Σύγκριση μεθόδων

	Smith's Salience Index (S)	Cultural Consensus Model
Πεδίο	Anthropology	Anthropology
Οικογένεια	Process / pipeline	Latent structure
Έτος προέλευσης	1993	1986
Δημιουργός	J. Jerome Smith; popularized through ANTHROPAC (Borgatti) and AnthroTools	A. Kimball Romney, Susan C. Weller & William H. Batchelder
Τύπος	Salience statistic combining mention frequency and recall order in free-list data	Latent-structure measurement model for shared cultural knowledge
Θεμελιώδης πηγή	Bernard, H. R. (2017). Research Methods in Anthropology: Qualitative and Quantitative Approaches (6th ed.). Lanham, MD: Rowman & Littlefield. ISBN: 9780759112421	Romney, A. K., Weller, S. C., & Batchelder, W. H. (1986). Culture as consensus: A theory of culture and informant accuracy. American Anthropologist, 88(2), 313–338.
Εναλλακτικές ονομασίες	Smith's S, Salience Index S, Free-List Salience, Item Salience Score	Cultural Consensus Theory, CCT, Consensus Analysis, Informant Accuracy Model
Συναφείς	4	4
Σύνοψη	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.	The cultural consensus model is a latent-structure measurement framework that estimates the culturally shared answers to a set of questions and, simultaneously, how much each informant knows, without the researcher knowing the correct answers in advance. Introduced by Romney, Weller and Batchelder in 1986, it treats agreement among informants as evidence of shared knowledge and uses a factor-analytic (or, in modern variants, Bayesian) decomposition to recover both a single 'answer key' and an informant-specific competence score.