The Polas Methodological Fit Framework: A Unified Model for Selecting Research Methods in Social Science Research

Abstract

Selecting an appropriate research method remains a persistent challenge for social science researchers, particularly when research problems are complex and multidimensional. Despite the availability of methodological guidance, a significant gap exists between research questions and methodological choices, resulting in weak theoretical alignment and empirically fragile study designs. This conceptual paper addresses that gap by proposing a structured framework to guide researchers in determining when and why to use quantitative, qualitative, or mixed methods approaches. Drawing on methodological literature, conceptual synthesis techniques, and illustrative examples from practice, the paper introduces the Polas Methodological Fit Framework (PMFF) as a systematic, decision-oriented model for method selection. The study adopts a conceptual research design, integrating theoretical analysis, a review of existing method-selection guidelines, and an applied illustration. The PMFF operationalises method choice through six interrelated dimensions: the Nature of the Research Problem (NRP), the Type of Research Questions (TRQ), the Depth vs. Breadth Requirement (DBR), the Researcher Capacity and Constraints (RCC), the Nature of the Phenomenon (NOP), and the Complexity of Inquiry (COI). Together, these dimensions offer a structured basis for transparent and defensible methodological decisions. The framework carries significant implications for doctoral students, research supervisors, and early-career scholars by enhancing methodological clarity, transparency, and rigour. The PMFF represents a novel, unified, and sequentially ordered contribution that addresses the long-standing fragmentation in method-selection guidance and advances more coherent and contextually grounded research design in social science scholarship.

Received: 2026-04-23 | Revised: 2026-05-31 | Accepted: 2026-06-23 | Published: 2026-06-30

Declarations

Ethics and Guidelines: Not applicable.

Consent to participate: Not applicable.

Consent to publish: The authors have provided consent to publish.

Competing interests: The authors declare no competing interests.

Data availability statement: Data will be made available on reasonable request from the corresponding author.

Funding: This research received no external funding.

Clinical Trial Number: Not Applicable.

Declaration of using generative AI: The author used Microsoft Copilot and Claude, a generative artificial intelligence tool, to assist with tasks including language refinement, structural organisation, and editorial clarity during the preparation of this manuscript. The generative AI tool was not used to produce original ideas, develop theoretical arguments, interpret findings, or draw conclusions. All intellectual contributions, conceptual development, analytical decisions, and final scholarly judgements presented in this manuscript are entirely the responsibility of the author. The author reviewed, critically evaluated, and takes full accountability for all AI-assisted content included in the final submission.

Author Contributions: Conceptualization, M.R.H.P.; methodology, M.R.H.P.; formal analysis, M.R.H.P.; writing—original draft preparation, M.R.H.P.; writing—review and editing, M.R.H.P. All authors have read and agreed to the published version of the manuscript.