Concurrent Multilevel Mixed Methods Design

Concurrent triangulation collects QUAN and QUAL data simultaneously but typically at a single level of analysis and primarily to check convergence. Concurrent multilevel design adds structural complexity by deliberately targeting multiple hierarchical levels and integrating findings both across strands and across levels, producing cross-level meta-inferences rather than simple validation.

Not necessarily, but when the quantitative data have a nested structure — students in classrooms, patients in wards — ignoring that structure by using ordinary regression violates the independence assumption and typically inflates Type I error. Hierarchical linear modeling (HLM) or mixed-effects models are usually recommended. Whether they are required depends on the degree of intraclass correlation in your data.

Two levels is the minimum and often sufficient — for example, individuals within groups. Three or more levels add explanatory power but multiply the logistical and analytic complexity substantially. Start with the fewest levels that the research question genuinely requires, and ensure you have adequate sample sizes at each level before adding more.

Common integration strategies include joint displays — tables or matrices placing QUAN statistics alongside QUAL themes row by row — and narrative meta-inferences that synthesize conclusions across the levels. A useful test: if you can report the QUAN findings and QUAL findings in completely separate sections with no cross-referencing, integration has not occurred; genuine integration requires explicitly connecting results from different levels and strands.

Yes, though the design is more commonly used when the levels of analysis are theoretically specified in advance. In exploratory contexts, the qualitative strand may help identify which levels or processes merit quantitative measurement, but if that discovery process itself needs to guide QUAN instrument development, a sequential exploratory design may be more appropriate.