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Jurnal Penelitian dan Evaluasi Pendidikan

Keywords

Bayesian; confirmatory factor analysis; general self-efficacy scale-12; self-efficacy

Document Type

Article

Abstract

The General Self-Efficacy Scale 12 (GSES-12) is a brief measure for assessing self-efficacy. This study aimed to revise an Indonesian language version of the GSES-12 that was translated and adopted from previous research. The revision conducted by following the Guidelines for the Process of Cross-Cultural Adaptation of Self-Report Measures, and the final version was administered to 303 (132 male, 171 female) Indonesian students, with a mean age of 19.56 years (SD: 1.20). This study is presented to establish the construct validity of this instrument further. The results of Bayesian CFA revealed a higher-order structure of factor representing constructs of self-efficacy. Considering the theoretical background and the best model fit indices (PPP-value = 0.549 and BRMSEA = 0.001), it is concluded that the Indonesian version of GSES-12 appears to be a valid instrument in assessing self-efficacy in Indonesian speaking students and is expected to facilitate the examination of self-efficacy in Indonesian speaking populations.

First Page

12

Last Page

25

Issue

1

Volume

23

Digital Object Identifier (DOI)

10.21831/pep.v23i1.20008

References

Asparouhov, T., & Muthén, B. (2012). Comparison of computational methods for high dimensional item factor analysis. In Mplus technical report. Los Angeles, CA: Muthen & Muthen.

Bandura, A. (2012). On the functional properties of perceived self-efficacy revisited. Journal of Management, 38(1), 9-44. https://doi.org/10.1177/0149206311410606

Bashkov, B. M. (2015). Examining the performance of the Metropolis-Hastings Robbins-Monro algorithm in the estimation of multilevel multidimensional IRT Models. Unpbulished doctoral dissertation, James Madison University, Harrisonburg, VA.

Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191.

Bosscher, R. J., & Smit, J. H. (1998). Confirmatory factor analysis of the General Self-Efficacy Scale. Behaviour Research and Therapy, 36(3), 339-343. https://doi.org/10.1016/S0005-7967(98)00025-4

Brown, T. (2015). Confirmatory factor analysis for applied research: Second edition. New York, NY: The Guilford Press. https://doi.org/10.1680/geot.8.B.012

Cai, L. (2008). A Metropolis-Hastings Robbins-Monro algorithm for maximum likelihood nonlinear latent structure analysis with a comprehensive measurement model. Unpublished doctoral dissertation, University of North Carolina, Chapel Hill, NC.

Cai, L. (2013). Factor analysis of tests and items. In K. F. Geisinger (Ed.), APA handbook of testing and assessment in psychology. Washington, DC: American Psychological Association.

Cai, Li. (2010a). High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm. Psychometrika, 75(1), 33-57. https://doi.org/10.1007/s11336-009-9136-x

Cai, Li. (2010b). Metropolis-Hastings Robbins-Monro algorithm for confirmatory item factor analysis. Journal of Educational and Behavioral Statistics, 35(3), 307-335. https://doi.org/10.3102/1076998609353115

Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62-83. https://doi.org/10.1177/109442810141004

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). London: Chapman & Hall CRC.

Gelman, A., Meng, X. L., & Stern, H. S. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica, 6(4), 733-807.

Hoofs, H., van de Schoot, R., Jansen, N. W. H., & Kant, Ij. (2018). Evaluating model fit in Bayesian confirmatory factor analysis with large samples: Simulation study introducing the BRMSEA. Educational and Psychological Measurement, 78(4), 537-568. https://doi.org/10.1177/0013164417709314

Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329-367. https://doi.org/10.1177/0049124198026003003

Joreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36(2), 109-133.

Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Newbury Park, CA: Sage Publications.

Kaplan, D. (2014). Bayesian statistics for the social sciences. New York, NY: Guilford Press.

Klompstra, L., Jaarsma, T., & Strömberg, A. (2018). Self-efficacy mediates the relationship between motivation and physical activity in patients with heart failure. The Journal of Cardiovascular Nursing, 33(3), 211-216. https://doi.org/10.1097/JCN.0000000000000456

Luszczynska, A., Gutiérrez-Doña, B., & Schwarzer, R. (2005). General self-efficacy in various domains of human functioning: Evidence from five countries. International Journal of Psychology, 40(2), 80-89. https://doi.org/10.1080/00207590444000041

Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1-30. https://doi.org/10.18637/jss.v085.i04

Moors, G. (2008). Exploring the effect of a middle response category on response style in attitude measurement. Quality & Quantity, 42(6), 779-794. https://doi.org/10.1007/s11135-006-9067-x

Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313-335. https://doi.org/10.1037/a0026802

Muthén, L. K., & Muthén, B. O. (2017). Mplus user's guide: Statistical analysis with latent variables (8th ed.). Los Angeles, CA: Muthén & Muthén.

Muthén, Linda K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599-620. https://doi.org/10.1207/S15328007SEM0904_8

Putra, M. D. K., & Tresniasari, N. (2015). Pengaruh dukungan sosial dan selfefficacy terhadap orientasi masa depan pada remaja. TAZKIYA Journal of Psychology, 3(1), 71-82. Retrieved from http://journal.uinjkt.ac.id/index.php/tazkiya/article/view/9194

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338. https://doi.org/10.3200/JOER.99.6.323-338

Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user's portfolio. Causal and control beliefs (pp. 35-37). Windsor, UK: NFER-NELSON.

Sherer, M., Maddux, J. E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., & Rogers, R. W. (1982). The self-efficacy scale: Construction and validation. Psychological Reports, 51(2), 663-671. https://doi.org/10.2466/pr0.1982.51.2.663

Sorbom, D. (1989). Model modification. Psychometrika, 54(3), 371-384.

Tiyuri, A., Saberi, B., Miri, M., Shahrestanaki, E., Bayat, B., & Salehiniya, H. (2018). Research self-efficacy and its relationship with academic performance in postgraduate students of Tehran University of Medical Sciences in 2016. Journal of Education and Health Promotion, 7(1), 11. https://doi.org/10.4103/jehp.jehp_43_17

van de Schoot, R., & Depaoli, S. (2014). Bayesian analyses: Where to start and what to report. The European Health Psychologist, 16(2), 75-84.

van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85(3), 842-860. https://doi.org/10.1111/cdev.12169

van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A systematic review of Bayesian articles in psychology: The last 25 years. Psychological Methods, 22(2), 217-239. https://doi.org/10.1037/met0000100

Willson-Conrad, A., & Kowalske, M. G. (2018). Using self-efficacy beliefs to understand how students in a general chemistry course approach the exam process. Chemistry Education Research and Practice, 19(1), 265-275. https://doi.org/10.1039/C7RP00073A

Woodruff, S. L., & Cashman, J. F. (1993). Task, domain, and general efficacy: A reexamination of the self-efficacy scale. Psychological Reports, 72(2), 423-432. https://doi.org/10.2466/pr0.1993.72.2.423

Yang, J. S., & Cai, L. (2014). Estimation of contextual effects through nonlinear multilevel latent variable modeling with a Metropolis-Hastings Robbins-Monro algorithm. Journal of Educational and Behavioral Statistics, 39(6), 550-582. https://doi.org/10.3102/1076998614559972

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