Shadish W.R., Cook T.D., Campbell D.T. Experimental and Quasi-Experimental Designs for Generalized Causal Inference

Boston: Houghton Mifflin Company, 2002. — 643 p.This is a book for those who have already decided that identifying a dependable relationship between a cause and its effects is a high priority and who wish to consider experimental methods for doing so. Such causal relationships are of great importance in human affairs. The rewards associated with being correct in identifying causal relationships can be high, an the costs of misidentification can be tremendous. This book has two major purposes: to describe ways in which testing causal propositions can be improved in specific research projects, and to describe ways to improve generalizations about causal propositions.
This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation:
- Theoretical matters: Experimentation, causation, and validity
- Quasi-experimental design: Regression discontinuity designs, interrupted time series designs, quasi-experimental designs that use both pretests and control groups, and other designs
- Randomized experiments: Logic and design issues, and practical problems involving ethics, recruitment, assignment, treatment implementation, and attrition
- Generalized causal inference: A grounded theory of generalized causal inference, along with methods for implementing that theory in single and multiple studiesExperiments and generalized causal inference
Statistical conclusion validity and internal validity
Construct validity and external validity
Quasi-experimental designs that either lack a control group or lack pretest observations on the outcome
Quasi-experimental designs that use both control groups and pretests
Quasi-experiments: interrupted time-series designs
Regression discontinuity designs
Randomized experiments: rationale, designs, and conditions conducive to doing them
Practical problems 1: Ethics, participant recruitment, and random assignment
Practical problems 2: Treatment implementation and attrition
Generalized causal inference: a grounded theory
Generalized causal inference: methods for single studies
Generalized causal inference: methods for multiple studies
A critical assessment of our assumptions.