|Title:||Attrition Benchmarks Across Students and School Contexts: Evidence from Student Mobility in National Longitudinal Survey Data|
|Principal Investigator:||Rickles, Jordan||Awardee:||American Institutes for Research (AIR)|
|Program:||Statistical and Research Methodology in Education–Early Career [Program Details]|
|Award Period:||3 years (7/1/2015–6/30/2018)||Award Amount:||$199,980|
|Goal:||Methodological Innovation||Award Number:||R305D150026|
Co-Principal Investigator: Kristina Zeiser
The purpose of this research is to provide empirical benchmarks for the amount of attrition that can arise from natural rates of student mobility. Student-level, school-based longitudinal evaluations of education practices and interventions often encounter participant attrition when students move away from, or drop out of, study schools. In some cases, this mobility-induced attrition simply decreases sample size, weakening the study's power to detect a treatment effect. Attrition can also introduce bias into an otherwise well-designed study, thereby posing a threat to internal and external validity.
In order to construct a series of mobility-based attrition benchmarks for different student populations and school settings, the researchers will utilize four nationally representative longitudinal surveys. The research team will review attrition levels reported in recent randomized controlled trials in order to provide context for the benchmarks derived from the national datasets. The team will also conduct a simulation study to investigate the quality of the results from typical approaches for estimating treatment effects when attrition is an issue (e.g., weighting and multiple imputation). Researchers will disseminate the results of the study via peer-review conference presentations and journal articles. The research team will also produce an on-line guide that applied researchers can use to gauge, potential expected attrition rates during the planning phase of a study and analytical approaches to deal with realized attrition once data collection is complete.