Analysis

The Analysis menu opens a hub with three entries: Contingent valuation, Discrete Choice Analysis and Interactive Dashboard. Each is summarised below; the full documentation lives in the corresponding subsection of Section 9 (the same screens are also reachable from there).

10.1 Contingent valuation¶

tickStat's contingent valuation model. Estimates the demand curve and willingness-to-pay from a binary choice question (yes/no at a randomized price), reporting optimal price, mean WTP and maximum expected revenue. See Section 9.6 for the parent Choice Analysis when the experiment is multi-attribute rather than a single price prompt.

10.2 Discrete Choice Analysis¶

Pools all responses across the survey's choice questions (or across a single Choice Report Group) and fits a Multinomial Logit or Mixed Logit / Random Parameters Logit model. Outputs include attribute importance, part-worths, raw model coefficients, log-likelihood / pseudo R², a Status Quo report when applicable, and — for Mixed Logit — preference heterogeneity densities and a per-respondent WTP distribution. See Section 9.6 and Section 9.7 for the full report.

10.3 Interactive Dashboard¶

Cross-filtering charts and tables built from the survey responses. See Section 9.8.

10.4 Discrete Choice Simulator¶

Predicts market shares for any hypothetical product configuration the researcher describes, using the coefficients estimated by the Discrete Choice Analysis (Section 10.2). The simulator is launched from the Discrete Choice Analysis Report via the Simulate market shares button — there is no standalone menu entry, because the simulator only makes sense once a model has been estimated.

What you configure¶

The screen shows a row per attribute and one column per non-Status-Quo alternative (for example Programa A and Programa B in a two-alternatives-plus-SQ design, or three columns when the experiment had four alternatives plus SQ). Pick a level from each dropdown to define the scenario.

The Status Quo column is not editable because its utility is fixed at 0 by construction — this is what "no programme" / "keep current situation" means in the model. You only describe the active alternatives; the Status Quo is the baseline they are compared against.

Clicking Send Data runs the prediction and renders two pie charts.

Reading the charts¶

Both charts come from the same softmax over the alternative utilities, but they answer different questions:

Percentage of Individuals per Product (includes the Status Quo) — the raw softmax exp(U_k) / Σ exp(U_j) over all alternatives, including the Status Quo. Tells you adoption: how the population would split among the active alternatives versus doing nothing.
Market Share (excludes the Status Quo) — the same probabilities renormalised over the non-SQ alternatives so they sum to 100%. Answers: among respondents who would accept some programme, which one would they pick?

The simulator supports any number of non-SQ alternatives — both pie charts adapt automatically (each slice is one alternative, with the Status Quo shown only in the first chart).

The model is a Multinomial Logit. Its coefficients (β) are finite and the softmax exp(U) / Σ exp(U) is strictly positive for every alternative, so the predicted share of any option is bounded away from 0 even when its utility is very negative. Three practical consequences:

A scenario in which every attribute is set to its worst level may still receive 5–15% of individuals, depending on how strong the β estimates are. This reflects unexplained heterogeneity in the underlying choices, not a bug.
The informative quantity is the gap between scenarios, not the absolute share of the worst one. A 10% / 60% / 30% split tells you the model discriminates well; a 35% / 40% / 25% split tells you it does not.
If you need shares that go to ~0% on bad scenarios, fit a Mixed Logit instead — the per-respondent random coefficients capture preference heterogeneity explicitly. See Section 9.7.