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
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.
Anda lebih suka pelaksanaan tes seperti apa?
a.
Secara Online (menggunakan internet)
b.
Secara Manual (seperti saat ini)
Tuliskan alasan anda:
____________________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
Lampiran 2.
Output SPSS Uji Normalitas Kelompok Manual
Tests of Normality
Kolmogorov-Smirnova 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
Kolmogorov-Smirnova 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.
Lampiran 3.
Output SPSS (Pearson-Product Moment)
Korelasi Antar Aspek BFI (Kelompok Manual)
Correlations
TOT_A TOT_C TOT_E TOT_N TOT_O
TOT_A
Pearson Correlation 1 ,257** ,236** -,201** ,440**
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 302 302 302 302 302
TOT_C
Pearson Correlation ,257** 1 ,200** -,486** ,463**
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 302 302 302 302 302
TOT_E
Pearson Correlation ,236** ,200** 1 -,392** ,414**
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 302 302 302 302 302
TOT_N
Pearson Correlation -,201** -,486** -,392** 1 -,480**
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 302 302 302 302 302
TOT_O
Pearson Correlation ,440** ,463** ,414** -,480** 1
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
TOT_C = Total Skor Aspek Conscientiousness
TOT_E = Total Skor Aspek Extraversion
TOT_A = Total Skor Aspek Agreeableness
Lampiran 3.
Output SPSS (Pearson-Product Moment)
Korelasi Antar Aspek BFI (Kelompok Online)
Correlations
TOT_A TOT_C TOT_E TOT_N TOT_O
TOT_A
Pearson Correlation 1 ,099 ,312** -,156** ,366**
Sig. (2-tailed) ,081 ,000 ,006 ,000
N 315 315 315 315 315
TOT_C
Pearson Correlation ,099 1 ,148** -,417** ,236**
Sig. (2-tailed) ,081 ,009 ,000 ,000
N 315 315 315 315 315
TOT_E
Pearson Correlation ,312** ,148** 1 -,260** ,345**
Sig. (2-tailed) ,000 ,009 ,000 ,000
N 315 315 315 315 315
TOT_N
Pearson Correlation -,156** -,417** -,260** 1 -,335**
Sig. (2-tailed) ,006 ,000 ,000 ,000
N 315 315 315 315 315
TOT_O
Pearson Correlation ,366** ,236** ,345** -,335** 1
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 315 315 315 315 315
**. Correlation is significant at the 0.01 level (2-tailed).
Ket: TOT_O = Total Skor Aspek Openness
TOT_C = Total Skor Aspek Conscientiousness
TOT_E = Total Skor Aspek Extraversion
TOT_A = Total Skor Aspek Agreeableness
Lampiran 4.
Output SPSS Reliabilitas (alpha cronbach) setiap Aspek BFI
(Kelompok Manual)
1.
Aspek
Openness
(O)
Reliability Statistics
Cronbach’s
Alpha
Cronbach’s
Alpha Based on
Standardized
Items
N of Items
,853 ,853 13
2.
Aspek
Conscientiousness
©
Reliability Statistics
Cronbach’s
Alpha
Cronbach’s
Alpha Based on
Standardized
Items
N of Items
,736 ,734 7
3.
Aspek
Extraversion
(E)
Reliability Statistics
Cronbach’s
Alpha
Cronbach’s
Alpha Based on
Standardized
Items
N of Items
,720 ,716 6
4.
Aspek
Agreeableness
(A)
Reliability Statistics
Cronbach’s
Alpha
Cronbach’s
Alpha Based on
Standardized
Items
N of Items
5.
Aspek
Neuroticism
(N)
Reliability Statistics
Cronbach’s
Alpha
Cronbach’s
Alpha Based on
Standardized
Items
N of Items
,834 ,831 11
Lampiran 4.
Output SPSS Reliabilitas (alpha cronbach) setiap Aspek BFI
(Kelompok Online)
1.
Aspek
Openness
(O)
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
,817 ,816 13
2.
Aspek
Conscientiousness
(C)
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
3.
Aspek
Extraversion
(E)
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
,758 ,755 6
4.
Aspek
Agreeableness
(A)
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
,618 ,622 7
5.
Aspek
Neuroticism
(N)
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
,833 ,831 11
Lampiran 4.
Koefisien Reliabilitas dan Standar Deviasi setiap aspek BFI
Aspek
Kelompok Manual Kelompok Online
Reliabilitas Standar
Deviasi Reliabilitas
Standar Deviasi
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
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 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.
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 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.
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 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.
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.
******************** 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 ---
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
... 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
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 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 .
* 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.: 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
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 ********************
---
-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)
******************** 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
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 .
* 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.: 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 ---
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 ---
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
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
Estimations, standard errors, and effects
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.
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
******************** 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
******************** 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
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
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 ---
TOT_N ,211493 ,038676 5,468281 ,000000 1,235521
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.
