Concurrent Multilevel Mixed Methods Design
Also known as: simultaneous multilevel mixed methods, parallel multilevel mixed methods, multilevel concurrent mixed methods, QUAN+QUAL multilevel design
Concurrent multilevel mixed methods design collects quantitative and qualitative data simultaneously at two or more levels of a nested social system — for example, students within classrooms within schools — then integrates findings across those levels to produce a layered, comprehensive understanding of the phenomenon. The concurrent timing means both data strands are gathered in the same phase rather than one informing the other sequentially.
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When to use it
Use concurrent multilevel mixed methods when the research question involves a nested system — individuals embedded in groups, groups in organizations — and when both statistical patterns and lived meanings are needed to answer it fully. It is especially appropriate for program evaluations, educational research, health systems research, and organizational studies where multiple stakeholders at different levels experience the same phenomenon differently. Do not use this design when you lack access to multiple levels of the system, when the study timeline or budget cannot support simultaneous data collection across levels, or when a simpler single-level design would adequately address the question. Avoid it when the QUAN and QUAL strands have no meaningful connection at the integration stage — the design's value depends entirely on the quality of cross-level, cross-strand integration.
Strengths & limitations
- Captures complexity by gathering perspectives from multiple hierarchical levels of a social system simultaneously.
- Concurrent timing reduces total study duration compared with sequential designs that wait for one strand to complete before starting the other.
- Integration of QUAN and QUAL strands produces meta-inferences that neither strand alone could support.
- Well-suited to nested data structures common in education, health care, and organizational research.
- Allows statistical modeling of between-level variance (e.g., multilevel models) alongside qualitative interpretation of how that variance is experienced.
- Logistically demanding — coordinating simultaneous data collection across multiple levels and two methodological traditions requires substantial planning and resources.
- Integration across levels and strands is analytically complex; weak integration produces parallel rather than mixed methods findings.
- Sample size requirements differ sharply between strands: adequate statistical power for multilevel models typically requires large samples at lower levels, while qualitative depth requires purposive, often small samples.
- Assumes independence between strands during collection, which can be difficult to maintain in practice when field researchers work across both strands.
Frequently asked
How does this design differ from a simple concurrent triangulation 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.
Do I need multilevel modeling (HLM) for the quantitative strand?
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.
How many levels should I include?
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.
What does integration look like in practice?
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.
Can concurrent multilevel designs be used for exploratory research?
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.
Sources
- Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344996
- Hitchcock, J. H., & Onwuegbuzie, A. J. (Eds.). (2020). The Routledge Handbook for Advancing Integration in Mixed Methods Research. Routledge. link ↗
How to cite this page
ScholarGate. (2026, June 3). Concurrent Multilevel Mixed Methods Design. ScholarGate. https://scholargate.app/en/research-design/concurrent-multilevel-mixed-methods
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Concurrent Embedded Mixed Methods DesignResearch Design↔ compare
- Concurrent Triangulation Mixed Methods DesignResearch Design↔ compare
- Explanatory Sequential Mixed Methods DesignResearch Design↔ compare
- Exploratory Sequential Mixed Methods DesignResearch Design↔ compare
- Multilevel Mixed Methods DesignResearch Design↔ compare
- Multiphase Mixed Methods DesignResearch Design↔ compare