Experimental Phenomenological Analysis: A Step-by-Step Guide for Integrating Phenomenological Data into Neurophenomenology through a CAQDAS–R Workflow

Neurophenomenology aims to combine first-person subjective experience with third-person neurobiological measures but still lacks integrated, reproducible workflows. This article introduces Experimental Phenomenological Analysis (EPA), a structured workflow that uses Computer-Assisted Qualitative Data Analysis Software and the R statistical environment to systematically analyze phenomenological data. EPA proceeds through two cycles: foundational phenomenological construction with intersubjective triangulation, and corpus-wide consolidation with computational integration. It articulates lived experience through unified analytic units, diachronic phases and dynamics, synchronic categories, and experiential structures. The workflow includes intersubjective stabilization, agreement analysis, and computational visualization. Its application is illustrated with data from an empathy-for-pain paradigm involving simulated Alzheimer's patient interaction. EPA enables qualitative, quantitative, mixed-methods, and neurophenomenological analyses, supporting transparent and reproducible phenomenological science.

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Abstract

Neurophenomenology has progressively consolidated as a research program aimed at integrating first-person lived experience with third-person neurobiological and behavioral measures. Nevertheless, despite its growing empirical development, neurophenomenology continues to face what Varela described as a “pragmatic and methodological limbo”. Subjective experience is recognized as scientifically indispensable, yet the field still lacks sufficiently integrated, reproducible, and operationally coherent workflows for the empirical investigation of lived experience. In response to these limitations, the present article introduces Experimental Phenomenological Analysis (EPA), a structured and reproducible analytical workflow designed to support the systematic analysis of phenomenological data within neurophenomenology through the integration of Computer-Assisted Qualitative Data Analysis Software (CAQDAS) and the R statistical environment. EPA is organized into two interconnected analytic cycles: (i) foundational phenomenological construction and intersubjective triangulation, and (ii) corpus-wide consolidation and computational integration. Across these cycles, the framework progressively articulates lived experience through unified analytic units integrating diachronic and synchronic units, together with the progressive articulation of diachronic phases, diachronic dynamics, higher-order synchronic categories, and experiential structures grounded in participant descriptions. The workflow further incorporates procedures for intersubjective stabilization, intersubjective agreement analysis, computational visualization, and procedures supporting integration with quantitative and neurobiological measures. To illustrate its application, the article presents the complete EPA workflow using real phenomenological data derived from an experimental empathy-for-pain paradigm involving face-to-face interaction with a simulated Alzheimer’s patient. Importantly, EPA is not limited to qualitative phenomenological description; the resulting hierarchically organized and computationally structured corpus enables subsequent qualitative, quantitative, mixed-methods, and neurophenomenological analyses, including integration with psychometric, behavioral, physiological, and neurobiological measures. More broadly, EPA contributes to ongoing efforts to establish phenomenology as a transparent, reproducible, and multimodally integrated scientific practice within contemporary neurophenomenology.