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CORExpress 
Latent GOLD®
LG-Syntax Module
Latent GOLD® Choice 
SI-CHAID®
GOLDMineR®
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Latent GOLD® Choice 4.5
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Products > Latent GOLD Choice
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 All SI products are designed to operate
on MS Windows 2000, XP, Vista, and 7
System Requirements:
2MB Drive Space, 512MB of RAM
Input files: .sav and .txt
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| "We find that [LG Choice] consistently gives interpretable results when most other procedures do not. Given the significance of the confound between error variance and model estimates, it is not surprising that a LC model that takes scale differences between classes into account will perform better."
Jordan Louviere
CenSoC
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Overview
Latent GOLD Choice is available as a stand-alone program or it can be co-licensed with Latent
GOLD. LG Choice does not include the features of Latent GOLD.
Types of Models:
- First choice models – An extended multinomial logit model (MNL) is used to estimate the
probability of making a specific choice among a set of alternatives as a function of choice
attributes and individual characteristics (predictors).
- Ranking models (including MaxDiff scaling) – The sequential logit model is used for situations where a 1st and 2nd choice, 1st
and last choice (best-worst), other partial rankings or choices from a complete ranking of all
alternatives are obtained.
- Conjoint rating models – An ordinal logit model is used for situations where ratings of various
alternatives, which may be viewed as a special kind of choice, are obtained.
For each of these situations, response data are obtained for one or more replications known as choice sets.
Latent class (LC) choice models account for heterogeneity in the data by allowing for the fact that different
population segments (latent classes) express different preferences in making their choices. For any
application, separate models may be estimated that specify different numbers of classes. Various model fit
statistics and other output are provided to compare these models to assist in determining the actual number
of classes. Covariates may also be included in the model for improved description/ prediction of the
segments.
Types of Applications
LC choice models are appropriate for both Stated Preference (SP) as well as Revealed Preference (RP) data. SP data is generally obtained from choice survey experiments. Common SP applications are the identification of market segments and the evaluation of market potential and estimation of market share for new products or services for each segment. Choice attributes may include brand and price in which case the resulting model allows for simulation of market shares under various pricing scenarios. Covariates may be included in a model to predict segment membership and choices for cases not included in the survey.
Advanced Option
LG Choice 4.5 Basic includes the standard features listed above. The Advanced option includes additional advanced features for continuous latent variables (CFactors), multilevel modeling, and survey options for complex sample data. Learn More
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Capabilities
Known Class Indicator
This feature allows more control over the segment definitions by pre-assigning selected cases (not) to be in a particular class or classes.
Conditional Bootstrap p-value
Model difference bootstrap can be used to formally assess the significance in improvement associated with adding additional classes, additional DFactors and/or an additional DFactor levels to the model, or to relax any other model restriction.
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Latent GOLD® Choice takes the Nobel prize-winning methodology to the next level
During the 1970’s a powerful methodology was proposed for analyzing respondent choices and using the resulting part-worth utility parameters to calculate share estimates under different competitive scenarios. The proposed random utility model, now referred to as the conditional logit, multinomial logit or aggregate choice model, earned the author a Nobel prize. (See http://elsa.berkeley.edu/~mcfadden/iatbr00.html)
Recently, this aggregate model has been improved to allow for the fact that different consumer segments utilize different preferences in making their choices. The result is a model that produces better share estimates by simultaneously identifying the important segments and the estimated share for each segment. Latent GOLD Choice represents the GOLD standard for developing advanced choice models. Choice data is obtained from surveys or actual behavior where respondents rate/rank/choose products/services/alternatives/options. Choice models differ from traditional regression models in that choices are predicted as a function of characteristics of the choice alternatives. Each alternative/product/service/option has attributes. What is estimated is the importances/utilities of these attributes. Latent classes represent segments that give differential importance to the various attributes.
Latent Class models provide the best way to analyze choice data
The two most popular ways to take into account differences in respondent preferences are Hierarchical Bayes (HB) models and Latent Class (LC) models, also know as finite mixture models. A recent extensive comparison of the two was made by Andrews, Ainslie and Currim, (2002), ”An empirical comparison of logit choice models with discrete vs. continuous representations of heterogeneity”, Journal of Marketing Research, Vol. XXXIX (November), 479-487. In a followup publication by Andrews and Currim, (May 2003, JMR), the authors refer to their earlier work as “…showing that finite mixture [LC] models are at least as effective as more recent methods [HB] for recovering heterogeneity …”. Added to the fact that the Latent GOLD Choice program can estimate models in a fraction of the time that it takes to estimate HB models, plus provides many additional capabilities, we believe that Latent GOLD Choice is the GOLD standard for advanced choice modeling.
