So sánh phương pháp

	Nghiên cứu Định lượng Khám phá Phân cấp	Chọn mẫu theo cụm	Phân tích nhân tố khám phá (EFA)
Lĩnh vực	Thiết kế nghiên cứu	Phương pháp luận khảo sát	Thống kê
Họ	Process / pipeline	Process / pipeline	Latent structure
Năm ra đời	mid-20th century onward	Early-to-mid 20th century; canonical treatment 1953/1977	—
Người khởi xướng	Developed from survey research traditions (Kish, 1965; Babbie, 1990s)	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	—
Loại	Quantitative observational and survey design	Probability sampling design	Latent variable / dimension reduction
Công trình gốc	Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications. ISBN: 978-1452226101	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Tên gọi khác	stratified exploratory survey design, hierarchical survey research, multilevel exploratory quantitative design, hierarchical descriptive-quantitative design	cluster random sampling, area sampling, one-stage cluster sampling	common factor analysis, açımlayıcı faktör analizi, factor analysis
Liên quan	2	5	4
Tóm tắt	Hierarchical exploratory quantitative research is a survey and observational design that structures both sampling and analysis across nested population levels — such as students within classrooms within schools — to explore patterns, distributions, and relationships in numerical data without a pre-specified directional hypothesis. It is oriented toward discovery and description rather than confirmation, making it appropriate early in a research programme when the phenomenon is not yet well-mapped.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.