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

Georgios D Sideridis,Ioannis Tsaousis,Khaleel Al-Harbi

doi:10.3389/fpsyg.2021.624221

Abstract

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.

Highlights

There is a lack of consensus in the education literature regarding the definition of academic achievement, most scholars agree that academic achievement could be defined as the attained success in any educational act (Simpson and Weiner, 1989), or as the ability of an individual to reach a set goal through effort, skill or courage within the school context (Hornby, 2006)
The present study targets at identifying subgroups of high school students who display adaptive school behaviors and achievement using an optimal set of school indicators proposed in the literature and contribute information related to the consistency of these profiles over a 3-year period
The class enumeration process is described in more detail for this year as it will represent the baseline model for which invariance would be tested across subsequent years

Summary

Introduction

Academic achievement is a very important indicator of future success in the society since academically successful individuals are more likely to have better employment opportunities, to gain higher salaries, and experience higher levels of life satisfaction and better social relationships (Rumberger and Lamb, 2003; Kuncel et al, 2004; Archambault et al, 2009; Lewis et al, 2011; Flashman, 2012; Mishook et al, 2012; Kell et al, 2013; Baroody et al, 2016; Lansford et al, 2016; Liu et al, 2020). Identification of subgroups of high school students which are associated with desirable academic outcomes may inform unique pathways to academic success. Identification of these pathways is one important endeavor but investigation of the stability of these profiles is even more important as it adds generality to the emerged subgroups. The present study targets at identifying subgroups of high school students who display adaptive school behaviors and achievement using an optimal set of school indicators proposed in the literature (see section) and contribute information related to the consistency of these profiles over a 3-year period. The need to identify subgroups of students in which important school and home behaviors are combined that are predictive of academic achievement can inform intervention pathways, provide educational recommendations and can lead to educational policy changes and mandates. The present study describes the methodology and means to achieve these goals

Objectives

Results

Conclusion