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Data analysis: comparison between the different methodological perspectives of the Grounded Theory
José Luís Guedes Dos Santos1, Kamylla Santos da Cunha1, Edlamar Kátia Adamy2
1Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil.
Revista Da Escola De Enfermagem Da U S P
|April 19, 2018
Summary
This study compares data analysis in classic, Straussian, and constructivist Grounded Theory (GT) approaches. Understanding these distinct GT data analysis methods enhances research rigor in nursing.
Area of Science:
- Social Sciences
- Qualitative Research Methods
- Nursing Research
Background:
- Grounded Theory (GT) has evolved significantly since its inception.
- Three primary methodological perspectives exist: classic, Straussian, and constructivist GT.
- These approaches differ primarily in their data analysis processes.
Purpose of the Study:
- To describe and compare the data analysis procedures across the three main GT methodological perspectives.
- To elucidate the commonalities and distinctions in data analysis within classic, Straussian, and constructivist GT.
- To provide a framework for understanding GT data analysis variations.
Main Methods:
- Comparative analysis of the three Grounded Theory methodological perspectives.
- Detailed description and exemplification of the data analysis systems for each GT approach.
- Examination of data analysis stages, ranging from two to three, and the potential use of the paradigmatic model.
Main Results:
- The study outlines the unique characteristics of classic, Straussian, and constructivist Grounded Theory.
- Data analysis systems are described for each perspective, varying in stages and use of the paradigmatic model.
- Key differences and similarities in data analysis strategies are highlighted.
Conclusions:
- This research supports a clearer understanding of the distinct data analysis systems within different Grounded Theory methodologies.
- The findings can enhance the rigor and scientific quality of qualitative nursing research employing Grounded Theory.
- Distinguishing between GT approaches aids researchers in selecting and applying appropriate data analysis techniques.
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