Compara mètodes

	Investigació quantitativa exploratòria jeràrquica	Mostreig per conglomerats	Anàlisi Factorial Exploratòria (EFA)
Camp	Disseny de recerca	Metodologia d'enquestes	Estadística
Família	Process / pipeline	Process / pipeline	Latent structure
Any d'origen	mid-20th century onward	Early-to-mid 20th century; canonical treatment 1953/1977	—
Autor original	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	—
Tipus	Quantitative observational and survey design	Probability sampling design	Latent variable / dimension reduction
Font seminal	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.
Àlies	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
Relacionats	2	5	4
Resum	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.