Design and Analytic Features for Reducing Biases in Skill-Building Intervention Impact Forecasts. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Alvarez-Vargas, Daniela; Wan, Sirui; Fuchs, Lynn S.; Klein, Alice; Bailey, Drew H.
• Is Part Of: Journal of research on educational effectiveness. Volume 16:Issue 2(2023)
• Page Start: 271
• Page End: 299
• Publication Date: 2023-04-03
• Subjects: Prediction -- forecasting -- non-experimental -- intervention -- evaluation; Education -- Research -- Periodicals; Education -- Evaluation -- Periodicals; Education -- Methodology -- Periodicals; 371.422
• DOI: 10.1080/19345747.2022.2093298
• Languages: English
• ISSNs: 1934-5747

Abstract

Abstract: Despite policy relevance, longer-term evaluations of educational interventions are relatively rare. A common approach to this problem has been to rely on longitudinal research to determine targets for intervention by looking at the correlation between children's early skills (e.g., preschool numeracy) and medium-term outcomes (e.g., first-grade math achievement). However, this approach has sometimes over—or under—predicted the long-term effects (e.g., 5th-grade math achievement) of successfully improving early math skills. Using a within-study comparison design, we assess various approaches to forecasting medium-term impacts of early math skill-building interventions. The most accurate forecasts were obtained when including comprehensive baseline controls and using a combination of conceptually proximal and distal short-term outcomes (in the nonexperimental longitudinal data). Researchers can use our approach to establish a set of designs and analyses to predict the impacts of their interventions up to 2 years post-treatment. The approach can also be applied to power analyses, model checking, and theory revisions to understand mechanisms contributing to medium-term outcomes.