Diferencias en el análisis de datos desde distintas versiones de la Teoría Fundamentada

  1. Estrada-Acuña, Rosa Amelia
  2. Arzuaga, María Angélica
  3. Giraldo, Clara Victoria
  4. Cruz, Fatima
Revista:
Empiria: Revista de metodología de ciencias sociales

ISSN: 1139-5737

Año de publicación: 2021

Número: 51

Páginas: 185-229

Tipo: Artículo

DOI: 10.5944/EMPIRIA.51.2021.30812 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Empiria: Revista de metodología de ciencias sociales

Objetivos de desarrollo sostenible

Resumen

Grounded Theory (TF) is a widely known and widely used research methodology in the social sciences. It has various versions (Glaserian Grounded Theory, Straussian, Dimensional, Constructivist and Situational Analysis) show differences that tend to generate some confusion in their study and use. It is the case with the data analysis process. To identify these differences, we carried out an intentional review of the literature published in the last 20 years in Spanish, Portuguese, and English. We did a systematic search in five scientific databases and Google Scholar Beta. We selected 72 texts, included books and articles. They were 31 of them written by the main referents of each version and, 41 were written by other authors who contribute to the understanding of the method. The analysis of these texts allowed us to identify common elements and significant differences. The results that we found reveal five common aspects between the versions: constant comparison, theoretical sampling, elaboration of memoranda, theoretical sensitivity, and theoretical saturation. Also, we found we find significant differences in the data analysis process, the philosophical currents of each version, the theory generated, the data collected as interviews, among others. In particular, the analysis process in the five versions has common aspects, but the procedures used are different. Finally, we elaborate graphical representations to facilitate understanding of data analysis. The article makes it easier for investigators, especially new ones, to recognize how and when to fit in the various aspects used for data analysis, especially if the research requirements force you to mix or merge some perspectives during the analysis process.

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