Quasi-Experimentation

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A01=Charles S. Reichardt
advanced causal effect estimation
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analysis of interventions
analysis of treatment effects
analyzing treatment effects
Author_Charles S. Reichardt
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behavioral science research
Category1=Non-Fiction
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clinical trials
COP=United States
counterfactual framework
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educational researchers
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estimating treatment effects
estimation
evaluating interventions
evaluation
field methods analysis
graduate classes
graduate courses
graduate students
internal validity
internal validity threats
interrupted time series design
Language_English
mediation moderation
methodology
noncompliance adjustment
nonequivalent group design
outcome measures
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pattern matching methods
pretest-posttest design
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propensity score analysis
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psychology researchers
quantitative data analysis
quasi-causal
quasi-experimental design
quasi-experiments
randomized experiments
real-world studies
regression
regression discontinuity design
research designs
research methods
SN=Methodology in the Social Sciences
social research
social researchers
softlaunch
statistical models
statistics
user-friendly guides

Product details

  • ISBN 9781462540259
  • Weight: 900g
  • Dimensions: 178 x 254mm
  • Publication Date: 28 Aug 2019
  • Publisher: Guilford Publications
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.

Charles S. Reichardt, PhD, is Professor of Psychology at the University of Denver. He is an elected fellow of the American Psychological Society, an elected member of the Society of Multivariate Experimental Psychology, and a recipient of the Robert Perloff President’s Prize from the Evaluation Research Society and the Jeffrey S. Tanaka Award from the Society of Multivariate Experimental Psychology. Dr. Reichardt’s research focuses on quasi-experimentation.

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