Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Sampuli ya Kwenye Kundi (Cluster Sampling)× | Uchanganuzi wa Vipengele vya Uchunguzi (EFA)× | |
|---|---|---|
| Nyanja≠ | Metodolojia ya Dodoso | Takwimu |
| Familia≠ | Process / pipeline | Latent structure |
| Mwaka wa asili≠ | Early-to-mid 20th century; canonical treatment 1953/1977 | — |
| Mwanzilishi≠ | Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice | — |
| Aina≠ | Probability sampling design | Latent variable / dimension reduction |
| Chanzo asilia≠ | 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. DOI ↗ |
| Majina mbadala | cluster random sampling, area sampling, one-stage cluster sampling | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | 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. |
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