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| A breakthrough in conjoint and discrete choice analysis! |
"LGC simultaneously finds latent class segments among the sample respondents, and estimates discrete choice parameters
for each of those segments. The flexibility, scope, and graphical output of LGC are outstanding."
-- Ken Deal, Marketing Research Magazine, Fall 2006
Wizards and Potions: A new approach casts power on discrete choice conjoint experiments
(2006) K. Deal, Marketing Research Magazine, Fall 2006: 49-51
Version 4.0 of Latent GOLD Choice now available!
Version 4.0 is now available with new and exciting features plus an optional Advanced Module! Learn more about Version 4.0:
SI-CHAID® 4.0 add-on to Latent GOLD Choice 4.0 is now available!
SI-CHAID 4.0 profiling extension allows you to profile your latent class segments in terms of many covariates using the CHAID (CHi-squared Automatic Interaction Detector).
As shown in Magidson and Vermunt (2005), SI-CHAID 4.0 extends Latent GOLD’s covariate capabilities to improve your ability to profile latent classes in terms of demographics and other exogenous variables.
Learn more about SI-CHAID 4.0
To learn how Latent GOLD Choice 4.0 works with SI-CHAID 4.0, see Tutorial #1A
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.
Hear from the Experts
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.
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.
Learn more details about Latent class choice Modeling!
Also, check out our FAQ section on Latent GOLD® Choice.
Download a Free Demo!
Latent GOLD® Choice 4.0: New Features
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.
For more information, see
Tutorial #5: Using Latent GOLD 4.0 with the Known Class Option.
In this tutorial, we illustrate the use of the ‘known class’ feature in Latent GOLD 4.0 to take into account additional information on a subset of cases which allows us to
classify them into a particular class with probability one. In this case, the information comes from a physician’s diagnosis of the patient as ‘Depressed’ or merely ‘Troubled’,
corresponding to 2 of the 3 latent classes.
Download Tutorial 5 - coming soon!
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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 4.0: Advanced Module
The new Advanced module contains additional advanced features. The Advanced module is available for an extra $200.
Continuous latent variables (CFactors)
An option for specifying models containing continuous latent variables, called CFactors, in a cluster, DFactor or regression model.
CFactors can be used to specify continuous latent variable models, such as factor analysis and item response theory models, and regression models with continuous random effects. For more details, see:
- Popper, Richard, Kroll, Jeff and Magidson, Jay (2004).
"Applications of latent class models to food product development: a case study"
Sawtooth Software Proceedings, 2004.
- Tutorial #6: Estimating a Random Intercept Regression Model. In this tutorial, we illustrate the use of continuous factors (CFactors) to control for the ‘level effect’ in ratings data.
A latent class regression model is estimated where the dependent variable is ratings of 15 crackers on taste, and 12 predictors correspond to different attributes of the crackers. Different classes are identified that show
different taste preferences, controlling for their overall rating level. These data are based on a paper by Popper et. al. The use of CFactors requires the Advanced version of Latent GOLD 4.0.
Download Tutorial 5 - coming soon!
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Multilevel Modeling
an option for defining two-level data variants of any model implemented in Latent GOLD. Group-level variation may be accounted for by specifying group-level latent classes (GClasses) and/or group-level CFactors (GCFactors).
In addition, when 2 or more GClasses are specified, group-level covariates (GCovariates) can be included in the model to describe/predict them. The multilevel option can also be used for specifying three-level parametric or nonparametric random-effects regression models.
Sumultaneously develop country-level and individual level segments. See:
- Bijmolt, T.H., Paas, L.J., Vermunt , J.K. (2004).
Country and Consumer Segmentation: Multi-level Latent Class Analysis of Financial Product Ownership
International Journal of Research in Marketing, 21, 323-340
- Vermunt, J.K, and Magidson, J. (2005).
Hierarchical mixture models for nested data structures
In C. Weihs und W. Gaul (eds),
Classification: The Ubiquitous Challenge. Heidelberg:
Springer.
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For information on other Advanced Module features, download
Chapter 1 of the Latent GOLD User's Guide
Survey Options for complex sample data
Two important survey sampling designs are stratified sampling -- sampling cases within strata, and two-stage cluster sampling -- sampling within primary sampling units (PSUs) and subsequent sampling of cases within the selected PSUs.
Moreover, sampling weights may exist. The Survey option takes the sampling design and the sampling weights into account when computing standard errors and related statistics associated with the parameter estimates, and estimates the ‘design effect’
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Latent GOLD® Choice Documentation
Download the complete Latent GOLD Choice Manual or the Latent GOLD Choice Technical Guide:
- Complete Latent GOLD Choice User's Guide (8.5 MB)
- Latent GOLD Choice Technical Guide (439 KB)
- Tutorials
Latent GOLD® Choice Tutorials
The Demo version of Latent GOLD® Choice comes with the following tutorials and accompanying data sets:
- cbcRESP.sav - a simulated choice
experiment
- brandAB.sav - a simulated
brand-price choice experiment
Note: The following tutorials are currently being updated for version 4.0. They still accurately illustrate the different features of Latent GOLD Choice.
- cbcRESP.sav - a simulated choice
experiment
- brandsAB1file.sav - a simulated
brand-price choice experiment
- bank45.sav & bank9-1-file.sav -
real data from a bank segmentation study
- conjoint.sav, ratingRSP.sav,
ratingALT.sav, ratingSET.sav: simulated data utilizing a
5-point ratings scale
Order Latent GOLD® Choice now!
Latent GOLD® Choice is available either as a stand alone program or as an add-on to Latent GOLD® 3.0.
We are now accepting orders for Latent GOLD® Choice.
Unlike our other products, Latent GOLD® Choice is licensed on an annual basis and includes extended support and updates as part of the annual fee.
List price is $1,195 for the first year and $995 a year thereafter. The Advanced Module is an extra $200. Academic licenses are only $695 for the first year and $495 each year thereafter and include the Advanced module for free.
You can order Latent GOLD® Choice through our online order form.
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Free Latent GOLD® Demo 
