预测初中生有问题的互联网使用:神经网络模型和线性混合模型在纵向研究中的比较
Mei Tian1, Qiulian Xing2, Xiao Wang2
1Library, Shandong Normal University, Jinan, People's Republic of China.
Psychology research and behavior management
|March 20, 2024
概括
与线性混合模型相比,神经网络模型对心理纵向数据提供了更高的预测准确度. 早期的年级数据,特别是来自多个时间点的数据,显著改善了以后年级问题性互联网使用的预测.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 心理学的大数据量越来越大,需要先进的分析技术.
- 深度学习神经网络在处理复杂的心理数据和改进模型匹配方面表现有前途.
- 传统的模型,如线性混合模型,可能存在主观和纵向心理变量的局限性.
研究的目的:
- 为了比较神经网络模型和线性混合模型对心理纵向数据的预测准确度.
- 研究神经网络在分析心理学研究中普遍存在的主观变量的有效性.
- 通过神经网络分析识别问题性互联网使用的关键影响因素和时间变化,以获得早期干预见解.
主要方法:
- 分析了初中学生的纵向数据.
- 在神经网络模型和线性混合模型之间进行了比较分析.
- 神经网络模型被用来检查有问题的互联网使用的预测因素和时间动态.
主要成果:
- 神经网络模型的预测误差明显低于线性混合模型.
- 对单个时间点的分析表明,七年级的数据有效地预测了九年级有问题的互联网使用.
- 对多个时间点的分析表明,六年级,七年级和八年级的数据提供了对九年级有问题的互联网使用的更准确的预测.
结论:
- 神经网络模型为心理纵向数据分析和预测提供了比线性混合模型更高的精度.
- 通过结合较早学年级的数据,提高了有问题的互联网使用的预测准确性.
- 利用来自多个时间点的纵向数据,为有问题的互联网使用提供了最高的预测准确度.
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