Thesis
Predicting high school graduation rate using clustered multilevel modeling
University of Iowa
Master of Arts (MA), University of Iowa
Autumn 2025
DOI: 10.25820/etd.008206
Abstract
The present research investigated the predictive variables at level-1 and level-2 that impact the prediction of high school graduation rate—the outcome variable—in the U.S. state of Tennessee. Multilevel models were fitted to 241 schools nested in 81 school districts to examine within-district school-level effects of student cohort population and student absenteeism, as well as contextual-district effects of staff count and district classification (urban or rural). Results show that within-district student cohort population and student absenteeism and contextual-district staff count, and district classification are good predictive variables of high school students' graduation rate.
Details
- Title: Subtitle
- Predicting high school graduation rate using clustered multilevel modeling
- Creators
- Eric Antwi Akuoko
- Contributors
- Lesa Hoffman (Advisor)Jonathan Templin (Committee Member)Gavin Fulmer (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Arts (MA), University of Iowa
- Degree in
- Psychological and Quantitative Foundations (Educational Measurement and Statistics)
- Date degree season
- Autumn 2025
- DOI
- 10.25820/etd.008206
- Publisher
- University of Iowa
- Number of pages
- vii, 25 pages
- Copyright
- Copyright 2025 Eric Antwi Akuoko
- Language
- English
- Date submitted
- 08/03/2025
- Description illustrations
- tables
- Description bibliographic
- Includes bibliographical references (pages 20-25).
- Public Abstract (ETD)
- The study investigated the variables that predict high school graduation in the state of Tennessee in the U.S., using school-level and district-level characteristics. Both school-level and district-level data were collected, allowing for the school-level data to be nested into district-level data providing affordance to model variable effects at different levels on graduation rate. A multilevel model was fitted to 241 schools nested in 81 districts, examining the predictive role of school level characteristics student cohort population and student absenteeism and district-level characteristics staff count and district classification (urban or rural). Results show that both school-level characteristics (cohort population and student absenteeism) and district level characteristic (district staff count) are good predictors of high school students rate of graduation in the state of Tennessee.
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9985135249102771
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