Discrete-choice data
Alternative-specific and case-specific covariates
Balanced and unbalanced choice sets
One selected outcome per case or ranked outcomes
Conditional logit models
McFadden’s choice model
Odds ratios
Robust, cluster–robust, bootstrap, and jackknife standard errors
Mixed logit models
Also known as
Mixed multinomial logit models
Mixed discrete choice models
Discrete choice models with random coefficients
Random-effect and random-coefficient distributions
Normal
Correlated normal
Lognormal
Truncated normal
Uniform
Triangular
Cross-sectional or panel data
Relaxes IIA assumption
Robust, cluster–robust, bootstrap, and jackknife standard errors
Survey data support
Multinomial probit models
Homo- or heteroskedastic variances
Various correlation structures, including user-specified
Relaxes IIA assumption
Probabilities based on GHK simulator
Robust, cluster–robust, bootstrap, and jackknife standard errors
Nested logit models
Random-utilities maximization model
Full maximum-likelihood estimation
Up to eight nested levels
Facilities to set up the data and display the tree structure
Predictions for utility functions, probabilities, conditional probabilities, and inclusive values
Robust, cluster–robust, bootstrap, and jackknife standard errors
Linear constraints, including constraints on inclusive-value parameters
Summarize choice data
Tabulate choice sets
Summarize covariates by alternative
Tabulate covariates by chosen alternative
Report potential problems in data
Rank-ordered probit models
Plackett–Luce model, exploded logit, choice-based conjoint analysis
Homo- or heteroskedastic variances
Various correlation structures, including user-specified
Relaxes IIA assumption
Probabilities based on GHK simulator
Robust, cluster–robust, bootstrap, and jackknife standard errors
Rank-ordered logit models
Also known as
Plackett–Luce model
Exploded logit
Choice-based conjoint analysis
Complete rankings of ordered outcome
Incomplete rankings of ordered outcome
Account for ties (indifference
)
Prediction of probability that alternatives are ranked first
Robust, cluster–robust, bootstrap, and jackknife standard errors
Truly interpret results
Estimate
Expected probabilities of selecting each alternative
In the population
In a subpopulation
At specified covariate levels
Difference in probabilities of selecting an alternative
As a covariate changes for this alternative
As a covariate changes for another alternative
As a covariate changes for all alternatives
Marginal effects
Tests and confidence intervals for everything