Hi Lisa,

beyond mere predictive modeling (like regression), there is no "theory-less" procedure to incorporate controls in the causal model. Control variables are those variables that--based on your / the literature's assumptions--play a causal role in the modelled system such that omission would lead to a bias of the focal effects. This will happen if the respective control variable is embedded in a "backdoor path" that creates a non-causal, spurious covariance between IV and DV. The essential characteristic of such a path is that it starts with an arrow pointing at the IV and it ends with an arrow pointing to the DV. In the most straightforward way, this is the case for simple confounders that affect the IV as well as the DV.

This definition also saves you from the most fundamental mistakes, namely accidently controlling for mediators (for which the first characteristic fails) and colliders (for which both characteristics fail).

Long story short, you have to create a path diagram that expresses not only your beliefs about the causal interplay but also with regard to the role of the control variable. The causal estimates of the model represent this essential conditional nature (if the model with its structure of relevant variables is correct THEN the estimates represent the respective causal impact).

There is a nice browser software called dagitty.net with which you can draw path diagrams and decide which variables have to be controlled. Often, control variables themselves have some causal interconnection which implies that controlling only some of them (or even one) is sufficient.

See

Vahratian, A., Siega-Riz, A. M., Savitz, D. A., & Zhang, J. (2005). Maternal pre-pregnancy overweight and obesity and the risk of cesarean delivery in nulliparous women. Annals of Epidemiology, 15(7), 467-474. doi:10.1016/j.annepidem.2005.02.005

Vanderweele, T. J. (2019). Principles of confounder selection. European Journal of Epidemiology, 3, 211-219. doi:10.1007/s10654-019-00494-6

Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research. (pp. 245-273). Dordrecht Heidelberg New York London: Springer.

Best,

Holger

Mostly the socio-demographic variables are used as moderating variables of the model.

Hi again,

I just saw that I did not respond to your specific questions

Q1: Again, if your theory says that a variable should be controlled, do it. Don't judge based on any empirical stats (correlations, effects) whether to control it. Sample estimates are just error-prone estimates and--especially in low-N situations, power may be reduced. The essence of setting up the model (with controls) deals with issues on the population level and on the sample level *on average across all sample*. This is all you can achieve as a scientist: Being correct on average.

Q2: There are no steps. Just set up the structure as you think it is. Normally this will mean that you incorporate controls with effects on all variables. This has the positive result that confounding effects are reduced but at the same time, there is nothing testable when doing so. Hence, be sure that your overall model has still some "holes in the chees"

Q3: When your control variables are categorical, proceed as in any other dummy situation, that is, create k-1 dummy variables and use these.

Best,

Holger