#conjoint analysis

Choice Based Conjoint Study Designer: Attribute and Level Audit, Price Level Spacing, Tasks and Concepts per Screen, Dual Response None, Design Efficiency Test, Sample Size Rule of Thumb, HB Estimation, and Simulator Plan

Design a choice based conjoint study that can answer a real pricing or packaging question: audit attributes and levels for overlap and ambiguity, space price levels across a believable range, choose tasks and concepts per screen, add a dual response none and holdout tasks, test design efficiency before fielding, size the sample with a stated rule of thumb, and plan HB estimation, quality checks, and a share simulator.

Act as a market research methodologist who designs choice based conjoint (CBC) studies in Sawtooth Software (Discover and Lighthouse Studio) and Conjointly, and has had to explain away results from studies with overlapping attributes, a price range nobody would pay, and a sample too small for the segments the client wanted. Inputs: - The business decision the study must inform: [DecisionQuestion] - Draft attributes and levels, including price: [AttributeDraft] - Target respondents, screener criteria, and segments to compare: [Population] - Conjoint platform and panel source: [Platform] - Respondent budget and interview length limit in minutes: [SampleBudget] - Current or competitor prices in the market, if known: [PriceContext] - Output format: [Format] Generate: 1. A decision to design map: what each attribute lets the client decide, and any attribute that maps to no decision, flagged for removal. 2. An attribute and level audit of AttributeDraft: each attribute independent of the others, levels mutually exclusive and concrete (no "good" or "premium" without a definition), about 6 attributes or fewer for full profile tasks, and 2 to 5 levels each. Note the number of levels effect when one attribute has many more levels than the rest. 3. Price levels: evenly spaced across a range anchored to PriceContext, wide enough to show a slope and narrow enough to stay believable, with a note on whether price will be estimated per level or as a linear term. 4. Prohibitions: list any impossible combinations, keep them to the minimum, and explain the efficiency cost of each. 5. Task design: tasks per respondent, concepts per task, a dual response none question, and 2 fixed holdout tasks for validation, fitted to SampleBudget's interview length. 6. A design efficiency test before fielding: run the platform's test design with simulated respondents and review standard errors for each level, using the platform's guidance as the threshold. 7. Sample size: apply the Johnson and Orme rule of thumb n x t x a / c >= 500 (respondents, tasks, alternatives per task, largest number of levels on any attribute), show the floor, then raise it so each segment in Population has enough respondents, stated as a team choice. 8. Data quality: speeders, straightliners, and a fit statistic such as RLH from HB estimation compared with the chance level for the number of concepts. 9. An analysis plan: HB multinomial logit utilities, attribute importances with their caveats, a market simulator for share of preference, and willingness to pay reported only from simulations, never by dividing utilities. Constraints: - Never invent utilities, shares, or results; the output is a design. - Thresholds are labeled as guidelines or team choices. No em dashes.