Pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling: studi dengan simulasi Monte Carlo

Muhammad Dwirifqi Kharisma Putra, Jahja Umar, Bahrul Hayat, Agung Priyo Utomo

Abstract


Studi ini menggunakan simulasi Monte Carlo dilakukan untuk melihat pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling. Kondisi simulasi diciptakan dengan beberapa faktor yang ditetapkan yaitu lima kondisi ICC (0.05, 0.10, 0.15, 0.20, 0.25), jumlah kelompok (30, 50, 100 dan 150), jumlah observasi dalam kelompok (10, 20 dan 50) dan diestimasi menggunakan lima metode estimasi: ML, MLF, MLR, WLSMV dan BAYES. Jumlah kondisi keseluruhan sebanyak 300 kondisi dimana tiap kondisi direplikasi sebanyak 1000 kali dan dianalisis menggunakan software Mplus 7.4. Kriteria bias yang masih dapat diterima adalah < 10%. Hasil penelitian ini menunjukkan bahwa bias yang terjadi dipengaruhi oleh ukuran sampel dan ICC, penelitian ini juga menujukkan bahwa metode estimasi WLSMV dan BAYES berfungsi lebih baik pada berbagai kondisi dibandingkan dengan metode estimasi berbasis ML.

Kata kunci: multilevel latent variable modeling, intraclass correlation coefficients, Metode Markov Chain Monte Carlo

 

THE IMPACT OF SAMPLE SIZE AND INTRACLASS CORRELATION COEFFICIENTS (ICC) ON THE BIAS OF PARAMETER ESTIMATION IN MULTILEVEL LATENT VARIABLE MODELING: A MONTE CARLO STUDY

Abstract

A monte carlo study was conducted to investigate the effect of sample size and intraclass correlation coefficients (ICC) on the bias of parameter estimates in multilevel latent variable modeling. The design factors included (ICC: 0.05, 0.10, 0.15, 0.20, 0.25), number of groups in between level model (NG: 30, 50, 100 and 150), cluster size (CS: 10, 20 and 50) to be estimated with five different estimator: ML, MLF, MLR, WLSMV and BAYES. Factors were interegated into 300 conditions (4 NG  3 CS  5 ICC  5 Estimator). For each condition, replications with convergence problems were exclude until at least 1.000 replications were generated and analyzed using Mplus 7.4, we also consider absolute percent bias <10% to represent an acceptable level of bias. We find that the degree of bias depends on sample size and ICC. We also show that WLSMV and BAYES estimator performed better than ML-based estimator across varying sample sizes and ICC’s conditions.

Keywords: multilevel latent variable modeling, intraclass correlation coefficients, Markov Chain Monte Carlo method

Full Text:

Fulltext PDF

References


American Psychological Association. (2015). APA dictionary of psychology (2nd ed.). Washington, DC: American Psychological Association

Asparouhov, T., & Muthén, B. O. (2003). Full-information maximum-likelihood estimation of general two-level latent variable models with missing data. Mplus Working Paper. Los Angeles, CA: Muthén & Muthén, Inc

Asparouhov, T., & Muthén, B. O. (2012). Saddle points. Mplus Working Paper. Los Angeles, CA: Muthén & Muthén, Inc

Asparouhov, T., & Muthén, B. O. (2014). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. Mplus Working Paper. Los Angeles, CA: Muthén & Muthén, Inc

Beauducel, A. & Herzberg, P. Y. (2006) On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA, Structural Equation Modeling: A Multidisciplinary Journal, 13(2), 186-203

Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.

Burstein, L. (1980). The analysis of multilevel data in educational research and evaluation. Review of research in education, 8(1), 158-233

Cai, L. (2010b). Metropolis-hastings robbins-monro algorithm for confirmatory item factor analysis. Journal of Educational and Behavioral Statistics, 35(3), 307-335

de Leeuw, J. & Meijer, E. (2008), Handbook of multilevel analysis. New York, NY: Springer.

Dyer, N. G., Hanges, P. J., Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. The Leadership Quarterly. 16, 149–167

Feinberg, R. A. & Rubright, J. D. (2016). Conducting simulation studies in psychometrics. Educational Measurement: Issues and Practice, 35(2), 36-49

Geldhof, G. J., Preacher, K. J., Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91

Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). London: Wiley.

Hayes, A. F. (2006). A primer on multilevel modeling. Human communication research, 32, 385–410




DOI: https://doi.org/10.21831/pep.v21i1.12895

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Our journal indexed by:


ISSN 2338-6061 (online)

View Journal Visitor Stats