Compară metode

	Smith's Salience Index (S)	Pile Sorting
Domeniu	Anthropology	Anthropology
Familie	Process / pipeline	Process / pipeline
Anul apariției	1993	1988
Autorul original	J. Jerome Smith; popularized through ANTHROPAC (Borgatti) and AnthroTools	Cognitive anthropology tradition (Weller & Romney; Borgatti)
Tip	Salience statistic combining mention frequency and recall order in free-list data	Elicitation procedure for perceived similarity among domain items
Sursa seminală	Bernard, H. R. (2017). Research Methods in Anthropology: Qualitative and Quantitative Approaches (6th ed.). Lanham, MD: Rowman & Littlefield. ISBN: 9780759112421	Weller, S. C., & Romney, A. K. (1988). Systematic Data Collection. Qualitative Research Methods Series 10. Newbury Park, CA: Sage. ISBN: 9780803930742
Denumiri alternative	Smith's S, Salience Index S, Free-List Salience, Item Salience Score	Pile Sort Task, Free Pile Sort, Card Sorting (ethnographic), Sorting Task
Înrudite	4	4
Rezumat	Smith's salience index, conventionally written S, is the standard statistic for summarizing free-list data: for each item it combines how many informants mentioned the item with how early the item appeared in their lists. Within a single list each item receives a local salience equal to the number of items below it divided by the list length, so the first item scores highest and the last scores lowest; S is then the average of that local salience across the entire sample, counting zero for informants who never listed the item. The result is a single per-item number that ranks the items of a cultural domain by their joint frequency-and-priority prominence.	Pile sorting is an elicitation technique in which informants are handed a set of cards — one per item in a cultural domain — and asked to group them into piles of items that 'go together.' By recording which items each person places in the same pile and aggregating across many informants, the researcher builds a similarity matrix that reveals how the culture organizes the domain, which is then visualized with multidimensional scaling and clustering.