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Lizardo Vargas-Bianchi https://orcid.org/0000-0002-9027-7673

Abstract

This article presents a methodological proposal for thematic analysis based on two-level coding, in which data synthesis and conceptualization are conducted as independent moments during the initial stage of the analytic process. This coding approach differs from other initial procedures in thematic analysis by incorporating progressive coding levels with differentiated purposes of condensation and conceptualization. Because it is performed in close proximity to the original dataset, two-level coding helps preserve nuances and contextualized information, reducing the risk of premature thematization, superficial interpretation, and limited linkage to the data. The first level consists of identifying and condensing information relevant to the purpose of the study by synthesizing meaning into segments. The second level focuses on conceptualizing these condensed segments through the assignment of representative labels that function as codes. This approach addresses an underexplored niche in initial data processing, situated between types of thematic analysis and template analysis. The proposed technique enables a progressive, data-near analytic system applicable to any study that implements thematic analysis. Two-level coding is an alternative when researchers need to divide interpretive steps during coding in order to ground the analysis and reduce the risk of bias, advancing through controlled increments before thematization. It is also useful when analysis is carried out in fragmented periods, since it operates through delimited and progressive stages, providing an organized basis that facilitates returning to the work without losing analytic continuity. The proposal strengthens procedural transparency and supports traceability from research data to codes and, subsequently, to thematic development in practice.

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Section
Methodological Innovation Studies

How to Cite

Vargas-Bianchi, L. (2026). LEVEL-BASED ANALYSIS: PROGRESSIVE CODING IN PROXIMITY TO DATA. New Trends in Qualitative Research, 22(2), e1251. https://doi.org/10.36367/ntqr.22.2.2026.e1251
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