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 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
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 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: 1476,799
-2 Log-Likelihood of full Model: 1104,474
LR-statistic
Chisqu. DF Prob. %-Reduct 372,325 2,000 ,000 ,252
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_O ,317271 ,019535 16,241537 ,000000 1,373375 6,396297
grup -,265645 ,164612 -1,613762 ,106579 ,766712 ,875552
Const.1 -9,100970 ,896567 -10,150906 ,000000 ,000112 1,000000
Const.2 -11,013610 ,876482 -12,565694 ,000000 ,000016 1,000000
Const.3 -14,588522 ,966290 -15,097453 ,000000 ,000000 1,000000
Const.4 -17,920201 1,062193 -16,870939 ,000000 ,000000 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 51,32
Standardized regression weights of the latent variable: TOT_O ,7139
grup -,0511
--- END MATRIX ---
Matrix
Run MATRIX procedure:
LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE
(by Steffen M. KUEHNEL)
Interaction term TOT_O*grup int1.1 TOT_O 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 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
Means and standard deviations of independent variables: Mean Std.Dev.
TOT_O 48,5624 5,8490 grup 1,5105 ,5003 int1.1 73,3177 25,8937
******************** 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: 1104,349
LR-statistic
Chisqu. DF Prob. %-Reduct 372,450 3,000 ,000 ,252
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_O ,301862 ,047616 6,339484 ,000000 1,352375 5,845026
grup -,758523 1,402497 -,540837 ,588620 ,468358 ,684214
int1.1 ,010168 ,028731 ,353905 ,723410 1,010220 1,301203
Const.2 -10,263329 2,290239 -4,481334 ,000007 ,000035 1,000000
Const.3 -13,842204 2,314722 -5,980073 ,000000 ,000001 1,000000
Const.4 -17,172003 2,360582 -7,274479 ,000000 ,000000 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 51,30
Standardized regression weights of the latent variable: TOT_O ,6793
grup -,1460 int1.1 ,1013
--- END MATRIX ---
Lampiran 6.
OUTPUT SPSS REGRESI LOGISTIK ORDINAL
(aitem nomor 6 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:
(by Steffen M. KUEHNEL)
******************** Information Section ********************
Dependent variable is: item
Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 43,00 6,97 93,03 2,00 152,00 24,64 68,40 3,00 187,00 30,31 38,09 4,00 152,00 24,64 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
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: 1860,344
-2 Log-Likelihood of full Model: 1319,154
Chisqu. DF Prob. %-Reduct 541,190 1,000 ,000 ,291
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_E ,616883 ,032557 18,947606 ,000000 1,853142 10,229251
Const.1 -8,390482 ,570179 -14,715524 ,000000 ,000227 1,000000
Const.2 -11,535403 ,652809 -17,670404 ,000000 ,000010 1,000000
Const.3 -13,804299 ,719021 -19,198748 ,000000 ,000001 1,000000
Const.4 -16,070053 ,786779 -20,425111 ,000000 ,000000 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 62,17
Standardized regression weights of the latent variable: TOT_E ,7885
--- END MATRIX ---
Matrix
Run MATRIX procedure:
LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE
(by Steffen M. KUEHNEL)
******************** Information Section ********************
Dependent variable is: item
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
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: 1860,344
-2 Log-Likelihood of full Model: 1316,456
LR-statistic
Chisqu. DF Prob. %-Reduct 543,888 2,000 ,000 ,292
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_E ,616068 ,032588 18,904470 ,000000 1,851633 10,197895
Const.1 -7,968926 ,622088 -12,809962 ,000000 ,000346 1,000000
Const.2 -11,129293 ,694725 -16,019702 ,000000 ,000015 1,000000
Const.3 -13,411085 ,754654 -17,771161 ,000000 ,000001 1,000000
Const.4 -15,677746 ,818898 -19,144936 ,000000 ,000000 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 62,34
Standardized regression weights of the latent variable: TOT_E ,7856
grup -,0435
--- 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 43,00 6,97 93,03 2,00 152,00 24,64 68,40 3,00 187,00 30,31 38,09 4,00 152,00 24,64 13,45 5,00 83,00 13,45 ,00
Effective sample size: 617
Means and standard deviations of independent variables: Mean Std.Dev.
******************** 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: 1860,344
-2 Log-Likelihood of full Model: 1316,397
LR-statistic
Chisqu. DF Prob. %-Reduct 543,947 3,000 ,000 ,292
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_E ,633436 ,078637 8,055243 ,000000 1,884074 10,887860
grup -,025232 ,966163 -,026115 ,979165 ,975084 ,987456
int1.1 -,011064 ,045498 -,243179 ,807867 ,988997 ,877122
Const.1 -8,335537 1,632946 -5,104602 ,000000 ,000240 1,000000
Const.2 -11,493421 1,653334 -6,951661 ,000000 ,000010 1,000000
Const.3 -13,776505 1,684323 -8,179254 ,000000 ,000001 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 62,35
Standardized regression weights of the latent variable: TOT_E ,8078
grup -,0043 int1.1 -,0444
--- END MATRIX ---
Lampiran 6.
