Suchergebnisse

A review of the book Methods in Educational Research: From Theory to Practice by Maguerite G. Lodico, Dean T. Spaulding, and Katherine H. Voegtle published in 2006 by Jossey-Bass.

Objective: Over the past two decades, the lack of reliable empirical evidence concerning the effectiveness of educational interventions has motivated a new wave of research in education in sub-Saharan Africa (and across most of the world) that focuses on impact evaluation through rigorous research designs such as experiments. Often these experiments draw on the random assignment of entire clusters, such as schools, to accommodate the multilevel structure of schooling and the theory of action underlying many school-based interventions. Planning effective and efficient school randomized studies, however, requires plausible values of the intraclass correlation coefficient (ICC) and the variance explained by covariates during the design stage. The purpose of this study was to improve the planning of two-level school-randomized studies in sub-Saharan Africa by providing empirical estimates of the ICC and the variance explained by covariates for education outcomes in 15 countries. Method: Our investigation drew on large-scale representative samples of sixth-grade students in 15 countries in sub-Saharan Africa and includes over 60,000 students across 2,500 schools. We examined two core education outcomes: standardized achievement in reading and mathematics. We estimated a series of two-level hierarchical linear models with students nested within schools to inform the design of two-level school-randomized trials. Results: The analyses suggested that outcomes were substantially clustered within schools but that the magnitude of the clustering varied considerably across countries. Similarly, the results indicated that covariance adjustment generally reduced clustering but that the prognostic value of such adjustment varied across countries.

Background: Prior research has focused primarily on empirically estimating design parameters for cluster-randomized trials (CRTs) of mathematics and reading achievement. Little is known about how design parameters compare across other educational outcomes. Objectives: This article presents empirical estimates of design parameters that can be used to appropriately power CRTs in science education and compares them to estimates using mathematics and reading. Research Design: Estimates of intraclass correlations (ICCs) are computed for unconditional two-level (students in schools) and three-level (students in schools in districts) hierarchical linear models of science achievement. Relevant student- and school-level pretest and demographic covariates are then considered, and estimates of variance explained are computed. Subjects: Five consecutive years of Texas student-level data for Grades 5, 8, 10, and 11. Measures: Science, mathematics, and reading achievement raw scores as measured by the Texas Assessment of Knowledge and Skills. Results: Findings show that ICCs in science range from .172 to .196 across grades and are generally higher than comparable statistics in mathematics, .163–.172, and reading, .099–.156. When available, a 1-year lagged student-level science pretest explains the most variability in the outcome. The 1-year lagged school-level science pretest is the best alternative in the absence of a 1-year lagged student-level science pretest. Conclusion: Science educational researchers should utilize design parameters derived from science achievement outcomes.

The purpose of this study was to examine the role of school background and school process in closing achievement gaps between White and non-White students in science. To answer the research questions, a series of two-level hierarchical linear models (HLM) was performed on the fourth-grade U.S. portion of the 2015 Trends in International Mathematics and Science Study (TIMSS) data. Results indicate that (a) the science achievement gap between White and non-White students is 0.21 standard deviation, holding student and school background constant; (b) the science achievement gap varies across schools; (c) none of the school background variables are associated with the achievement gap in a school; and (d) school emphasis on student academic learning is not only associated with higher school-level science achievement, but also with a narrower science achievement gap between White and non-White students. However, teacher collaboration is not associated with school-level science achievement but is associated with a larger achievement gap. Implications, limitations, and recommendations for further research are discussed.