Estimating Student Attrition in School-Based Prevention Studies: Guidance from State Longitudinal Data in Maryland

被引:0
作者
Angela K. Henneberger
Bess A. Rose
Yi Feng
Tessa Johnson
Brennan Register
Laura M. Stapleton
Tracy Sweet
Michael E. Woolley
机构
[1] University of Maryland School of Social Work,
[2] University of Maryland College Park,undefined
来源
Prevention Science | 2023年 / 24卷
关键词
Attrition; Study design; School-based prevention; Program evaluation; Longitudinal data;
D O I
暂无
中图分类号
学科分类号
摘要
Attrition is a critical concern for evaluating the rigor of prevention studies, and the current study provides rates of attrition for subgroups of students and schools who are often sampled for prevention science. This is the first study to provide practical guidance for expected rates of attrition using population-level statewide data; findings indicated that researchers using K-12 school-based samples should plan for attrition rates as high as 27% during middle school and 54% during elementary school. However, researchers should consider the grade levels initially sampled, the length of follow-up, and the specific student characteristics and schools available for sampling. Postsecondary attrition ranged from 45% for bachelor’s degree seekers to 73% for associate degree seekers. This practical guidance can help researchers to proactively plan for attrition in the study design phase, limiting bias and increasing the validity of prevention studies.
引用
收藏
页码:1035 / 1045
页数:10
相关论文
共 19 条
  • [1] Dynarski SM(2015)The missing manual: Using National Student Clearinghouse data to track postsecondary outcomes Educational Evaluation and Policy Analysis 37 53S-79S
  • [2] Hemelt SW(2004)Alternative methods for handling attrition: An illustration using data from the fast track evaluation Evaluation Review 28 434-464
  • [3] Hyman JM(2014)Flaws in evaluations of social programs: Illustrations from randomized controlled trials Evaluation Review 38 359-387
  • [4] Foster EM(2019)Statistical analysis with linked data International Statistical Review 87 S139-S157
  • [5] Fang GY(2018)Accounting for student attrition in power calculations: Benchmarks and guidance Journal of Research on Educational Effectiveness 11 622-644
  • [6] Greenberg D(1993)Regression analysis of data files that are computer matched Survey Methodology 19 39-58
  • [7] Barnow BS(2019)Aiming further: Addressing the need for high-quality longitudinal research in education Journal of Research on Educational Effectiveness 12 648-658
  • [8] Han Y(2021)Missing, presumed different: Quantifying the risk of attrition bias in education evaluations Journal of the Royal Statistical Society: Series A (statistics in Society) 184 732-760
  • [9] Lahiri P(undefined)undefined undefined undefined undefined-undefined
  • [10] Rickles J(undefined)undefined undefined undefined undefined-undefined