Сравнение на методи

	Choice-Based Conjoint	MaxDiff / Best-Worst Scaling
Област	Marketing Research	Marketing Research
Семейство	Regression model	Regression model
Година на възникване	1983	2005
Създател	Jordan J. Louviere & George Woodworth; Sawtooth Software (Bryan Orme)	Jordan J. Louviere; A. A. J. Marley & Jordan Louviere
Тип	Discrete-choice experiment for product preference and part-worth utilities	Best-worst choice task for scaling relative importance of items
Основополагащ източник	Louviere, J. J., & Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research, 20(4), 350-367.	Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-Worst Scaling: Theory, Methods and Applications. Cambridge: Cambridge University Press.
Други названия	CBC, Discrete-Choice Conjoint, Choice Experiment Conjoint, Choice-Based Conjoint Analysis	MaxDiff, Best-Worst Scaling, BWS, Maximum Difference Scaling
Свързани	4	4
Резюме	Choice-based conjoint analysis (CBC) measures how consumers value the features of a product by observing the choices they make among competing, attribute-defined profiles rather than by asking them to rate attributes directly. Each respondent completes a series of choice tasks, picking the single most preferred alternative (often with a 'none' option) from a small set, and the pattern of choices across many tasks reveals the implicit trade-offs people make. The method grew out of Louviere and Woodworth's 1983 integration of conjoint measurement with discrete-choice theory, which showed that controlled choice experiments could be analyzed with the multinomial logit model. Because the choice task mimics a real purchase decision, CBC has become the dominant form of conjoint in commercial marketing research, popularized by Sawtooth Software. Estimation recovers part-worth utilities for every attribute level, either at the aggregate level or, more commonly today, individually through hierarchical Bayes. Those utilities then feed market simulators that predict shares of preference for new or hypothetical product configurations.	MaxDiff, also known as best-worst scaling (BWS), measures the relative importance or preference of a set of items by repeatedly asking respondents to identify the best (most important or most preferred) and worst (least) item within small subsets. Introduced by Jordan Louviere and formalized by Marley and Louviere's 2005 probabilistic models, the method exploits the fact that people are far better at picking extremes than at rating many items on a scale. Each best-worst judgment reveals the maximum-difference pair in a set, and across many balanced subsets the choices pin down a single interval scale of item utilities. Because every respondent is forced to make trade-offs, MaxDiff sidesteps the scale-use bias and lack of discrimination that plague rating grids, where respondents often call everything important. Item scores can be computed by simple best-minus-worst counts or, more rigorously, by fitting a multinomial logit choice model, with hierarchical Bayes yielding individual-level, probability-scaled importances. The result is a clear, discriminating ranking of items that supports prioritization, segmentation, and feature selection.