Comparison of the Performance of Restricted Maximum Likelihood and MINQUE Methods for Multilevel Data on High School Education Quality in West Java - Fitri Aulia; Budi Susetyo; Kusman Sadik (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
The development of research in various disciplines often results in tiered or hierarchical data structures. In analyzing the quality of education at the high school level in West Java, multilevel data often needs to be processed to account for the hierarchical structure of educational data. Two popular methods for estimating parameters in such models are the Restricted Maximum Likelihood (REML) and Minimum Norm Quadratic Unbiased Estimation (MINQUE) methods. This study aims to compare the performance of these two methods in handling multilevel data concerning high school education quality in West Java. The results of this study found that there is not much difference in parameter values between the REML and Jackknife MINQUE estimates. Regarding significance at the 5% significance level, the Jackknife MINQUE estimate yields a smaller p-value compared to the REML estimate. Based on empirical results from the multilevel model on high school education quality data in West Java, the best-recommended model is the Jackknife MINQUE model.
Keywords: Jacknife; Multilevel regression; MINQUE; REML