Specifically, The LC models as implemented in Latent GOLD® Choice provide the following advantages over HB models:
- Much faster estimation -- Typical models are estimated in seconds or minutes as opposed to the hours required to estimate HB models.
- Simultaneous segmentation - In addition to individual level part-worth utility estimates, segments are identified simultaneously with the estimation of their utilities.
- Inclusion of covariates to describe/ predict segments. In addition to differing in preferences, covariates can be included in the model to see how the segments differ with respect to demographics and other respects.
- Justified statistically as part of the maximum likelihood (ML) framework. The ML framework allows numerous hypotheses to be tested.
See what the experts have to say about the future of conjoint and choice modeling.
"Wish List for Conjoint Analysis" by Eric Bradlow and comments by Jordan Louviere,
Bryan Orme, Joffre Swait, Jeroen Vermunt and Jay Magidson. Download a zip file (42K) or a pdf file of all of the articles (65K), or read them individually in our Articles section.
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Tutorials
All sample datasets and sample .lgf files used the tutorials below are downloaded to your computer when you install the demo version of Latent GOLD Choice. To download individual datasets and .lgf files, please refer to our Sample Datasets Page.
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Tutorial 1: Using LG Choice 4.5 to Estimate Discrete Choice Models
download PDF
+ overview
- overview
In this tutorial, we analyze data from a simple choice-based conjoint (CBC) experiment designed to
estimate market shares (choice shares) for shoes. In this tutorial you will:
- Set up an analysis
- Estimate choice models that specify different numbers of classes (segments)
- Explore which of these models provides the best fit to the data
- Utilize restrictions to refine the best fitting model
- Interpret results using our ‘final’ model
- Save results
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Tutorial 1A: Using CHAID to Profile Latent Class Segments
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+ overview
- overview
From Tutorial 1, the final model will be used to:
- Predict future choices
- Simulate choices among additional products of interest
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Tutorial 2: Using LG Choice to Predict Future Choices
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+ overview
- overview
One of the major benefits of discrete choice modeling is the ability to use the model to
predict choices for any choice set of interest including ones that were not utilized in the
original choice experiment (inactive sets). In this tutorial, we utilize our final 3-segment
model from tutorial 1 to simulate choice results for additional product alternatives of
interest. You will:
- Retrieve our previous model setup
- Utilize different Alternatives and Sets Files
- Examine predicted choice shares for current and inactive sets
- Create your own sets and obtain choice share predictions for these
- Include your sets in the tri-plot display
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Tutorial 3: Estimating Brand and Price Effects
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+ overview
- overview
A popular application of discrete choice modeling is to simulate how market share changes when the price
of a brand changes and when the price of a competitive brand changes. With latent class choice modeling,
it is possible to estimate these changes separately among those who are price sensitive, and those who are
not so price sensitive. That is, separate effects can be obtained for each segment as well as the overall
market.
One of the flexible features of the 3-file format in Latent GOLD Choice is that it is easy to define the
effects and interactions to be included in a model. In this tutorial, we first show how to estimate the effects
of brand and price using a model where PRICE and BRAND are treated as a distinct attributes – that is,
where the effect of price sensitivity is assumed to be the same for both brands. We then show how easy it
is to relax this assumption and examine the consequences if in fact the PRICE effect differs by brand.
In this tutorial you will:
- Retrieve a previously saved model setup
- Re-estimate all models
- Determine the number of classes and name the segments
- Impose restrictions to simplify model
- Examine Output including share simulations for additional choice sets
- Include Brand x Price interactions in the model
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Tutorial 4: Using the 1-file Format
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+ overview
- overview
In tutorial #3, we illustrated some analyses on data from the Brand Pricing Experiment,
where the data was input from a 3-file format. In this tutorial, we illustrate use of the
program when the same data is provided in the 1-file format.
In this tutorial you will:
- Retrieve a previously saved model setup
- Re-estimate all models
- Determine the number of classes and name the segments
- Impose restrictions to simplify model
- Examine Output including share simulations for additional choice sets
- Include Brand x Price interactions in the model
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Tutorial 5: Analyzing Ranking Data
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+ overview
- overview
Choice tutorials 1-4 all dealt with the analysis of first choices among sets of alternatives.
