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A Practical Iterative Framework for Qualitative Data Analysis

2009· article· en· 1,215 citations· W1928669025 on OpenAlex· 10.1177/160940690900800107

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Classifier prediction

metacan-v1-d91a1de5be90

Predictions imitate two machine teachers. Scores are not calibrated prevalence probabilities.

Classifier candidate
MetaresearchQualitativeTheoretical or conceptualNot applicable
Classifier consensus
MetaresearchQualitativeTheoretical or conceptual
Teacher imitation scores

Codex

Metaresearch0.995
Theoretical or conceptual0.991
Qualitative0.905
Science and technology studies0.107
Not applicable0.054
Research integrity0.048
Other design0.017
Meta-epidemiology (broad)0.003
Bibliometrics0.002
Meta-analysis0.001
Scholarly communication0.001
Open science0.001
Bench or experimental0.000
Systematic review0.000
Simulation or modelling0.000
Case report0.000
Non-randomized trial0.000
Observational0.000
Meta-epidemiology (narrow)0.000
Randomized trial0.000

Gemma

Theoretical or conceptual0.998
Science and technology studies0.468
Research integrity0.189
Qualitative0.174
Not applicable0.161
Metaresearch0.065
Simulation or modelling0.010
Bibliometrics0.003
Meta-epidemiology (broad)0.002
Meta-analysis0.001
Open science0.001
Systematic review0.000
Case report0.000
Bench or experimental0.000
Non-randomized trial0.000
Meta-epidemiology (narrow)0.000
Scholarly communication0.000
Observational0.000
Randomized trial0.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.919
GPT teacher head0.828
Teacher spread
0.091 how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

The role of iteration in qualitative data analysis, not as a repetitive mechanical task but as a reflexive process, is key to sparking insight and developing meaning. In this paper the authors presents a simple framework for qualitative data analysis comprising three iterative questions. The authors developed it to analyze qualitative data and to engage with the process of continuous meaning-making and progressive focusing inherent to analysis processes. They briefly present the framework and locate it within a more general discussion on analytic reflexivity. They then highlight its usefulness, particularly for newer researchers, by showing practical applications of the framework in two very different studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.