https://www.openaccessjournals.eu/index.php/ijdias/issue/feed 2024-05-14T15:01:44+00:00 Rosa Batista [email protected] Open Journal Systems <p><strong>International Journal of Discoveries and Innovations in Applied Sciences</strong> (<strong>IJDIAS</strong>) <strong><em>ISSN <a href="https://portal.issn.org/resource/ISSN/2792-3983">2792-3983</a></em></strong> is open access, peer-reviewed journal that focused on multidisciplinary research areas. <strong>IJIDIAS </strong>is a peer-reviewed international online journal in English published monthly. The scope of this journal is to publish the original research and review of all categories of science. <strong>Journal</strong> has created to make better development on innovative research on applied sciences.</p> https://www.openaccessjournals.eu/index.php/ijdias/article/view/2627 2024-05-14T15:01:06+00:00 Kareem Khalaf Aazer [email protected]

<p><span style="color: #0d0d0d; font-family: Söhne, ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, Ubuntu, Cantarell, 'Noto Sans', sans-serif, 'Helvetica Neue', Arial, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: pre-wrap; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">Multilevel regression models are widely utilized in various fields, such as health, education, and agriculture, due to their ability to address dependencies between variables at different levels of aggregation. This study compares two estimation methods for parameters in multilevel regression models: the maximum likelihood method (MLE) and the variance constraints method (RCR). Utilizing simulation experiments repeated 1000 times across different sample sizes (15, 40, and 60), the study evaluates these methods using the Akaike Information Criterion (AIC) and mean square error (MSE). Results indicate that the MLE consistently outperforms the RCR in terms of lower AIC and MSE values, especially as sample size increases. Despite the closeness in efficiency between the methods at smaller sample sizes, the MLE's superiority becomes more pronounced with larger samples, suggesting its robustness and reliability for parameter estimation in multilevel regression models. This finding is crucial for researchers seeking accurate and efficient estimation techniques in hierarchical data analysis.</span></p>

2024-05-14T00:00:00+00:00 Copyright (c) 2024 Kareem Khalaf Aazer