General

What is conjoint and discrete choice analysis?

Why can’t discriminant analysis and traditional multinomial logit models be used to estimate discrete choice models?

How do latent classes enter into conjoint analysis?

Can latent class conjoint models be estimated with traditional statistical modeling software?

Advanced

We usually build our own simulator in Excel. If I use the individual HB-like utilities generated by the Latent GOLD Choice program, can I utilize my MNL share calculator with Latent Gold to estimate individual share of preference?

Does inclusion of latent classes in my model allow better share predictions?

General

Q. What is conjoint and discrete choice analysis?

A. In conjoint analysis, the goal is to obtain share estimates for various product or service configurations of interest. These configurations are defined based on combinations of attribute levels (e.g, PRICE = $499, BRAND = Sony, …). In traditional conjoint analysis, also known as ratings-based conjoint, respondents rate the various products/services/alternatives/options. In discrete choice studies, also known as choice based conjoint (CBC) experiments, respondents are posed with various competitive scenarios where they are asked to choose between 2 or more products/services/alternatives/options. Rather than rating each alternative, they are asked to select the most preferred or most important, or rank the alternatives. In both kinds of conjoint analyses, alternatives are expressed in terms of 1 or more attributes. A model then estimates the importances/utilities of these attributes.

Q. Why can’t discriminant analysis and traditional multinomial logit models be used to estimate discrete choice models?.

A. Such models utilize predictors that are characteristics of the respondents, and cannot accomodate characteristics (attributes) of the choices as predictors. To do the latter requires the conditional logit model, now also known as the multinomial logit model.

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Q. How do latent classes enter into conjoint analysis?

A. Since not all respondents have the same utilities, K>1 latent classes (segments), each having its own importance to the various attributes are assumed to exist, and utilities are estimated for each segment. Various statistics are available to help determine the number of segments K..

Q. Can latent class conjoint models be estimated with traditional statistical modeling software?.

A. No. Traditional conjoint programs do not include latent classes and therefore typically produce distorted share estimates (see article). Ratings based conjoint models can be estimated using Latent GOLD (see LG tutorial #2) or Latent GOLD Choice. Discrete choice (choice-based conjoint) models require Latent GOLD Choice..

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Advanced

Q. We usually build our own simulator in Excel. If I use the individual HB-like utilities generated by the Latent GOLD Choice program, can I utilize my MNL share calculator with Latent Gold to estimate individual share of preference?

A. Yes. Latent GOLD Choice allows you to specify both active and inactive sets (and active as well as inactive alternatives) for an analysis. The inactive sets (and inactive alternatives) are not included in the experiment so that no response information is available for these. They are used because they are of interest and the resulting model can be used to generate/simulate shares.

ACTIVE SETS

The output tab of the program allows you to output to a file a) predicted values and/or b) individual coefficients for each choice set for each case. For the individual predicted values, this output consists of 1) the probability (share) of each choice and 2) the most likely choice. If you select the "HB prediction" option, these shares are calculated (automatically) using the HB-like individual coefficients. Alternatively, the default option is to calculate shares using maximum likelihood estimation which generally differs slightly from the HB estimates.

Regarding simulation, you could use these individual coefficients in your Excel simulator as you do now.

SEPARATE RESULTS BY SEGMENT

For the K-class model, the standard output of the program includes for each of the K classes (for each of the K segments) separately, as well as for the overall sample a) the part-worth utilities ("parameters output"), plus b) predicted probabilities (shares) for each choice set ("Sets Profile").

INACTIVE SETS

Regarding simulation, the program allows you to specify any number of additional choice sets to be simulated (we call these 'inactive' sets), and includes share estimates for each of these in addition to each of the 'active' sets in the Sets Profile output. See Tutorial 2 for an example of this. If the "HB Prediction" option is set in the Output tab, the overall share estimates in the Sets Profile output are based on the individual HB-like coefficients. Otherwise, they are based on the maximum likelihood.

Q. Does inclusion of latent classes in my model allow better share predictions?

A. Yes. The standard aggregate model generally suffers from violations of the independence of irrelevant alternatives (IIA) problem, which distorts share predictions. Inclusion of latent classes to account for the fact that different segments have different utility preferences, (and IIA holds true within each segment), is a way of resolving this problem and improving share predictions. See "New Developments in Latent Class Choice Models" article for more information.

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