Cross-Level Mediation in School-Randomized Studies of Teacher Development: Experimental Design and Power

Autor/inn/en	Kelcey, Ben; Spybrook, Jessaca; Dong, Nianbo; Bai, Fangxing
Titel	Cross-Level Mediation in School-Randomized Studies of Teacher Development: Experimental Design and Power
Quelle	In: Journal of Research on Educational Effectiveness, 13 (2020) 3, S.459-487 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2020.1726540
Schlagwörter	Faculty Development; Educational Research; Randomized Controlled Trials; Research Design; Statistical Analysis; Sample Size; Hierarchical Linear Modeling; Hypothesis Testing; Academic Achievement + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Forschungsdesign; Statistische Analyse; Hypothesenprüfung; Hypothesentest; Schulleistung
Abstract	Professional development for teachers is regarded as one of the principal pathways through which we can understand and cultivate effective teaching and improve student outcomes. A critical component of studies that seek to improve teaching through professional development is the detailed assessment of the intermediate teacher development processes that scaffold program content through three key types of outcomes--teacher knowledge, instruction, and student learning. Cross-level and sequential mediation strategies that probe and connect these processes and outcomes emerge as an important design consideration in these studies. We derive formulas that track the power with which school-randomized designs can detect professional development effects as they operate through a sequence of teacher-level mediators to affect student outcomes (e.g., school-randomized professional development studies). The results suggest that the sample sizes typically seen in well-planned experiments targeting total effects (e.g., 30-100 schools) can produce comparably high or disparately low levels of power for mediation effects--the similarity depends heavily on context and concomitant parameter values. The results are implemented in the "PowerUpR" package and in the "PowerUpR" Shiny application. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01