BAB V KESIMPULAN DAN SARAN
B. Saran
Berdasarkan hasil penelitian, maka disarankan beberapa hal sebagai berikut: 1. Saran untuk seluruh pengguna alat tes psikologi, Psikolog, Praktisi dan
Ilmuwan Psikologi, yaitu:
a. Perlu adanya perhatian khusus terhadap administrasi tes. Karakteristik administrasi tes yang berbeda dapat membuat alat tes menjadi tidak adil. b. Bagi perusahaan ataupun praktisi Psikologi yang masih menggunakan alat tes kepribadian yang sudah terlalu lama usianya, dapat mempertimbangkan menggunakan BFI versi Indonesia sebagai salah satu alternatif pengganti.
2. Saran untuk Penelitian Selanjutnya
Adapun saran yang dapat diberikan untuk peneliti yang ingin mengembangkan penelitian ini ke tahap yang lebih lanjut, yaitu :
a. Pada penelitian selanjutnya, dapat dilakukan penelitian dengan lebih mengontrol administrasi tes online, misalnya dengan pengerjaan tes secara bersama-sama pada ruangan tes tertentu dengan menggunakan komputer dalam pengerjaannya. Administrasi tes manual dan online
hanya dibedakan dalam penggunaan komputer. Hal ini untuk melihat bagaimana respon yang dihasilkan, sehingga pengujian mengenai DIF administrasi tes akan lebih kaya. Semakin banyak pengujian yang dilakukan, maka akan memperkuat penggunaan administrasi tes BFI versi Indonesia sesuai kebutuhan dengan alasan ilmiah yang sudah teruji.
b. Disarankan agar penelitian di bidang pengembangan alat tes psikologi lebih sering dilakukan, sehingga diharapkan alat tes psikologi yang digunakan dengan berbagai tujuan di tengah-tengah masyarakat dapat senantiasa terjaga kualitasnya. Pengujian tidak hanya berhenti pada validitas dan reliabilitas saja, namun diperlukan pengujian yang lebih komprehensif sebelum dipergunakannya suatu alat tes, diantaranya adalah dengan melakukan pengujian DIF. Seperti halnya BFI versi Indonesia yang sudah diuji reliabilitas dan validitasnya, DIF administrasi tesnya, DIF Budaya (Batak Toba dan Jawa), pengujian faktor asli dengan menggunakan aitem BFI versi Indonesia yang
muncul pada orang Batak Toba. Pengujian-pengujian ini akan memperkaya informasi mengenai alat tes BFI versi Indonesia sehingga penggunaannya akan lebih terjamin.
DAFTAR PUSTAKA
Acar, Tülin. (2012). Determination of a Differential Item Functioning Procedure Using the Hierarchical Generalized Linear Model: A Comparison Study With Logistic Regression and Likelihood Ratio Procedure. Diakses melalui http://www.sagepublications.com
Anastasi, A. & Urbina, S. (1997). Psychological Testing, New Jersey : Prentice- Hall Inc.
Aslam, M. (2011). Pengaruh Administrasi Big Five Inventory terhadap Hasil BFI. Diakses melalui Universitas Sumatera Utara website: repository.usu.ac.id/ Azwar, S. (2012). Reliabilitas dan Validitas. Yogyakarta : Pustaka Pelajar _______. (2011). Metode Penelitian (cetakan XII).Yogyakarta : Pustaka Pelajar _______. (2007). Dasar-dasar Psikometri (cetakan kelima). Yogyakarta: Pustaka
Pelajar.
Bushnell, L.W.R & Mullin, J.T. (1987). Experimental Psychology: A Computerized Laboratory Course. New Jersey: Lawrence Erlbaum Associates, Publishers.
Camilli. G. & Shepard. L. A. (1994). Methods for identifying biased test items.
California : SAGE Publication, Inc.
Coaley, Keith., (2010). An Introduction to Psychological Assessment and Psychometrics. London: SAGE Publications Inc.
Dantes, Nyoman., (2012). Metode Penelitian. Yogyakarta: C.V Andi OFFSET. Field, A. (2009). Discovering Statistics Using SPSS. London: SAGE Publications
Inc.
Groth-Marnat, (1999). Handbook of Psychological Assessment. John Wiley & Sons, Inc
Hortensius, Lian. (2012). Advanced Measurement - Logistic regression for DIF detection. Diakses melalui http://www.tc.umn.edu/
Hurlock, E. B. (1993). Psikologi Perkembangan : Suatu Pendekatan Sepanjang Rentang Kehidupan(Edisi Kelima). Jakarta : Erlangga.
Jodoin, M.G. & Gierl, J., (1999). Running head: LOGISTIC REGRESSION FOR DIF DETECTION. University of Alberta, Edmonton, Alberta. Diakses melalui http://www2.education.ualberta.ca/
John, O.P. o/c of Berkeley Personality Lab (tanpa tahun). The Big Five Inventory.
