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Sarah E. Robertson; Jon A. Steingrimsson; Issa J. Dahabreh – Evaluation Review, 2024
When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…
Descriptors: Randomized Controlled Trials, Generalization, Inferences, Hierarchical Linear Modeling
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An, Weihua – Sociological Methods & Research, 2016
One interesting idea in social network analysis is the directionality test that utilizes the directions of social ties to help identify peer effects. The null hypothesis of the test is that if contextual factors are the only force that affects peer outcomes, the estimated peer effects should not differ, if the directions of social ties are…
Descriptors: Peer Relationship, Social Networks, Statistical Analysis, Network Analysis
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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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Harvill, Eleanor L.; Peck, Laura R.; Bell, Stephen H. – American Journal of Evaluation, 2013
Using exogenous characteristics to identify endogenous subgroups, the approach discussed in this method note creates symmetric subsets within treatment and control groups, allowing the analysis to take advantage of an experimental design. In order to maintain treatment--control symmetry, however, prior work has posited that it is necessary to use…
Descriptors: Experimental Groups, Control Groups, Research Design, Sampling
Karabatsos, G.; Walker, S.G. – Society for Research on Educational Effectiveness, 2010
Causal inference is central to educational research, where in data analysis the aim is to learn the causal effects of educational treatments on academic achievement, to evaluate educational policies and practice. Compared to a correlational analysis, a causal analysis enables policymakers to make more meaningful statements about the efficacy of…
Descriptors: Bayesian Statistics, Causal Models, Educational Research, Writing Instruction
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Riegg, Stephanie K. – Review of Higher Education, 2008
This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…
Descriptors: Research Methodology, Causal Models, Inferences, Test Bias