Quasi‐experimental Designs

Abstract

Quasi‐experiments are used to test hypotheses about the effects of manipulable treatments but lack the process of random assignment that occurs with true experiments. Consequently, researchers must be particularly concerned with reducing threats to the validity of causal inferences. Quasi‐experiments can be thought of as a collection of design elements aimed at reducing threats to validity. A number of threats to validity and basic quasi‐experimental designs are discussed.

Learning Compositional Simulation Models

“…(1) Literature review -We reviewed key texts on quasi-experimental design (Campbell & Stanley 1963;Cook & Campbell 1979;Shadish, Cook & Campbell 2002), major journal articles, and websites devoted to methods for quasi-experimental design. This work required substantial translation between concepts common to machine learning and those common in social science and experimental design.…”

“…These methods are routinely used to support causal inferences in medicine, economics, and social science. They can be grouped under the rubric "quasi-experimental design" (QED), and they attempt to exploit inherent characteristics of observational data sets that partially emulate the control and randomization possible in an experimental setting (Campbell, Stanley & Gage 1963;Cook & Campbell 1979;Shadish, Cook & Campbell 2002).…”

Evaluation at Sunset: Considerations When Evaluating a Program as It Concludes

“…Many sources of existing data can support a sunset evaluation, and some can even allow for the use of a number of traditional evaluation designs (Shadish, Cook, & Campbell, 2002). For example, data may be available that allow for pre-to post-program tests of participant improvement.…”

Full Issue

“…Many sources of existing data can support a sunset evaluation, and some can even allow for the use of a number of traditional evaluation designs (Shadish, Cook, & Campbell, 2002 (Mueller & Gaus, 2015).…”