Purpose: With the rise of big data, deep learning neural networks have garnered attention from psychology researchers due to their ability to process vast amounts of data and achieve superior model fitting. We aim to explore the predictive accuracy of neural network models and linear mixed models in tracking data when subjective variables are predominant in the field of psychology. We separately analyzed the predictive accuracy of both models and conduct a comparative study to further investigate. Simultaneously, we utilized the neural network model to examine the influencing factors of problematic internet usage and its temporal changes, attempting to provide insights for early interventions in problematic internet use.
Patients and Methods: This study compared longitudinal data of junior high school students using both a linear mixed model and a neural network model to ascertain the efficacy of these two methods in processing psychological longitudinal data.
Results: The neural network model exhibited significantly smaller errors compared to the linear mixed model. Furthermore, the outcomes from the neural network model revealed that, when analyzing data from a single time point, the influences of seventh grade better predicted Problematic Internet Use in ninth grade. And when analyzing data from multiple time points, the influences of sixth, seventh, and eighth grades more accurately predicted Problematic Internet Use in ninth grade.
Conclusion: Neural network models surpass linear mixed models in precision when predicting and analyzing longitudinal data. Furthermore, the influencing factors in lower grades provide more accurate predictions of Problematic Internet Use in higher grades. The highest prediction accuracy is attained through the utilization of data from multiple time points.

Keywords: problematic internet use, junior high school students, neural network model, linear mixed model


中文翻译：

---

初中生问题性互联网使用预测：纵向研究中神经网络模型与线性混合模型的比较

目的：随着大数据的兴起，深度学习神经网络因其处理大量数据并实现卓越模型拟合的能力而引起了心理学研究人员的关注。我们的目的是探索当主观变量在心理学领域占主导地位时，神经网络模型和线性混合模型在跟踪数据中的预测准确性。我们分别分析了两种模型的预测准确性，并进行了比较研究以进一步探讨。同时，我们利用神经网络模型来研究互联网使用问题的影响因素及其时间变化，试图为互联网使用问题的早期干预提供见解。
患者和方法：本研究使用线性混合模型和神经网络模型比较了初中生的纵向数据，以确定这两种方法在处理心理纵向数据时的有效性。
结果：与线性混合模型相比，神经网络模型的误差明显更小。此外，神经网络模型的结果表明，在分析单个时间点的数据时，七年级的影响可以更好地预测九年级的有问题的互联网使用。在分析多个时间点的数据时，六年级、七年级和八年级的影响更准确地预测了九年级的有问题的互联网使用。
结论：在预测和分析纵向数据时，神经网络模型的精度优于线性混合模型。此外，低年级的影响因素可以更准确地预测高年级的有问题的互联网使用情况。通过利用多个时间点的数据可以获得最高的预测精度。

关键词:网络使用问题, 初中生, 神经网络模型, 线性混合模型