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Perhaps most pervasive in the social science literature is the implicit belief held by many that methods such as regression and analysis of covariance allow one to “control” variables that would otherwise not be controllable in the nonexperimental design. As is emphasized throughout this book, statistical methods, whatever the kind, do not provide methods of controlling variables, or “holding variables constant” as it were. Not in the real way. To get these kinds of effects, you usually need a strong and rigorous bullet‐proof experimental design.

It is true, however, that statistical methods do afford a method, in some sense, for presuming (or guessing) what might have been had controls been put into place. For instance, if we analyze the correlation between weight and height, it may make sense to hold a factor such as age “constant.” That is, we may wish to partial out age. However, partialling out the variability due to age in the bivariate correlation is not equivalent to actually controlling for age. The truth of the matter is that our statistical control is telling us nothing about what would actually be the case had we been able to truly control age, or any other factor. As will be elaborated on in Chapter 8 on multiple regression, statistical control is not a sufficient “proxy” whatsoever for experimental control. Students and researchers must keep this distinction in mind before they throw variables into a statistical model and employ words like “control” (or other power and action words) when interpreting effects. If you want to truly control variables, to actually hold them constant, you usually have to do experiments. Estimating parameters in a statistical model, confident that you have “controlled” for covariates, is simply not enough.