Design and Analytic Features for Reducing Biases in Skill-Building Intervention Impact Forecasts

Autor/inn/en	Alvarez-Vargas, Daniela; Wan, Sirui; Fuchs, Lynn S.; Klein, Alice; Bailey, Drew H.
Titel	Design and Analytic Features for Reducing Biases in Skill-Building Intervention Impact Forecasts
Quelle	In: Journal of Research on Educational Effectiveness, 16 (2023) 2, S.271-299 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Alvarez-Vargas, Daniela) ORCID (Fuchs, Lynn S.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2022.2093298
Schlagwörter	Bias; Skill Development; Intervention; Program Evaluation; Prediction; Mathematics Skills; Regression (Statistics); Program Effectiveness; Mathematics Achievement; Outcomes of Education; Grade 1; Elementary School Students; Achievement Tests; Wide Range Achievement Test + Suchen Sie Ihr Suchwort? Kompetenzentwicklung; Qualifikationsentwicklung; Programme evaluation; Programmevaluation; Vorhersage; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Regression; Regressionsanalyse; Mathmatics sikills; Mathematical ability; Lernleistung; Schulerfolg; School year 01; 1. Schuljahr; Schuljahr 01; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01