Short Comparative Interrupted Time Series Using Aggregate School-Level Data in Education Research

Autor/inn/en	Hallberg, Kelly; Williams, Ryan; Swanlund, Andrew; Eno, Jared
Titel	Short Comparative Interrupted Time Series Using Aggregate School-Level Data in Education Research
Quelle	In: Educational Researcher, 47 (2018) 5, S.295-306 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-189X
DOI	10.3102/0013189X18769302
Schlagwörter	Leitfaden; Educational Research; Research Methodology; Comparative Analysis; Time; Data Analysis; School Effectiveness; Teacher Effectiveness; Quasiexperimental Design; Program Evaluation; Grants; Educational Improvement; Ohio + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Research method; Forschungsmethode; Zeit; Auswertung; Schuleffizienz; Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; Programme evaluation; Programmevaluation; Grant; Finanzielle Beihilfe; Teaching improvement; Unterrichtsentwicklung
Abstract	Short comparative interrupted times series (CITS) designs are increasingly being used in education research to assess the effectiveness of school-level interventions. These designs can be implemented relatively inexpensively, often drawing on publicly available data on aggregate school performance. However, the validity of this approach hinges on a variety of assumptions and design decisions that are not clearly outlined in the literature. This article aims to serve as a practice guide for applied researchers when deciding how and whether to use this approach. We begin by providing an overview of the assumptions needed to estimate causal effects using school-level data, common threats to validity faced in practice and what effects can and cannot be estimated using school-level data. We then examine two analytic decisions researchers face in practice when implementing the design: correctly modeling the pretreatment functional form, which is modeling the preintervention trend, and selecting comparison cases. We then illustrate the use of this design in practice drawing on data from the implementation of the school improvement grant (SIG) program in Ohio. We conclude with advice for applied researchers implementing this design. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01