手法を比較

	パイロット理論的サンプリング	Grounded Theory	意図的サンプリング
分野	調査方法論	質的研究	調査方法論
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1967 (theoretical sampling origin); compound practice formalized in qualitative methodology literature	1967	Formalized ~1980–1990
提唱者	Glaser & Strauss (theoretical sampling); pilot study concept is longstanding in research methodology	Barney Glaser and Anselm Strauss	Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry
種類	Qualitative sampling strategy with pilot phase	Method	Non-probability sampling strategy
原典	Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. ISBN: 978-0202302607	Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.	Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. ISBN: 978-0803937796
別名	pilot-phase theoretical sampling, exploratory theoretical sampling, preliminary theoretical sampling	GT, Grounded Theory Approach	judgmental sampling, selective sampling, criterion-based sampling, purposeful sampling
関連	3	3	4
概要	Pilot theoretical sampling applies the logic of theoretical sampling — selecting participants based on emerging concepts and theory — within a deliberate pilot or preliminary phase of a study. Rather than committing immediately to a full sampling strategy, the researcher conducts a small initial round of data collection and analysis to test whether theoretical sampling is feasible, to refine the sensitizing concepts guiding participant selection, and to identify whether the field is productive before full-scale data collection begins.	Grounded Theory (GT) is a systematic qualitative research methodology in which theory emerges directly from data through iterative analysis, rather than being imposed before data collection. Developed by Barney Glaser and Anselm Strauss in 1967, GT prioritizes generating explanatory frameworks grounded in evidence.	Purposive sampling is a non-probability strategy in which the researcher deliberately selects participants, documents, or cases that are information-rich with respect to the research question. Rather than drawing units at random, the researcher applies explicit criteria aligned with the study's purpose, maximising the depth and relevance of the data collected. It is the default sampling logic in most qualitative research designs and is also used in mixed-methods and applied evaluative work.