A Process for Generating Strong, Novel, and Parsimonious Explanatory Models
Charlette Donalds, Kweku-Muata Osei-Bryson · 2024
In this chapter, our new process for generating strong, novel, and parsimonious explanatory models is presented and logically justified. This process would take as its major inputs the following: 1) results from a recent meta-analytic review (MAR) that focused on studies in the specific domain of interest and 2) results from a study in the domain of interest that is even more recent than the given MAR , and which includes at least one statistically significant predictor variable that was not included in the MAR . The proposed process has as its major outputs one or more novel candidate causal models, each of which is likely to offer strong empirical support with respect to the given dependent variable. The number of novel causal models that are generated depends on or is determined by the research team. The process also has an optional feature that given a set of candidate models, the research team may be interested in determining what would be a promising subset of these models for which can data be collected on a questionnaire that will result in at least the required N Min usable observations via sampling the relevant target population.