Mangino Anthony A, Finch W Holmes
Ball State University, Teachers College, Muncie, IN, USA.
Educ Psychol Meas. 2021 Dec;81(6):1118-1142. doi: 10.1177/0013164421992818. Epub 2021 Feb 16.
Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms across several varied data conditions for the purpose of prediction. Among these models, the panel neural network and Bayesian generalized mixed effects model (multilevel Bayes) consistently yielded the highest prediction accuracy in test data across nearly all data conditions.
在社会科学和自然科学的许多领域中，数据常常是在嵌套结构中获取的（例如，学校中的学生）。为了有效地分析具有这种结构的数据，经常会使用多层模型。本研究利用蒙特卡罗模拟，在几种不同的数据条件下比较几种新颖的多层分类算法，以进行预测。在这些模型中，面板神经网络和贝叶斯广义混合效应模型（多层贝叶斯）在几乎所有数据条件下的测试数据中始终产生最高的预测准确率。
