Exploring Academic Outcome, Personal Traits, and Health Behavior: Analytical Accessibility and Advantages of Large-Scale Databases

Session Type: Paper Session

Abstract

Papers in this session demonstrated the richness and possibilities of scholarly investigation allowed and encouraged by the international or national databases. These studies make inferences to different target populations (e.g., students, teachers, people with disabilities), examined a variety of outcomes (e.g., academic achievement, health behaviors, psychological traits), and utilized various statistical models (e.g., latent class analysis, multi-level models). As computation techniques have advanced, issues that used to be challenging for analysis using complex databases, such as estimate correction for multistage/multilevel sampling, missing data treatment, and joint analysis for the mixture of both latent and manifest variables, become less demanding and labor-intensive, which showcases the analytical accessibility and advantages of large scale databases.

Sub Unit

• SIG-Advanced Studies of National Databases

Chair

• Billie Gastic Rosado, New York University

Papers

• What Type of Teacher Support Do Students Receive? A Latent Class Analysis - Aidi Bian, HD School

• Health Behavior Profiles of Children Diagnosed With Autism Spectrum Disorder: A Latent Class Analysis - Jeanette Garcia, University of Central Florida; Debbie L. Hahs-Vaughn, University of Central Florida

• Developmental Trajectories of Neuroticism During Midlife: Findings From a Latent Growth Class Analysis - Fangning Wang; Jian Li, Kent State University

• A Bivariate Latent Growth Model of Neuroticism and Depression Development - Fangning Wang; Jian Li, Kent State University

• Science-Related Degree Attainment of Aspiring Scientists - Maria Adamuti-Trache, The University of Texas - Arlington; Yi Leaf Zhang, University of Texas at Arlington

Discussant

• Yihua Hong, RTI International