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Journal of Research on Educational Effectiveness Volume 8, 2015 - Issue 4
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METHODOLOGICAL STUDIES

Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Peter M. SteinerUniversity of Wisconsin–Madison, Madison, Wisconsin, USACorrespondence[email protected]
,
Thomas D. CookNorthwestern University, Evanston, Illinois, USA
,
Wei LiNorthwestern University, Evanston, Illinois, USA
&
M. H. ClarkUniversity of Central Florida, Orlando, Florida, USA
Pages 552-576 | Published online: 02 Jul 2015

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Lee LeBoeuf, Jacob Goldstein-Greenwood & Angeline S. Lillard. (2023) Multilevel Modeling Resolves Ambiguities in Analyses of Discipline Disproportionality: A Demonstration Comparing Title 1 Montessori and Non-Montessori Schools. Journal of Research on Educational Effectiveness 0:0, pages 1-26.
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Yongnam Kim & Peter Steiner. (2016) Quasi-Experimental Designs for Causal Inference. Educational Psychologist 51:3-4, pages 395-405.
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Articles from other publishers (14)

Lauren Eutsler, Meena Naik, Scott Peecksen & Regina Branton. (2023) Impact of inquiry portfolios within a service-learning literacy field experience on preservice teachers’ knowledge growth and GPA. Teaching and Teacher Education 124, pages 104032.
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Lee LeBoeuf, Jacob Goldstein-Greenwood & Angeline S. Lillard. (2023) Rates of chronic absenteeism in Montessori and non-Montessori Title 1 schools. Frontiers in Education 8.
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Seth Brown, Mengli Song, Thomas D. Cook & Michael S. Garet. (2022) Combining a Local Comparison Group, a Pretest Measure, and Rich Covariates: How Well Do They Collectively Reduce Bias in Nonequivalent Comparison Group Designs?. American Educational Research Journal 60:1, pages 141-182.
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Jessica Levy, Martin Brunner, Ulrich Keller & Antoine Fischbach. (2022) How sensitive are the evaluations of a school’s effectiveness to the selection of covariates in the applied value-added model?. Educational Assessment, Evaluation and Accountability 35:1, pages 129-164.
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Ben Weidmann & Luke Miratrix. (2020) LURKING INFERENTIAL MONSTERS? QUANTIFYING SELECTION BIAS IN EVALUATIONS OF SCHOOL PROGRAMS. Journal of Policy Analysis and Management 40:3, pages 964-986.
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Bryan Keller. (2019) Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests. Journal of Educational and Behavioral Statistics 45:2, pages 119-142.
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K. C. Culver & Nicholas A. Bowman. (2019) Is What Glitters Really Gold? A Quasi-Experimental Study of First-Year Seminars and College Student Success. Research in Higher Education 61:2, pages 167-196.
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Deborah K. Reed, Ariel M. Aloe, Adam J. Reeger & Jessica Sidler Folsom. (2019) Defining Summer Gain Among Elementary Students With or at Risk for Reading Disabilities. Exceptional Children 85:4, pages 413-431.
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Marie-Ann Sengewald & Steffi Pohl. (2019) Compensation and Amplification of Attenuation Bias in Causal Effect Estimates. Psychometrika 84:2, pages 589-610.
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Nicholas A. Bowman & Joshua M. Holmes. (2017) Getting off to a good start? First-year undergraduate research experiences and student outcomes. Higher Education 76:1, pages 17-33.
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Nicholas A. Bowman & KC Culver. (2017) When Do Honors Programs Make the Grade? Conditional Effects on College Satisfaction, Achievement, Retention, and Graduation. Research in Higher Education 59:3, pages 249-272.
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Vivian C. Wong, Peter M. Steiner & Kylie L. Anglin. (2018) What Can Be Learned From Empirical Evaluations of Nonexperimental Methods?. Evaluation Review 42:2, pages 147-175.
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Adam C. Sales, Ben B. Hansen & Brian Rowan. (2017) Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates. Journal of Educational and Behavioral Statistics 43:1, pages 3-31.
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Peter M. Steiner & Yongnam Kim. (2016) The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases. Journal of Causal Inference 4:2.
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