Goodness Of Fit Test In Structural Equation Modeling with Unweighted Least Square (ULS) Estimation Method
Abstract
Structural equation model (SEM) is a multivariate analysis method that is used to describe a linear relationship simultaneously between indicator variables and latent variables. There are several estimation methods in SEM, one of them is Unweighted Least Square (ULS). The method doesn‟t have specific assumptions about the distribution of variables. This study aims to estimate the model using the ULS method and see the influence of employee competency variables and library facilities on the quality of service at the University of Lampung library. Survey of quality of service in the library of Lampung University is used in the research. Based on the results of the study, it is found that from the three suitability tests, namely the overall model test, the structural model test and the measurement model test using ULS estimation give good results in explaining the compatibility between the model and observation results.
References
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092.
Dhaene, S., & Rosseel, Y. (2023). An Evaluation of Non-Iterative Estimators in Confirmatory Factor Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 1–13.
Du, H., & Bentler, P. M. (2022). Distributionally weighted least squares in structural equation modeling. Psychological Methods, 27(4), 519.
Foldnes, N., & Grønneberg, S. (2022). The sensitivity of structural equation modeling with ordinal data to underlying non-normality and observed distributional forms. Psychological Methods, 27(4), 541.
Ghanbar, H., & Rezvani, R. (2023). Structural Equation Modeling in L2 Research: A Systematic Review. International Journal of Language Testing, 13(Special Issue), 79–108.
Hidayat, R., & Wulandari, P. (2022). Data Analysis Procedures with Structural Equation Modelling (SEM): Narrative Literature Review. Open Access Indonesia Journal of Social Sciences, 5(6), 859–865.
Hikmah, Z., Wijayanto, H., & Aidi, M. N. (2023). SELECTION OF THE BEST SEM MODEL TO IDENTIFY FACTORS AFFECTING MARKETING PERFORMANCE IN THE ICT INDUSTRY. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(2), 1149–1162.
Kyriazos, T., & Poga-Kyriazou, M. (2023). Applied Psychometrics: Estimator Considerations in Commonly Encountered Conditions in CFA, SEM, and EFA Practice. Psychology, 14(5), 799–828.
Mahmoodi, Z., Yazdkhasti, M., Rostami, M., & Ghavidel, N. (2022). Factors affecting mental health and happiness in the elderly: A structural equation model by gender differences. Brain and Behavior, 12(5), e2549.
Mokhtar, S. F., Yusof, Z. M., & Sapiri, H. (2023). Confidence Intervals by Bootstrapping Approach: A Significance Review. Malaysian Journal of Fundamental and Applied Sciences, 19(1), 30–42.
Pagadala, S. P., Sangeetha, V., Venkatesh, P., & Jha, G. K. (2023). An Overview of Structural Equation Modeling and Its Application in Social Sciences Research. Social Research Methodology and Publishing Results: A Guide to Non-Native English Speakers, 145–163.
Robitzsch, A. (2022). Comparing the robustness of the structural after measurement (SAM) approach to structural equation modeling (SEM) against local model misspecifications with alternative estimation approaches. Stats, 5(3), 631–672.
Tarko, A. P. (2023). Maximum likelihood method of estimating the conflict-crash relationship. Accident Analysis & Prevention, 179, 106875.
Zulkifli, N., Aimran, N., & Deni, S. (2023). The performance of unweighted least squares and regularized unweighted least squares in estimating factor loadings in structural equation modeling. International Journal of Data and Network Science, 7(3), 1017–1024.
