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McNeish, Daniel M.; Stapleton, Laura M. – Educational Psychology Review, 2016
Multilevel models are an increasingly popular method to analyze data that originate from a clustered or hierarchical structure. To effectively utilize multilevel models, one must have an adequately large number of clusters; otherwise, some model parameters will be estimated with bias. The goals for this paper are to (1) raise awareness of the…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Sample Size, Effect Size
Drechsler, Jörg – Journal of Educational and Behavioral Statistics, 2015
Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model should be preferred from a theoretical point of view if…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Educational Research, Statistical Bias
Rhoads, Christopher – Journal of Research on Educational Effectiveness, 2016
Experimental evaluations that involve the educational system usually involve a hierarchical structure (students are nested within classrooms that are nested within schools, etc.). Concerns about contamination, where research subjects receive certain features of an intervention intended for subjects in a different experimental group, have often led…
Descriptors: Educational Experiments, Error of Measurement, Research Design, Statistical Analysis
Westine, Carl D. – Society for Research on Educational Effectiveness, 2015
A cluster-randomized trial (CRT) relies on random assignment of intact clusters to treatment conditions, such as classrooms or schools (Raudenbush & Bryk, 2002). One specific type of CRT, a multi-site CRT (MSCRT), is commonly employed in educational research and evaluation studies (Spybrook & Raudenbush, 2009; Spybrook, 2014; Bloom,…
Descriptors: Correlation, Randomized Controlled Trials, Science Achievement, Cluster Grouping
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Aber, J. Lawrence; Torrente, Catalina; Starkey, Leighann; Johnston, Brian; Seidman, Edward; Halpin, Peter; Shivshanker, Anjuli; Weisenhorn, Nina; Annan, Jeannie; Wolf, Sharon – Journal of Research on Educational Effectiveness, 2017
This article examines the effects of one year of exposure to "Learning to Read in a Healing Classroom" (LRHC) on the reading and math skills of second- to fourth-grade children in the low-income and conflict-affected Democratic Republic of the Congo. LRHC consists of two primary components: teacher resource materials that infuse…
Descriptors: Grade 2, Grade 3, Grade 4, Elementary School Students
Evan, Aimee J.; Burden, Frances F.; Gheen, Margaret H.; Smerdon, Becky A. – Career and Technical Education Research, 2013
Career academies have been effective in reducing the high school dropout rates and increasing academic course taking and course credit accumulation among students (Kemple & Willner, 2008; Kemple & Snipes, 2000). However, not all students have access to career academy programs as they are not universally implemented across the state of…
Descriptors: High School Students, Career Academies, Access to Education, Geographic Location