1. Causal Identification, followed by
2. Computing the Effect Size.
I found a good example of this approach, within the SEM (Structural Equation Modeling) approach, in a a recent article in Computers in Human Behavior, by Yen & Wu, the sequence is simply: 1. A 'qualitative' (i.e. no numbers!) part: how variables/concepts are expected to 'string themselves' out; and 2. A quantitative part, the post-SEM estimation phase, with numbers attached this time.
1.What one expects:
Conrady, S. and L. Jouffe, Bayesian Networks and BayesiaLab:
A Practical Introduction for Researchers. 2015: Bayesia USA:
free online: http://www.bayesia.com/book
Yen, Y.-S., & Wu, F.-S. (2016). Predicting the adoption of mobile financial services:
The impacts of perceived mobility and personal habit. Computers in Human Behavior,
65, 31-42. doi: http://dx.doi.org/10.1016/j.chb.2016.08.017