99,35 2,00 22,00 3,57 95,79

  3,00 126,00 20,42 75,36 4,00 281,00 45,54 29,82 5,00 184,00 29,82 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_C 23,7634 4,0011 grup 1,5105 ,5003

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only:

  1474,597

• 2 Log-Likelihood of full Model: 1191,225 LR-statistic Chisqu. DF Prob. %-Reduct 283,372 2,000 ,000 ,192 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,385192 ,026117 14,748707 ,000000 1,469896 4,670156 grup ,216305 ,161847 1,336479 ,181393 1,241481 1,114289 Const.1 -3,303320 ,776929 -4,251763 ,000021 ,036761

  1,000000 Const.2 -5,383288 ,641531 -8,391313 ,000000 ,004593 1,000000 Const.3 -7,900119 ,664036 -11,897132 ,000000 ,000371 1,000000 Const.4 -10,666760 ,732696 -14,558232 ,000000 ,000023 1,000000 Results assuming a latent continuous variable

• R-Square (%): 41,56 Standardized regression weights of the latent variable: TOT_C ,6496 grup ,0456
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL) Interaction term TOT_C*grup int1.1 TOT_C grup

• Information Section ********************

  Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 4,00 ,65 99,35 2,00 22,00 3,57 95,79 3,00 126,00 20,42 75,36 4,00 281,00 45,54 29,82 5,00 184,00 29,82 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev. TOT_C 23,7634 4,0011 grup 1,5105 ,5003 int1.1 35,6094 12,6493

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1474,597
• 2 Log-Likelihood of full Model: 1191,212 LR-statistic Chisqu. DF Prob. %-Reduct 283,386 3,000 ,000 ,192

  Estimations, standard errors, and effects

• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_C ,377850 ,069326 5,450301 ,000000 1,459143 4,534961 grup ,101137 1,021051 ,099052 ,921097 1,106428 1,051900 int1.1 ,004947 ,043302 ,114237 ,909050 1,004959 1,064571

  Const.1 -3,128116 1,718892 -1,819844 ,068783 ,043800 1,000000 Const.2 -5,209702 1,648797 -3,159698 ,001579 ,005463 1,000000 Const.3 -7,728188 1,644096 -4,700569 ,000003 ,000440 1,000000 Const.4 -10,494642 1,674392 -6,267733 ,000000 ,000028 1,000000 Results assuming a latent continuous variable

• R-Square (%): 41,55 Standardized regression weights of the latent variable: TOT_C ,6372 grup ,0213 int1.1 ,0264
• END MATRIX -----

  

Lampiran 6. OUTPUT SPSS REGRESI LOGISTIK ORDINAL

(aitem nomor 4 BFI versi Indonesia)

* SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. 2696 0 set printback off. Warning # 235

  The position and length given in a macro SUBSTR function are inconsistent with the string argument. The null string has been used for the result.

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 61,00 9,89 90,11 2,00 167,00 27,07 63,05

  3,00 177,00 28,69 34,36 4,00 149,00 24,15 10,21 5,00 63,00 10,21 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_N 32,5041 6,8267

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1871,784
• 2 Log-Likelihood of full Model: 1435,738 LR-statistic Chisqu. DF Prob. %-Reduct 436,045 1,000 ,000 ,233 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_N ,285215 ,015860 17,983011 ,000000 1,330048 7,008246 Const.1 -5,934817 ,455266 -13,035942 ,000000 ,002646 1,000000

  Const.2 -8,292013 ,493379 -16,806563 ,000000 ,000251 1,000000 Const.3 -10,311413 ,556510 -18,528727 ,000000 ,000033 1,000000 Const.4 -12,732164 ,637366 -19,976220 ,000000 ,000003 1,000000 Results assuming a latent continuous variable

• R-Square (%): 53,54 Standardized regression weights of the latent variable: TOT_N ,7317
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 61,00 9,89 90,11 2,00 167,00 27,07 63,05

  3,00 177,00 28,69 34,36 4,00 149,00 24,15 10,21 5,00 63,00 10,21 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_N 32,5041 6,8267 grup 1,5105 ,5003

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1871,784
• 2 Log-Likelihood of full Model: 1432,466 LR-statistic

  Chisqu. DF Prob. %-Reduct 439,318 2,000 ,000 ,235 Estimations, standard errors, and effects

• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_N ,283294 ,015906 17,810520 ,000000 1,327495 6,916919 grup ,277032 ,153272 1,807459 ,070691 1,319209 1,148662 Const.1 -6,282027 ,497188 -12,635121 ,000000 ,001870