Diakses melalui http://www.ocf.berkeley.edu/~johnlab/
John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (Vol. 2, pp. 102–
138). New York: Guilford Press. Diakses melalui
http://www.ocf.berkeley.edu/~johnlab/
John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm Shift to the Integrative Big-Five Trait Taxonomy: History, Measurement, and Conceptual Issues. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.),
Handbook of personality: Theory and research (pp. 114-158). New York, NY: Guilford Press. Diakses melalui http://www.ocf.berkeley.edu/~johnlab/ John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory-- Versions 4a and 54. Berkeley, CA: University of California,Berkeley, Institute of Personality and Social Research. Diakses melalui http://www.ocf.berkeley.edu/~johnlab/
Kaplan, R. M. & Saccuzzo. (2005). Psychological Testing: principles, application, and issues (6th ed.). Belmont : Thomson Wadsworth.
Lang, dkk., (2011). Short assessment of the Big Five: robust across survey methods except telephone interviewing. Behav Res, 43, 548-567. doi: 10.3758/s13428-011-0066-z. Diakses melalui springerlink.com
Mariyanti & Rahmawati, Etty. (2011). Karakteristik Psikometri Big Five Inventory (BFI) Versi Adaptasi Bahasa Indonesia. Diakses melalui Universitas Sumatera Utara website: repository.usu.ac.id/
Mastuti, Endah. (2005). Analisis Faktor Alat Ukur Kepribadian Big Five Adaptasi dari IPIP) pada Mahasiswa Suku Jawa. Diakses melalui Universitas Sumatera Utara website: repository.usu.ac.id/
McCrae & Costa. (2003). Personality in Adulthood, a Five Factor Theory Perspective. New York: The Guilford Press A Division of Guilford Publications, Inc.
Osterlind, (2010). Modern Measurement (Theory, Principles, and Application of mental Appraisal) 2nd edition. USA: Pearson Education, Inc.
Pervin, L.A., Cervon, D., & John, O.P. (2005). Personality : theory and research. New York: John Wiley & Sons, Inc.
Rahmawati, Etty. (2010). Metode Deteksi Differential Item Functioning (Pendekatan dalam Teori Respons Aitem).
Rammstedt & John. (2006). Measuring personality in one minute or less: A 10- item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203-212. Diakses melalui www.sciencedirect.com
Reeve, B. B., (tanpa tahun). An Introduction to Modern Measurement Theory.
Diakses melalui http://appliedresearch.cancer.gov/areas/cognitive/immt.pdf Sacco, dkk., (2010). Differential Item Functioning of Pathological Gambling
Criteria: An Examination of Gender, Race/Ethnicity, and Age. Journal Gambl Stud, 27, 317–330. doi: 10.1007/s10899-010-9209-x. Diakses melalui springerlink.com
Schmitt, dkk., (2007) The Geographic Distribution Of Big Five Personality Traits. Journal Of Cross-Cultural Psychology, Vol. 38 No. 2, 173-212. doi: 10.1177/0022022106297299. Diakses melalui http://biculturalism.ucr.edu/ Schultz & Schultz. (2005). Theories of Personality 8th edition. Belmond:
Wadsworth, Inc.
Scott, dkk., (2010) Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression. Diakses melalui http://www.hqlo.com/content/8/1/81
Sheppard, R., dkk. (2006). Differential Item Functioning by Sex and Race in the Hogan Personality Inventory. DOI: 10.1177/1073191106289031. Diakses melalui http://www.sagepublications.com
Sugiyono, (2012). Statistika untuk Penelitian. Bandung: Alfabeta.
Widhiarso, Wahyu., (2004). Evaluasi Faktor dalam Big Five, Pendekatan Analisis Konfirmatori. Diakses melalui widhiarso.staff.ugm.ac.id/
Zumbo, B. D. 1999. A Handbook on the Theory and Methods of Differential Item Functioning (DIF): Logistic Regression Modeling as a Unitary Framework for Binary and Likert-Type (Ordinal) Item Scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Department of National Defense. Diakses melalui http://educ.ubc.ca/faculty/DIF/handbook.pdf
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
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.
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.
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
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. 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
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 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
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 TOT_N = Total Skor Aspek Neuroticism
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 ,481 ,493 7
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 ,658 ,664 7
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 Opennes 0,853 5,909 0,816 5,799 Neuroticism 0,831 6,536 0,831 6,949 Conscientiousness 0,734 4,089 0,664 3,839 Extraversion 0,716 3,529 0,755 3,978 Agreeableness 0,493 3,026 0,622 3,567
* 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 dibantu 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
* 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* 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 ---
--- 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
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
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
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 .
* 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.: 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
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.
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)
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: