The uncertainty that researchers face in specifying their
estimation model threatens the validity of their inferences. In
regression analyses of observational data, the 'true model' remains
unknown, and researchers face a choice between plausible
alternative specifications. Robustness testing allows researchers
to explore the stability of their main estimates to plausible
variations in model specifications. This highly accessible book
presents the logic of robustness testing, provides an operational
definition of robustness that can be applied in all quantitative
research, and introduces readers to diverse types of robustness
tests. Focusing on each dimension of model uncertainty in separate
chapters, the authors provide a systematic overview of existing
tests and develop many new ones. Whether it be uncertainty about
the population or sample, measurement, the set of explanatory
variables and their functional form, causal or temporal
heterogeneity, or effect dynamics or spatial dependence, this book
provides guidance and offers tests that researchers from across the
social sciences can employ in their own research.
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