1st begin with SEM to confirm hypothesized relationships and validate key variables, providing a strong quantitative foundation. Next, use fsQCA to explore combinations of variables, emphasizing causal complexity and uncovering multiple pathways (equifinality) to outcomes. Present SEM first for a broad overview and model credibility. This method leverages both methods for a comprehensive analysis and holistic understanding of the research problem.

What kind of sample do you have? SEM generally requires fairly large sample (e.g., 10 observations per parameter in the model), while FSQCA typically works with a limited number of cases.