APLIKASI MODEL RASCH CAMPURAN DALAM MENGEVALUASI PENGUKURAN HARGA DIRI

Wahyu Widhiarso

doi:10.21831/pep.v17i1.1367

APLIKASI MODEL RASCH CAMPURAN DALAM MENGEVALUASI PENGUKURAN HARGA DIRI

Wahyu Widhiarso, Universitas Gadjah Mada, Indonesia

10.21831/pep.v17i1.1367

Abstract

Penelitian ini bertujuan untuk mengeksplorasi keberadaan kelompok responden yang menyebabkan estimasi parameter butir melalui pemodelan Rasch tidak invarian pada keseluruhan responden. Teknik analisis yang dipakai adalah model Rasch campuran yang merupakan penggabungan antara Model Rasch dan analisis kelas laten. Dengan menggunakan data hasil pengukuran harga diri didapatkan hasil analisis bahwa keseluruhan responden penelitian sebanyak 2.987 dapat dikategorikan menjadi tiga kelas berdasarkan pola respons mereka pada skala. Hasil estimasi parameter butir pada responden pada masing-masing kelas dengan menggunakan model kredit parsial menunjukkan bahwa ketiga kelas memiliki parameter butir yang berbeda. Dua kelas relatif sesuai dengan model, sedangkan satu kelas tidak sesuai karena responden pada kelas tersebut merespons skala dengan cara yang unik. Keberadaan responden dengan respons unik ini relatif kecil (12,5%) sehingga tidak mengganggu estimasi parameter pada keseluruhan butir.

Kata kunci: model Rasch campuran, parameter butir, kelas responden

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RASCH MIXED MODEL APPLICATION IN EVALUATING THE MEASUREMENT OF SELF-ESTEEM

Abstract This study aimed to explore the existence of groups of items that cause Estimation of item parameters using Rasch modeling was not invariant for all respondents. Mixed Rasch model which is the combination between Rasch Models and Latent Class Analysis was employed. By using data from measuring self-esteem found for overall respondents (N=2987) can be categorized into three classes based on their item respons patterns on entire scale. Results based on estimation of item parameters to the respondents in each class using the Partial Credit Model found that each class has different item parameters. Two classes supported the model while the other class did not; due to respondents on this class give a response on the scale in a unique way. The proportion of the respondents with a unique response is relatively small (12,5%) therefore they do not much interfere the estimation of item parameters on the overall items.

Keywords: mixed rasch model, Item Estimation Parameter, Class

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References

Bem, D. J., & Allen, A. (1974). On predicting some of the people some of the time: The search for cross-situational consistencies in behavior. Psychological Review, 81(6), 506-552.

Cox, B. J., Swinson, R. P., Direnfeld, D. M., & Bourdeau, D. (1994). Social desirability and self-reports of alcohol abuse in anxiety disorder patients. Behaviour Research and Therapy, 32(1), 175-178. doi: 10.1016/0005-7967(94)90100-7

Eid, M., & Zickar, M. J. (2007). Detecting response styles and faking in personality and organizational assessments by mixed Rasch models. In M. Von Davier & C. Carstensen (Eds.), Multivariate and Mixture Distribution Rasch Models. New York: Springer.

Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists: Multivariate Applications. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Fisher, R. J. (1993). Social Desirability Bias and the Validity of Indirect Questioning. Journal of Consumer Research, 20(2), 303-315.

Goldstein, H., & Blinkhorn, S. (1982). The Rasch Model Still Does Not Fit. British Educational Research Journal, 8(2), 167-170.

Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174.

Osterlind, S. J., & Everson, H. T. (2009). Differential Item Functioning. Thousand Oaks: Sage Publications, Inc.

Peacock, G. G., Ervin, R. A., & Daly, E. J. (2009). Practical Handbook of School Psychology: Effective Practices for the 21st Century. New York, NY: Guilford Press.

Preinerstorfer, D., & Formann, A. K. (2011). Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical and Statistical Psychology. doi: 10.1111/j.2044-8317.2011.02020.x

Retnowati, S. (2004). Depresi Pada Remaja: Model Integrasi Penyebab Depresi Dan Pengatasan Depresi Pada Remaja. Disertasi, Universitas Gadjah Mada, Yogyakarta.

Rosenberg, M. (1965). Society and the Adolescent Self-Image. Princeton, NJ: Princeton University Press.

Rost, J. (1988). Rating scale analysis with latent class models. Psychometrika, 53(3), 327-348. doi: 10.1007/bf02294216

Rost, J. (1990). Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis. Applied Psychological Measurement, 14(3), 271-282. doi: 10.1177/014662169001400305

Rost, J. (1997). Logistic mixture models. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of Modern Item Response Theory. New York: Springer-Verlag.

Rost, J., Carstensen, C., & von Davier, M. (1997). Applying the mixed Rasch model to personality questionnaires. In J. Rost & R. Langeheine (Eds.), Applications of Latent Trait and Latent Class Models in the Social Sciences. Münster: Waxmann.

Smit, A., Kelderman, H., & van der Flier, H. (1999). Collateral information and Mixed Rasch models. Methods of Psychological Research Online, 4(3).

Van de Mortel, T. F. (2008). Faking it: Social desirability response bias in self-report research. Australian Journal of Advanced Nursing, 25(4), 40-48.

Von Davier, M. (2001). WINMIRA 2001. Kiel: Institute for Science Education.

Yamamoto, K. (1989). A Hybrid model of IRT and latent class models (ETS Research Report). Princeton, NJ: Educational Testing Service.

DOI: https://doi.org/10.21831/pep.v17i1.1367