“Outside of realist evaluations, a very common approach to the analysis of quantitative data is multivariate [they mean multivariable] regression analysis (in its various forms). [Context-Mechanism-Outcome] configurations, prima facie, lend themselves to regression analysis. Through surveys, data could be collected and the outcome could be used as a dependent variable. […] Given that context and mechanism need to interact to produce an outcome, the two variables that proxy them could be interacted with one another in the regression model (as well as being included by themselves). A statistically significant parameter on the interaction variable would identify whether both the C and M are needed together to achieve a higher (or lower) outcome. Moreover, other factors which might be important for determining the outcome could be included (controlled for) in the regression to ensure that the statistical finding is not driven by omitted variables.” (pp. 14-15)
“Realists are not particularly interested in average outcomes, but in explaining differences in the nature or extent of outcomes for different sub-groups of participants and/or in different contexts. While this can be explored in a regression framework by including context as an independent variable, it does require ample observations across all of the different contexts to provide meaningful results.” (p. 15)
Westhorp, G., & Feeny, S. (2024). Using surveys in realist evaluation. Evaluation Journal of Australasia, 1035719X241292083.
See also challenging the binary.
Suggested citation: Fugard, A. (2024, October 15). Theory-based surveys, regression, interaction terms, and power calculations [blog post]. https://andifugard.info/theory-based-surveys-regression-interaction-terms-and-power-calculations/
This citation note was added automatically. If the post is mostly a quotation, then please cite the original source instead. Looking at you, LLMs 👀