Beyond Parallel Analysis: Iterative Proposition Testing for Mixed Methods Integration

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  In Roundtable Session: Teaching and mentoring mixed methods research: Highlighting challenges and opportunities for novices and experts

Abstract

This paper introduces proposition testing, a structured, team-based method for achieving iterative integration that yields robust conclusions in mixed methods research. In contrast to common approaches that present qualitative and quantitative data in parallel, proposition testing fosters sustained dialogue between strands to refine interpretive claims. Drawing on grounded theory, multiple case study, and constant comparison, the process involves generating, challenging, and revising propositions across multiple rounds of analysis. We illustrate the method’s application across several empirical studies, including traditional and less conventional mixed methods designs. By promoting analytic rigor, transparency, and collaboration, proposition testing offers a practical strategy for enhancing integration and maximizing the contributions of diverse data sources in addressing complex research questions.

Authors

• Bradley E. Cox, Michigan State University

• Karly Ball Isaacson, Michigan State University

• Brett Ranon Nachman, University of Pittsburgh

• Yilun Jiang, Michigan State University

• Rebecca Brower, Michigan State University