Leading With Data· a Bicycle Guide

Partial book profile

Experimental Quasiexperimental Designs Shadish

A comprehensive guide to designing and interpreting experimental and quasi-experimental studies to draw valid inferences about cause, effect, and their generalization to broader populations, settings, treatments, and outcomes.

The full book profile is not available yet.

Get the book →

What it’s about

Building on the classic works of Campbell and Stanley (1963) and Cook and Campbell (1979), this book is the definitive resource for researchers, evaluators, and students who need to establish credible cause-and-effect relationships. It provides a sophisticated yet practical framework centered on a four-part validity typology—statistical conclusion, internal, construct, and external validity—and a systematic process of identifying and ruling out plausible threats to each. The authors offer a rich toolkit of design elements for constructing strong studies, especially quasi-experiments for field settings where randomization is not feasible. Going beyond its predecessors, the book presents a novel, grounded theory of causal generalization, offering principles and methods for extending findings beyond the specific context of a single study. For anyone serious about evidence-based causal claims in the social, behavioral, health, or policy sciences, this text is an indispensable guide to methodological rigor and thoughtful inference.

The through-line

Who it’s for
A social, behavioral, health, or policy researcher, evaluator, or graduate student who wants to conduct studies that produce credible, defensible evidence about whether their interventions, programs, or policies cause desired outcomes.
The problem
It is difficult to design studies that convincingly demonstrate a causal relationship, especially in complex field settings where full control is impossible and random assignment is often impractical or unethical. They feel uncertain about their conclusions, frustrated by ambiguous findings, and worried that their hard work will be dismissed as methodologically weak and its conclusions ignored.
The plan
  1. Master the four types of validity (statistical conclusion, internal, construct, external) and the logic of identifying and ruling out specific threats to your causal inferences.
  2. Learn a flexible toolkit of design elements (e.g., control groups, pretests, time-series) to construct strong quasi-experiments tailored to your specific research context.
  3. Understand the theory and practice of implementing randomized experiments, including how to handle common problems like attrition and failed treatment delivery.
  4. Apply a powerful, grounded theory of generalization to extend your causal findings to other people, places, times, and treatments.
The payoff
Designing and conducting studies that yield clear, credible, and defensible causal conclusions. · Feeling confident in interpreting and communicating research findings to both scientific and lay audiences. · Contributing to a cumulative body of knowledge that informs evidence-based policy and practice.

See our guide

Related profiles we’ve built

Additional reading

  • Experimental and Quasi-Experimental Designs for Research · Campbell, D. T., & Stanley, J. C. (1963)

    This is the foundational work that established the validity typology, the logic of threats to validity, and the distinction between experimental and quasi-experimental designs that this book builds upon.

  • Quasi-Experimentation: Design and Analysis Issues for Field Settings · Cook, T. D., & Campbell, D. T. (1979)

    This is the direct predecessor to the current book, which significantly expanded the 1963 work, particularly by elaborating the validity typology into four types and providing more detail on time-series and regression discontinuity designs.

  • Designing Evaluations of Educational and Social Programs · Cronbach, L. J. (1982)

    Presents a major alternative perspective on validity and generalization that heavily influenced this book's expanded treatment of construct and external validity.

  • The Design of Experiments · Fisher, R. A. (1935)

    The classic text that established the statistical rationale for randomized experiments, which is the foundation for the discussions in Chapters 8-10.

  • Observational Studies · Rosenbaum, P. R. (1995)

    Represents the modern statistical thinking on causal inference in nonrandomized studies, including concepts like propensity scores and sensitivity analyses that are discussed as important developments for strengthening quasi-experiments.

Featured in these guides

Co-cited books