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Showing 1 to 15 of 16 results Save | Export
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Peter Z. Schochet – Journal of Educational and Behavioral Statistics, 2025
Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
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Bixi Zhang; Wolfgang Wiedermann – Society for Research on Educational Effectiveness, 2022
Background: Studying causal effects is an important aim in education. Causal relationships indicate how well implements (e.g., interventions) work for the target subjects. A good strategy to get the inference in such relationships is to conduct randomized experiments. However, random assignment is limited in education research, even is discouraged…
Descriptors: Statistical Analysis, Causal Models, Algorithms, Simulation
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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
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Peter Schochet – Society for Research on Educational Effectiveness, 2024
Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
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Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying…
Descriptors: Structural Equation Models, Statistical Analysis, Educational Research, Causal Models
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Singer, Judith D. – Journal of Research on Educational Effectiveness, 2019
The arc of quantitative educational research should not be etched in stone but should adapt and change over time. In this article, I argue that it is time for a reshaping by offering my personal view of the past, present and future of our field. Educational research--and research in the social and life sciences--is at a crossroads. There are many…
Descriptors: Educational Research, Research Methodology, Longitudinal Studies, Evaluation
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Chan, Wendy – Journal of Research on Educational Effectiveness, 2017
Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative…
Descriptors: Generalization, Inferences, Probability, Educational Research
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Dogan, Murat; Hasanoglu, Gülcihan – Educational Research and Reviews, 2016
Memory plays a profound role in explaining language development, academic learning, and learning disabilities. Even though there is a large body of research on language development, literacy skills, other academic skills, and intellectual characteristics of children with hearing loss, there is no holistic study on their memory processes.…
Descriptors: Content Analysis, Memory, Hearing Impairments, Causal Models
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Frank, Kenneth A.; Maroulis, Spiro J.; Duong, Minh Q.; Kelcey, Benjamin M. – Educational Evaluation and Policy Analysis, 2013
We contribute to debate about causal inferences in educational research in two ways. First, we quantify how much bias there must be in an estimate to invalidate an inference. Second, we utilize Rubin's causal model to interpret the bias necessary to invalidate an inference in terms of sample replacement. We apply our analysis to an inference…
Descriptors: Causal Models, Inferences, Research Methodology, Robustness (Statistics)
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Taylor, Joseph; Kowalski, Susan; Stuhlsatz, Molly; Wilson, Christopher; Spybrook, Jessaca – Society for Research on Educational Effectiveness, 2013
The purpose of this paper is to use both conceptual and statistical approaches to explore publication bias in recent causal effects studies in science education, and to draw from this exploration implications for researchers, journal reviewers, and journal editors. This paper fills a void in the "science education" literature as no…
Descriptors: Science Education, Influences, Bias, Statistical Analysis
Porter, Stephen R. – Online Submission, 2012
Selection bias is problematic when evaluating the effects of postsecondary interventions on college students, and can lead to biased estimates of program effects. While instrumental variables can be used to account for endogeneity due to self-selection, current practice requires that all five assumptions of instrumental variables be met in order…
Descriptors: Statistical Bias, College Students, Educational Research, Statistical Analysis
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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
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Stuart, Elizabeth A. – Educational Researcher, 2007
Education researchers, practitioners, and policymakers alike are committed to identifying interventions that teach students more effectively. Increased emphasis on evaluation and accountability has increased desire for sound evaluations of these interventions; and at the same time, school-level data have become increasingly available. This article…
Descriptors: Research Methodology, Computation, Causal Models, Intervention
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Wessels, Holger; Lamb, Michael E.; Hwang, Carl-Philip – European Journal of Psychology of Education, 1996
Illustrates problems facing researchers trying to demonstrate causal relationships between types of nonparental care and differences between groups of Swedish children. Argues that efforts must be made to validate and interpret differences that are found. Indicates ways to avoid misinterpretation of differences that are attributable to…
Descriptors: Causal Models, Child Development, Day Care, Educational Assessment
Weerts, David J. – 1999
This study sought to identify factors that explained variations in state support for higher education during the 1990s, particularly in view of declining federal support and greater pressure on states to fund other programs. The literature points to a complex array of factors that shape state budgets for colleges and universities. These include:…
Descriptors: Causal Models, Educational Finance, Educational Research, Federal Aid
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