قارن الطرق

	النظرية المجذرة الستراوسية القائمة على حالات متعددة	النظرية الكلاسيكية المتجذرة
المجال	نوعي	نوعي
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1990s (synthesis of Strauss & Corbin 1990 and multi-case design conventions)	1967
صاحب الطريقة	Anselm Strauss & Juliet Corbin (Straussian GT); multiple-case design formalized by Robert K. Yin and Kathleen Eisenhardt	Barney G. Glaser and Anselm L. Strauss
النوع	Qualitative research design and analytic strategy	Qualitative research method
المصدر التأسيسي	Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage. ISBN: 978-0803932500	Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.
الأسماء البديلة	multi-case Straussian GT, Strauss-Corbin grounded theory across cases, multiple-site Straussian grounded theory, multi-case GT (Strauss & Corbin)	Glaserian GT, CGT, original grounded theory, classic GT
ذات صلة	6	6
الملخص	Multiple case-based Straussian grounded theory combines Strauss and Corbin's systematic coding procedures — open, axial, and selective coding — with a multiple case design in which the same grounded theory analysis is conducted across two or more purposively selected cases. The approach aims to generate a mid-range theory grounded in rich, cross-case qualitative data while capitalizing on the comparative leverage offered by multiple sites or units, ultimately producing a theory with broader scope and stronger transferability than a single-case grounded theory study.	Classic Grounded Theory (CGT) is a systematic qualitative methodology for generating substantive theory from empirical data. Developed by Barney Glaser and Anselm Strauss in 1967, it uses iterative cycles of data collection, constant comparison, and memo writing to produce a core category and surrounding conceptual framework that explains a social or psychological process. Unlike its later variants, Glaserian CGT insists on emergence — theory must arise from data without forcing preconceived frameworks.