  1,000000 Const.2 -8,639426 ,533175 -16,203747 ,000000 ,000177 1,000000 Const.3 -10,667379 ,594575 -17,941170 ,000000 ,000023 1,000000 Const.4 -13,103960 ,674890 -19,416434 ,000000 ,000002 1,000000 Results assuming a latent continuous variable

• R-Square (%): 53,87 Standardized regression weights of the latent variable: TOT_N ,7242 grup ,0519
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL) Interaction term TOT_N*grup int1.1 TOT_N grup

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 61,00 9,89 90,11

  2,00 167,00 27,07 63,05 3,00 177,00 28,69 34,36 4,00 149,00 24,15 10,21 5,00 63,00 10,21 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_N 32,5041 6,8267 grup 1,5105 ,5003 int1.1 49,6256 20,9133

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1871,784
• 2 Log-Likelihood of full Model: 1428,401 LR-statistic Chisqu. DF Prob. %-Reduct 443,383 3,000 ,000 ,237 Estimations, standard errors, and effects
• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)

  TOT_N ,211493 ,038676 5,468281 ,000000 1,235521 4,236795 grup -1,263377 ,779994 -1,619727 ,105291 ,282698 ,531495 int1.1 ,047553 ,023630 2,012357 ,044182 1,048702 2,703333 Const.1 -3,978688 1,237918 -3,214016 ,001309 ,018710 1,000000 Const.2 -6,326599 1,253919 -5,045460 ,000000 ,001788 1,000000 Const.3 -8,352902 1,280980 -6,520712 ,000000 ,000236 1,000000 Const.4 -10,832612 1,300539 -8,329325 ,000000 ,000020 1,000000 Results assuming a latent continuous variable

• R-Square (%): 54,29 Standardized regression weights of the latent variable: TOT_N ,5382 grup -,2356 int1.1 ,3707
• END MATRIX -----

  Lampiran 6. OUTPUT SPSS REGRESI LOGISTIK ORDINAL (aitem nomor 5 BFI versi Indonesia) * SPSS SYNTAX written by: .

• Bruno D. Zumbo, PhD .
• Professor of Psychology and Mathematics, .
• University of Northern British Columbia .
• e-mail: zumbob@unbc.ca .
• Instructions .
• Copy this file and the file "ologit2.inc", and your SPSS data file into the same folder .
• Change the filename, currently 'binary.sav' to your file name .
• Change 'item', 'total', and 'grp', to the corresponding variables in your file.
• Run this entire syntax command file. include file='ologit2.inc'. 2696 0 set printback off. Warning # 235 The position and length given in a macro SUBSTR function are inconsistent with the string argument. The null string has been used for the result.

  Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 9,00 1,46 98,54 2,00 30,00 4,86 93,68

  3,00 227,00 36,79 56,89 4,00 274,00 44,41 12,48 5,00 77,00 12,48 ,00 Effective sample size: 617 Means and standard deviations of independent variables: Mean Std.Dev.

  TOT_O 48,5624 5,8490

• Estimation Section ******************** Running Iteration No.:

  1 Running Iteration No.:

  2 Running Iteration No.:

  3 Running Iteration No.:

  4 Running Iteration No.:

  5 ..... Optimal solution found.

• OUTPUT SECTION ******************** LR-test that all predictor weights are zero
• 2 Log-Likelihood of Model with Constants only: 1476,799
• 2 Log-Likelihood of full Model: 1107,085 LR-statistic Chisqu. DF Prob. %-Reduct 369,714 1,000 ,000 ,250 Estimations, standard errors, and effects

• Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S) TOT_O ,316744 ,019506 16,238145 ,000000 1,372652 6,376616 Const.1 -9,497409 ,865834 -10,969087 ,000000 ,000075 1,000000

  Const.2 -11,400566 ,846255 -13,471779 ,000000 ,000011 1,000000 Const.3 -14,964803 ,941374 -15,896770 ,000000 ,000000 1,000000 Const.4 -18,284943 1,041360 -17,558723 ,000000 ,000000 1,000000 Results assuming a latent continuous variable

• R-Square (%): 51,06 Standardized regression weights of the latent variable: TOT_O ,7146
• END MATRIX -----

Matrix

  Run MATRIX procedure: LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE (by Steffen M. KUEHNEL)

• Information Section ******************** Dependent variable is: item Marginal distribution of dependent variable