Evaluation-focused Explanatory Sequential Mixed Methods
Evaluation-focused Explanatory Sequential Mixed Methods Design · Also known as: explanatory sequential evaluation design, sequential explanatory mixed-methods evaluation, QUAN → QUAL evaluation design, two-phase sequential evaluation
Evaluation-focused explanatory sequential mixed methods is a two-phase research design in which a quantitative evaluation phase — typically measuring program outcomes, treatment effects, or performance indicators — is conducted first and then followed by a qualitative phase specifically designed to explain, contextualise, or interpret the quantitative findings. The design is widely used in program evaluation, policy research, and educational assessment where numbers reveal what happened but qualitative data reveal why.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
Use this design when a program evaluation or policy study produces quantitative outcome data that require contextual explanation — particularly when effect sizes vary unexpectedly across subgroups or sites, when null results are surprising, or when stakeholders need to understand the mechanisms behind measured impacts. It is especially suitable when quantitative data collection logistically precedes qualitative access (e.g., survey data are collected before site visits can be arranged). Do not use it when the evaluation question is purely exploratory and no prior quantitative framework exists, when resources do not allow two sequential phases, or when the qualitative inquiry needs to be open rather than targeted at specific numerical patterns.
Strengths & limitations
- Sequential structure is straightforward to plan, execute, and communicate to non-research stakeholders.
- Qualitative phase is strategically focused rather than open-ended, making data collection efficient and directly relevant to evaluation decisions.
- Produces actionable evaluation findings: numbers tell what happened; qualitative data tell why, supporting program improvement.
- Well-suited to evaluation contexts where administrative or survey data are routinely collected first.
- The two-phase structure allows different team members or skills to lead each phase, facilitating interdisciplinary evaluation teams.
- The sequential structure is time-intensive; both phases must be completed before integration findings are available.
- The qualitative phase is deliberately constrained by the quantitative results, which may miss important participant perspectives not anticipated by the initial analysis.
- Requires the researcher to make consequential sampling and protocol decisions between phases, demanding strong mixed-methods expertise at the transition point.
- If the quantitative phase produces weak or poorly measured outcomes, the qualitative follow-up has a flawed foundation to explain.
Frequently asked
How is this design different from a standard explanatory sequential mixed methods design?
The core sequential structure — QUAN first, QUAL second to explain — is the same. The 'evaluation-focused' label signals that the study's purpose is program evaluation rather than basic research: the quantitative phase measures program outcomes or impacts (not just descriptive variables), and the qualitative phase is oriented toward understanding implementation, mechanisms, or contextual factors relevant to improving or judging the program. The logic of the design is the same; the evaluation purpose shapes what is measured and who the audience for findings is.
Can both phases carry equal weight (QUAN + QUAL) rather than QUAN being primary?
In the standard explanatory sequential design, the quantitative phase is primary and the qualitative phase is supplementary. If the evaluation genuinely treats both strands as equal in weight and importance, a convergent parallel or embedded mixed methods design is more appropriate. Calling it explanatory sequential implies that QUAL exists to explain QUAN, which sets a clear priority hierarchy.
How do I decide which quantitative results to follow up on qualitatively?
Focus on results that are (1) unexpected or counterintuitive, (2) show unexplained variation across subgroups or sites, (3) are statistically significant but practically ambiguous, or (4) are particularly consequential for evaluation decisions. Avoid trying to explain every finding qualitatively — selectivity makes the qualitative phase manageable and strategically valuable.
Is this design appropriate for randomised controlled trial evaluations?
Yes. The design is compatible with RCT or quasi-experimental outcome studies. In this context, the quantitative phase is the trial's outcome analysis, and the qualitative follow-up might interview participants in treatment and control arms to understand differential experiences, implementation fidelity, or reasons for attrition — factors that help interpret why the trial produced the results it did.
How do I report integration in a journal article or evaluation report?
Dedicate a specific section to integration rather than presenting results in two separate chapters. Organise integration around the quantitative findings that prompted the qualitative inquiry, and for each finding present the qualitative themes that explain it. Use connecting statements such as 'The quantitative finding that X was explained qualitatively by Y' to make the explanatory link explicit and auditable.
Sources
- Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications. ISBN: 978-1483344379
- Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed Methods Research: A Guide to the Field. SAGE Publications. ISBN: 978-1452205434
How to cite this page
ScholarGate. (2026, June 3). Evaluation-focused Explanatory Sequential Mixed Methods Design. ScholarGate. https://scholargate.app/en/research-design/evaluation-focused-explanatory-sequential-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.
- Program EvaluationField Methods↔ compare