MétaCan
Menu
Back to cohort
Record W2888035510 · doi:10.1111/nin.12261

Discriminating among grounded theory approaches

2018· article· en· W2888035510 on OpenAlexaff
Kendra L. Rieger

Bibliographic record

VenueNursing Inquiry · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGrounded theoryEpistemologyPerspective (graphical)Selection (genetic algorithm)Qualitative researchPragmaticsTransparency (behavior)Process (computing)SociologyComputer scienceManagement scienceArtificial intelligenceSocial scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

To rationalize the selection of a research methodology, one must understand its philosophical origins and unique characteristics. This process can be challenging in the landscape of evolving qualitative methodologies. Grounded theory is a research methodology with a distinct history that has resulted in numerous approaches. Although the approaches have key similarities, they also have differing philosophical assumptions that influence the ways in which their methods are understood and implemented. The purpose of this discussion paper is to compare and contrast three widely used grounded theory approaches with key distinguishing characteristics, enabling a more thoughtful selection of approach. This work contributes to the existing literature through contrasting classic Glaserian grounded theory, Straussian grounded theory, and constructivist grounded theory in a systematic manner with prominent distinguishing characteristics developed from a review of the literature. These characteristics included historical development, philosophical perspective, role of the researcher, data analysis procedures, perspective of the grounded theory, and strengths/critique. Based on this analysis, three considerations are proposed to direct the methodological choice for a study: purpose, philosophy, and pragmatics. Understanding the similarities and differences in the grounded theory approaches can facilitate methodological transparency and determine the best fit for one's study and worldview as a researcher.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.246
metaresearch head score (Gemma)0.283
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score0.930

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2460.283
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0160.017
Science and technology studies0.0070.017
Scholarly communication0.0270.017
Open science0.0070.015
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

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.

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

Opus teacher head0.445
GPT teacher head0.539
Teacher spread0.094 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations223
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNursing InquirySame topicQualitative Research Methods and ApplicationsFrench-language works237,207