The interaction of history and treatment deals with the extent to which a causal relationship replicates across different times. The interaction of setting and treatment describes effects that vary by setting. The interaction of selection and treatment hypothesizes that an effect size varies by population studied.

The interaction of testing and treatment suggests that an effect size varies by the conditions of measurement. Because external validity specifies the conditions under which an internally valid relationship can be reproduced, threats to external validity necessarily invoke internal validity. Interactions occur often between treatment, populations, measures and context, posing the most plausible threats to external validity (Campbell and Stanley 1966, Cook and Campbell 1979). The practical task is to assess the most plausible interactions within a given research area. The challenge is: how do we cope with this complexity? Unfortunately, the number of possible interactions is endless, posing problems to the analyst that are insuperable, at least in theory (Cook 1993). Without a sensitive assessment of such interactions, true effects can be obscured or causal claims can be overgeneralized to a wider range of people, settings, times, treatments, or outcome constructs than is warranted. This means that the strength of a causal relationship is assumed to vary with the population, setting, or time represented within any given study, and with the researcher's choices about treatments and measurement of outcomes. Leviton, in International Encyclopedia of the Social & Behavioral Sciences, 2001 1.3 The Challenge of Complex InteractionsĬausal relationships in real-world settings are complex, and statistical interactions of variables are assumed to be pervasive (e.g., Brunswik 1955, Cronbach 1982).

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