International Journal of Discoveries and Innovations in Applied Sciences
https://openaccessjournals.eu/index.php/ijdias
<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>en-US[email protected] (Rosa Batista)[email protected] (Editorial Board)Tue, 14 May 2024 15:01:44 +0000OJS 3.3.0.6http://blogs.law.harvard.edu/tech/rss60Comparing Estimation Methods for Multilevel Regression Parameters
https://openaccessjournals.eu/index.php/ijdias/article/view/2627
<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>Kareem Khalaf Aazer
Copyright (c) 2024 Kareem Khalaf Aazer
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https://openaccessjournals.eu/index.php/ijdias/article/view/2627Tue, 14 May 2024 00:00:00 +0000Supporting Children’s Language Learning
https://openaccessjournals.eu/index.php/ijdias/article/view/2640
<p>This study focused on the importance of supporting children's learning of English as a second language in early childhood using practical strategies. The research question focuses on improving children's language skills in order to support their overall development. The aim is to investigate the impact of language development programs on young children's language skills. The methodology involved a six-week intervention program with of 50 preschool children, including storytelling, word-building activities, and music-based exercises with parent participation. The results showed significant improvements in participants' vocabulary, language skills and fluency. Parental involvement plays an important role in supporting children's language development outside of school. The results suggest that a comprehensive intervention program that includes a variety of activities and parental involvement can have a positive impact on children's language learning. Future research could investigate the long-term effects of the intervention.</p>Gulnora Abdullaeva Gaybulloevna
Copyright (c) 2024
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https://openaccessjournals.eu/index.php/ijdias/article/view/2640Sun, 26 May 2024 00:00:00 +0000