In applications where information is also available on additional choices -- 2nd choice, 3rd
choice, last choice, etc. -- improved efficiency of the part-worth utility estimates is
possible by taking this additional information into account. In such cases, Latent GOLD
Choice allows the utilization of the sequential logit model, a generalization of the
conditional logit model, to account for the additional choice information.
The way the sequential logit model works is that the first choice is analyzed as usual
based on the conditional logit model. If the model specified in Latent GOLD Choice is
set to a ‘Ranking’ Model, after the first record within a set, any additional records are
assumed to be associated with a 2nd choice, 3rd choice, etc. A 2nd choice is considered to
be a first choice from the set of alternatives that excludes the 1st choice, and so on for the
3rd, 4th and additional choices. For ranking models, Latent GOLD Choice automatically
excludes these prior choices from the consideration set of alternatives used for a current
choice.
This tutorial deals with full ranking data obtained from a real bank segmentation study as
described in Kamakura, Wedel, and Agrawal (1994), “Concommitant variable latent class
models for conjoint analysis”, International Journal of Research in Marketing,11, 451-
464. The data was provided for our use by Wagner Kamakura.
This tutorial illustrates the use of the Latent GOLD Choice program to analyze ranking
data.
You will:
- Identify 4 segments that differ in the importance placed upon various checking
account attributes.
- Interpret output in the context of rank-order preference data.
- Use concomitant variables (“covariates”) to predict and describe these segments.
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Tutorial 6: Using LG Choice to Estimate max-diff (best-worst) and Other Partial Ranking Models
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+ overview
- overview
In this tutorial, we will perform a re-analysis of the data used in tutorial #5. In this
application, 9 checking account alternatives were ranked in order of preference by N=256
bank customers. These alternatives are defined in terms of 4 attributes in the file
bank9ALT.sav:
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Tutorial 7: LC Segmentation with Ratings-based Conjoint Data
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+ overview
- overview
This tutorial shows how to use the Latent GOLD Choice program when the scale type of
the dependent variable corresponds to a Rating as opposed to a Choice or Ranking.
Ratings data can also be analyzed using the Regression module in Latent GOLD 3.0 and
the resulting parameter estimates will be identical. However, the Latent GOLD Choice
program produces additional output, and can be used to ‘fuse’ rating and choice data,
which is useful for situations where both types of data are available.
In this tutorial, we will reanalyze the conjoint data used previously in Latent GOLD
Tutorial #2: (http://www.statisticalinnovations.com/products/lg_tutorial2.pdf) “LC
Regression with Repeated Measures”.
You will learn how to:
- Setup the data for the Latent GOLD Choice program using the 1-file format
- Examine the additional output not available in Latent GOLD Regression module.
By examining the additional ‘Set Profile’ Output generated by the LG Choice program
we will see that the predicted ratings obtained using the standard aggregate (1-class)
conjoint model fail to provide an adequate fit to the observed ratings. In contrast, we will
see that the predictions generated by the 3-class model are quite good.
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Tutorial 7A: LC Segmentation with Ratings-based Conjoint Data
download PDF
+ overview
- overview
This tutorial shows how to use the Latent GOLD Choice program when the scale type of
the dependent variable corresponds to a Rating as opposed to a Choice or Ranking. For
this tutorial, the file setup is shown for the 3-file format structure. A more extensive
analysis of these data is provided Tutorial #7 which utilizes the 1-file format and also the
Latent GOLD Tutorial #2: “LC Regression with Repeated Measures”
(http://www.statisticalinnovations.com/products/lg_tutorial2.pdf).
In this tutorial, you will learn how to:
- Setup the data for a Ratings-based conjoint analysis using the 3-file format in the
Latent GOLD Choice program.
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Related Products
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Unleash the full power of the Advanced versions Latent GOLD 4.5 and/or LG Choice 4.5 with the new LG-Syntax module. Learn More
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Latent GOLD 4.5 is a powerful latent class and finite mixture program. Latent GOLD contains separate modules for estimating three different model structures -- LC Cluster models, Discrete Factor (DFactor) models, and LC Regression models. Learn More
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SI-CHAID is a program for performing CHAID (CHi-squared Automatic Interaction Detector) analyses. Results can be displayed simultaneously in the form of an intuitive tree diagram, crosstabulations, and a gains chart summary. Learn More
Using SI-CHAID, a CHAID analysis may be performed following the estimation of any LC model in Latent GOLD to profile the resulting LC segments based on demographics and/or other exogenous variables (Covariates). Learn More
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