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ORIGINAL RESEARCH

Prediction of Junior High School Students’ Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study

, , , ORCID Icon, , , , & ORCID Icon show all
Pages 1191-1203 | Received 17 Jan 2024, Accepted 12 Mar 2024, Published online: 15 Mar 2024
 

Abstract

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.

Ethics Approval and Consent to Participate

The study was approved by the Institutional Review Board (or Ethics Committee) of Ethics Committee of Shandong Normal University and this study was conducted in accordance with the Declaration of Helsinki. The parents of the participants signed an informed consent form, and the participants were told that the information they provided was anonymous and confidential. We declare that participants in our research study allow us to use their data for academic research and publication.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This project is supported by the Shandong Province Natural Science Foundation (ZR2020MF158).