Guo Siwen, Houang Richard T, Schmidt William H
Department of Psychology, Renmin University of China, Beijing, China.
Center for the Study of Curriculum Policy, Department of Counseling, Educational Psychology & Special Education, Michigan State University, East Lansing, MI, United States.
Front Psychol. 2021 Jun 3;12:541803. doi: 10.3389/fpsyg.2021.541803. eCollection 2021.
In contextual studies, group compositions are often extracted from individual data in the sample, in order to estimate the group compositional effects [e.g., school socioeconomic status (SES) effect] controlling for interindividual differences in multilevel models. As the same variable is used at both group level and individual level, an appropriate decomposition of between and within effects is a key to providing a clearer picture of these organizational and individual processes. The current study developed a new approach with within-group finite population correction (fpc). Its performances were compared with the manifest and latent aggregation approaches in the decomposition of between and within effects. Under a moderate within-group sampling ratio, the between effect estimates from the new approach had a lesser degree of bias and higher observed coverage rates compared with those from the manifest and latent aggregation approaches. A real data application was also used to illustrate the three analysis approaches.
在情境研究中，群组构成通常从样本中的个体数据中提取，以便在多层模型中控制个体间差异时估计群组构成效应[例如，学校社会经济地位（SES）效应]。由于同一变量在群组层面和个体层面都被使用，对组间效应和组内效应进行适当分解是更清晰地呈现这些组织和个体过程的关键。本研究开发了一种新的组内有限总体校正（fpc）方法。在组间效应和组内效应的分解中，将其性能与显性和隐性聚合方法进行了比较。在适度的组内抽样率下，与显性和隐性聚合方法相比，新方法的组间效应估计偏差较小，观察覆盖率较高。还通过一个实际数据应用来说明这三种分析方法。
