University of Virginia, Curry School of Education, Charlottesville, VA 22903-2495, USA.
J Sch Psychol. 2010 Feb;48(1):85-112. doi: 10.1016/j.jsp.2009.09.002.
Collecting data from students within classrooms or schools, and collecting data from students on multiple occasions over time, are two common sampling methods used in educational research that often require multilevel modeling (MLM) data analysis techniques to avoid Type-1 errors. The purpose of this article is to clarify the seven major steps involved in a multilevel analysis: (1) clarifying the research question, (2) choosing the appropriate parameter estimator, (3) assessing the need for MLM, (4) building the level-1 model, (5) building the level-2 model, (6) multilevel effect size reporting, and (7) likelihood ratio model testing. The seven steps are illustrated with both a cross-sectional and a longitudinal MLM example from the National Educational Longitudinal Study (NELS) dataset. The goal of this article is to assist applied researchers in conducting and interpreting multilevel analyses and to offer recommendations to guide the reporting of MLM analysis results.
在教育研究中，常用的两种抽样方法是在课堂或学校内收集学生数据，以及随着时间的推移多次从学生那里收集数据，这两种方法通常需要使用多层次建模 (MLM) 数据分析技术来避免第一类错误。本文的目的是澄清多层次分析涉及的七个主要步骤：(1) 阐明研究问题，(2) 选择适当的参数估计器，(3) 评估是否需要进行 MLM，(4) 构建一级模型，(5) 构建二级模型，(6) 多层次效应量报告，以及 (7) 似然比模型检验。这七个步骤结合来自国家教育纵向研究 (NELS) 数据集的横断面和纵向 MLM 示例进行了说明。本文的目的是帮助应用研究人员进行和解释多层次分析，并提供建议以指导 MLM 分析结果的报告。
