Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Δειγματοληψία Ευκολίας Πολλαπλών Επιπέδων× | Δειγματοληψία πολλαπλών επιπέδων με στρωματοποίηση× | |
|---|---|---|
| Πεδίο | Μεθοδολογία Επισκοπήσεων | Μεθοδολογία Επισκοπήσεων |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1980s–1990s (concurrent with multilevel modeling development) | 1950s–1970s |
| Δημιουργός≠ | Emerged from multilevel/hierarchical research traditions | Formalized by Leslie Kish and William G. Cochran in the mid-20th century survey sampling literature |
| Τύπος≠ | Non-probability sampling design | Probability sampling design |
| Θεμελιώδης πηγή≠ | Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728462 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Εναλλακτικές ονομασίες | hierarchical convenience sampling, nested convenience sampling, multilevel accessibility sampling, multi-tier convenience sampling | hierarchical stratified sampling, nested stratified sampling, multilevel stratified design, stratified multilevel sampling |
| Συναφείς≠ | 5 | 6 |
| Σύνοψη≠ | Multi-level convenience sampling is a non-probability approach in which units are selected by convenience at each of two or more nested levels of a hierarchy — for example, recruiting whatever schools agree to participate and then enrolling all available students within those schools. It is widely used in organizational, educational, and health research where the researcher has limited control over access but must respect the nested structure of the population. | Multi-level stratified sampling applies stratification at two or more hierarchical levels of a nested population structure — for example, first stratifying geographic regions, then stratifying schools within each region, then stratifying classrooms within each school. This layered control over the composition of the sample at every level reduces variance and supports analysis at each level of the hierarchy, making it a powerful design for large-scale educational, epidemiological, and organizational surveys. |
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