Table_1_Identifying Student Subgroups as a Function of School Level Attributes: A Multilevel Latent Class Analysis.DOCX

The purpose of the present study was to profile high school students’ achievement as a function of their demographic characteristics, parent attributes (e.g., education), and school behaviors (e.g., number of absences). Students were nested within schools in the Saudi Arabia Kingdom. Out of a large sample of 500k, participants involved 3 random samples of 2,000 students measured during the years 2016, 2017, and 2018. Randomization was conducted at the student level to ensure that all school units will be represented and at their respective frequency. Students were nested within 50 high schools. We adopted the multilevel latent profile analysis protocol put forth by Schmiege et al. (2018) and Mäkikangas et al. (2018) that account for nested data and tested latent class structure invariance over time. Results pointed to the presence of a 4-profile solution based on BIC, the Bayes factor, and several information criteria put forth by Masyn (2013). Latent profile separation was mostly guided by parents’ education and the number of student absences (being positive and negative predictors of high achievement classes, respectively). Two models tested whether the proportions of level 1 profiles to level 2 units are variable and whether level 2 profiles vary as a function of level 1 profiles. Results pointed to the presence of significant variability due to schools.

本研究旨在基于人口统计学特征、家长属性（如受教育程度）以及在校行为（如缺勤次数），剖析高中生的学业表现特征。研究对象为沙特阿拉伯王国的在校高中生，学生嵌套于学校层级之中，共涉及50所高中。原始大样本规模达50万，最终纳入的参与者为2016、2017及2018年三年间抽取的3组各2000名学生的随机样本。本次抽样采用学生层面随机化方式，以确保所有学校单元均能按其对应频次被纳入样本。本研究采用了Schmiege等人（2018）与Mäkikangas等人（2018）提出的多层潜在剖面分析（multilevel latent profile analysis）框架，该框架可处理嵌套型数据，并检验了潜在类别结构的跨时间不变性。研究结果基于贝叶斯信息准则（BIC）、贝叶斯因子（Bayes factor）以及Masyn（2013）提出的多项信息准则，最终确定最优模型为4剖面解。潜在剖面的区分主要由家长受教育程度与学生缺勤次数决定：二者分别为高学业表现类别的正向预测因子与负向预测因子。本研究设置了两个模型，分别检验两项假设：第一层（学生层）剖面占第二层（学校层）单元的比例是否存在变异，以及第二层剖面是否随第一层剖面的变化而变化。结果显示，学校层面存在显著的变异效应。