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ERIC Number: EJ1132363
Record Type: Journal
Publication Date: 2017
Pages: 16
Abstractor: As Provided
ISSN: ISSN-0895-7347
A Bayesian Hierarchical Selection Model for Academic Growth with Missing Data
Allen, Jeff
Applied Measurement in Education, v30 n2 p147-162 2017
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Grade 9; Junior High Schools; Middle Schools; Secondary Education; High Schools; Grade 10; Grade 11
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A