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Heining Cham; Hyunjung Lee; Igor Migunov – Asia Pacific Education Review, 2024
The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental…
Descriptors: Causal Models, Educational Research, Quasiexperimental Design, Research Design
Schochet, Peter Z.; Puma, Mike; Deke, John – National Center for Education Evaluation and Regional Assistance, 2014
This report summarizes the complex research literature on quantitative methods for assessing how impacts of educational interventions on instructional practices and student learning differ across students, educators, and schools. It also provides technical guidance about the use and interpretation of these methods. The research topics addressed…
Descriptors: Statistical Analysis, Evaluation Methods, Educational Research, Intervention
Harms, Kristyn; King, James; Francis, Charles – Journal of Natural Resources and Life Sciences Education, 2009
This study evaluated and described student perceptions of a course in agroecology to determine if participants experienced changed perceptions and behaviors resulting from the Agroecosystems Analysis course. A triangulation validating quantitative data mixed methods approach included a written survey comprised of both quantitative and open-ended…
Descriptors: Grounded Theory, Control Groups, Student Attitudes, Self Efficacy
Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation

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