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	Ethnographic Decision Modeling	Cultural Consensus Model	Decision Tree
Field	Anthropology	Anthropology	Machine learning
Family	Process / pipeline	Latent structure	Machine learning
Year of origin	1989	1986	1984
Originator	Christina H. Gladwin	A. Kimball Romney, Susan C. Weller & William H. Batchelder	Breiman, Friedman, Olshen & Stone
Type	Qualitative-formal model of real-world choice behavior	Latent-structure measurement model for shared cultural knowledge	Recursive partitioning (if-then rules)
Seminal source	Gladwin, C. H. (1989). Ethnographic Decision Tree Modeling. Qualitative Research Methods Series 19. Newbury Park, CA: Sage. ISBN: 9780803934870	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Aliases	Ethnographic Decision Tree Modeling, EDTM, Decision Tree Ethnography, Ethnographic Decision Models	Cultural Consensus Theory, CCT, Consensus Analysis, Informant Accuracy Model	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Related	2	4	5
Summary	Ethnographic decision tree modeling is a method for building a formal, qualitative model of how people actually make a specific recurring decision — such as whether to plant a crop, seek treatment, or adopt a practice. Developed by Christina Gladwin and set out in her 1989 Sage monograph, it elicits the criteria and rules people use through ethnographic interviews, represents them as an if-then decision tree, and then tests the tree's ability to predict the choices of a fresh sample of decision-makers.	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.