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| Paired Comparison Method× | Triad Test× | |
|---|---|---|
| Tudományterület | Anthropology | Anthropology |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve | 1988 | 1988 |
| Megalkotó≠ | Cognitive anthropology tradition (Weller & Romney) | Cognitive anthropology tradition (Weller & Romney; Borgatti) |
| Típus≠ | Elicitation procedure for scaling or ranking items by a single criterion | Elicitation procedure for fine-grained perceived similarity |
| Alapmű | Weller, S. C., & Romney, A. K. (1988). Systematic Data Collection. Qualitative Research Methods Series 10. Newbury Park, CA: Sage. ISBN: 9780803930742 | Weller, S. C., & Romney, A. K. (1988). Systematic Data Collection. Qualitative Research Methods Series 10. Newbury Park, CA: Sage. ISBN: 9780803930742 |
| Alternatív nevek | Method of Paired Comparisons, Pairwise Comparison Task, Pair-Comparison Ranking, Pairwise Judgment Elicitation | Triadic Comparison, Triads Task, Method of Triads, Triad Sorting |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | The paired comparison method is a systematic elicitation technique in which informants are shown every possible pair of items from a set and asked, for each pair, which member better fits a single criterion — which is sweeter, more dangerous, more prestigious, or more similar to a reference. Because every item is judged against every other item, the procedure forces fine, transitive discriminations that a one-shot ranking would blur. Aggregating the pairwise verdicts across informants yields a dominance or proximity matrix from which a stable rank order or an interval scale can be recovered. | The triad test is an elicitation technique for measuring perceived similarity among the items of a cultural domain. Informants are shown items three at a time and asked to pick the one that is most different (or, equivalently, which two are most alike). Across many triads and many informants, the pattern of which items are repeatedly kept together yields a fine-grained similarity matrix that is analyzed with multidimensional scaling and clustering. |
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