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Structural Equation Modeling: A Multidisciplinary Journal Volume 31, 2024 - Issue 1
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Book Review

Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)

By Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang. New York, NY: The Guilford Press, (2023), 416 pp. $93.00 (Hardback), ISBN: 9781462552931. $62.00 (Paperback), ISBN: 9781462552924. $62.00 (PDF).

Aszani AszaniDepartment of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Jalan Sekip Utara, Bulaksumur, Yogyakarta55281, Indonesia.Correspondence[email protected]
ORCID Iconhttps://orcid.org/0009-0008-5805-258XView further author information
ORCID Icon &
Ruslan AnwarDepartment of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Jalan Sekip Utara, Bulaksumur, Yogyakarta55281, Indonesia.View further author information
Pages 199-203 | Received 12 Sep 2023, Accepted 14 Sep 2023, Published online: 09 Nov 2023

References

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  • Baranowski, M. (2022). Epistemological aspect of topic modelling in the social sciences: Latent Dirichlet Allocation. Przegląd Krytyczny, 4, 7–16. https://doi.org/10.14746/pk.2022.4.1.1
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  • Krishna, C., Kumar, D., & Kushwaha, D. S. (2023). A comprehensive survey on pandemic patient monitoring system: Enabling technologies, opportunities, and research challenges. Wireless Personal Communications, 131, 1–48. https://doi.org/10.1007/s11277-023-10535-9
  • Lipesa, B. A., Okango, E., Omolo, B. O., & Omondi, E. O. (2023). An application of a supervised machine learning model for predicting life expectancy. SN Applied Sciences, 5, 189. https://doi.org/10.1007/s42452-023-04899-8
  • Luo, G., Nazir, S., Khan, H. U., & Haq, A. (2020). Spam detection approach for secure mobile message communication using machine learning algorithms. Security and Communication Networks, 2020, 1–6. https://doi.org/10.1155/2020/8873639
  • Ma, Y. (2022). Modeling social network of professional sports athletes based on machine learning algorithms. International Transactions on Electrical Energy Systems, 2022, 1–9. https://doi.org/10.1155/2022/6283618
  • Olteanu, A., Cernian, A., & Gâgă, S.-A. (2022). Leveraging machine learning and semi-structured information to identify political views from social media posts. Applied Sciences, 12, 12962. https://doi.org/10.3390/app122412962
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
  • Suarez, A. D., Rabago, J. K. M., & Paguyo, C. G. (2022). Identifying the extent of need in the different concepts under disciplines and ideas in social sciences: A basis in developing mobile based e-learning application. Asian Research Journal of Arts & Social Sciences, 18, 135–150. https://doi.org/10.9734/arjass/2022/v18i4411
  • Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023). Data collection and quality challenges in deep learning: A data-centric AI perspective. The VLDB Journal, 32, 791–813. https://doi.org/10.1007/s00778-022-00775-9
  • Zhong, X., Gallagher, B., Liu, S., Kailkhura, B., Hiszpanski, A., & Han, T. Y.-J. (2022). Explainable machine learning in materials science. Npj Computational Materials, 8, 204. https://doi.org/10.1038/s41524-022-00884-7

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