OUTPUT SPSS REGRESI LOGISTIK ORDINAL
(aitem nomor 7 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 ********************
item
Marginal distribution of dependent variable Value Frequ. Percent %>Value 1,00 4,00 ,65 99,35 2,00 19,00 3,08 96,27 3,00 178,00 28,85 67,42 4,00 297,00 48,14 19,29 5,00 119,00 19,29 ,00
Effective sample size: 617
Means and standard deviations of independent variables: Mean Std.Dev.
TOT_A 25,8379 3,3136
******************** 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: 1441,086
-2 Log-Likelihood of full Model: 1293,307
LR-statistic
Chisqu. DF Prob. %-Reduct 147,779 1,000 ,000 ,103
Estimations, standard errors, and effects ---
TOT_A ,304680 ,026597 11,455330 ,000000 1,356191 2,744502
Const.1 -2,311450 ,793581 -2,912682 ,003583 ,099117 1,000000
Const.2 -4,147150 ,658645 -6,296483 ,000000 ,015809 1,000000
Const.3 -6,992550 ,674233 -10,371125 ,000000 ,000919 1,000000
Const.4 -9,569778 ,729124 -13,125037 ,000000 ,000070 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 23,65
Standardized regression weights of the latent variable: TOT_A ,4864
--- 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 19,00 3,08 96,27 3,00 178,00 28,85 67,42 4,00 297,00 48,14 19,29 5,00 119,00 19,29 ,00
Effective sample size: 617
Means and standard deviations of independent variables: Mean Std.Dev.
******************** 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: 1441,086
-2 Log-Likelihood of full Model: 1292,410
LR-statistic
Chisqu. DF Prob. %-Reduct 148,676 2,000 ,000 ,103
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_A ,303929 ,026634 11,411385 ,000000 1,355172 2,737674
grup -,147300 ,155602 -,946644 ,343820 ,863035 ,928956
Const.1 -2,067399 ,834407 -2,477688 ,013224 ,126514 1,000000
Const.2 -3,899922 ,708185 -5,506925 ,000000 ,020243 1,000000
Const.3 -6,748827 ,721108 -9,358973 ,000000 ,001172 1,000000
Const.4 -9,330259 ,770848 -12,103896 ,000000 ,000089 1,000000
Results assuming a latent continuous variable ---
Standardized regression weights of the latent variable: TOT_A ,4848
grup -,0355
--- END MATRIX ---
Matrix
Run MATRIX procedure:
LOGISTIC REGRESSION with an ORDINAL DEPENDENT VARIBLE
(by Steffen M. KUEHNEL)
Interaction term TOT_A*grup int1.1 TOT_A 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 19,00 3,08 96,27 3,00 178,00 28,85 67,42 4,00 297,00 48,14 19,29 5,00 119,00 19,29 ,00
Effective sample size: 617
Means and standard deviations of independent variables: Mean Std.Dev.
TOT_A 25,8379 3,3136 grup 1,5105 ,5003 int1.1 38,9562 13,8543
******************** Estimation Section ********************
Running Iteration No.: 1
Running Iteration No.: 2
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: 1441,086
-2 Log-Likelihood of full Model: 1289,227
LR-statistic
Chisqu. DF Prob. %-Reduct 151,859 3,000 ,000 ,105
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_A ,444050 ,083341 5,328121 ,000000 1,559008 4,355411
grup 2,093111 1,266169 1,653106 ,098309 8,110104 2,849581
int1.1 -,086822 ,048717 -1,782154 ,074724 ,916840 ,300336
Const.1 -5,660452 2,180366 -2,596102 ,009429 ,003481 1,000000
Const.2 -7,512822 2,148941 -3,496057 ,000472 ,000546 1,000000
Const.3 -10,367888 2,161765 -4,796030 ,000002 ,000031 1,000000
Const.4 -12,959009 2,185293 -5,930102 ,000000 ,000002 1,000000
Results assuming a latent continuous variable ---
R-Square (%): 24,36
Standardized regression weights of the latent variable: TOT_A ,7055
grup ,5021 int1.1 -,5767
Lampiran 6.
OUTPUT SPSS REGRESI LOGISTIK ORDINAL
(aitem nomor 8 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
5,00 12,00 1,94 ,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: 1563,463
-2 Log-Likelihood of full Model: 1296,216
LR-statistic
Chisqu. DF Prob. %-Reduct 267,248 1,000 ,000 ,171
Estimations, standard errors, and effects ---
Coeff.=B Std.Err. B/Std.E. Prob. exp(B) exp(B*S)
TOT_C ,364499 ,024519 14,865991 ,000000 1,439792 4,299068
