A Bayesian Hierarchical Selection Model for Academic Growth With Missing Data. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Allen, Jeff
• Is Part Of: Applied measurement in education. Volume 30:Issue 2(2017)
• Page Start: 147
• Page End: 162
• Publication Date: 2017-04-03
• Subjects: Educational tests and measurements -- United States -- Periodicals; Educational tests and measurements -- Periodicals; Education -- Standards -- Periodicals; 371.26
• DOI: 10.1080/08957347.2017.1283318
• Languages: English
• ISSNs: 0895-7347

Abstract

ABSTRACT: Using a sample of schools testing annually in grades 9–11 with a vertically linked series of assessments, a latent growth curve model is used to model test scores with student intercepts and slopes nested within school. Missed assessments can occur because of student mobility, student dropout, absenteeism, and other reasons. Missing data indicators are modeled using logistic regression, with grade 9 and potentially unobserved growth scores used as covariates. Under a hierarchical selection model, estimates of school effects on academic growth and missingness are obtained. The results from the selection model are compared to a model that ignores the missing data process.