"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

在联合分析和离散选择分析方面的突破!

了解关于Latent GOLD Choice的更多信息:

• 下载Latent GOLD Choice试用版
• Latent GOLD Choice 4.5 基础版: 新特征列表
• Latent GOLD Choice 4.5 高级版(包含基础版)
• LG-Syntax模块
• 下载完整的Latent GOLD 4.0 Choice用户指导手册
• 下载Latent GOLD 4.5 Choice指南

Latent GOLD Choice 4.5的插件SI-CHAID 4.0现在发布了!

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.

了解SI-CHAID 4.0的更多内容

要了解Latent GOLD Choice 4.5和SI-CHAID 4.0是如何一起工作的，请查看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.

听专家们怎么说

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.

了解有关潜在类别选择建模的更多细节

同时，点击查看Latent GOLD Choice的常见问题部分

下载免费试用版!

下载Latent GOLD Choice免费试用版

Latent GOLD Choice 4.5: 新特征

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!

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.

Latent GOLD Choice 4.5: 高级模块

新的高级模块包括额外的高级特征：

连续潜在变量 (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!

多层建模

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.

其他有关高级模块特征的信息，下载Latent GOLD用户指导手册第一章

复杂样本数据的调查选项

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

Latent GOLD Choice文档

下载完整的Latent GOLD Choice手册或Latent GOLD Choice技术指导:

• 完整Latent GOLD Choice用户指导手册(8.5 MB)
• Latent GOLD Choice技术指导手册(439 KB)
• 指南

Latent GOLD Choice指南

The Demo version of Latent GOLD Choice comes with the following tutorials and accompanying data sets:

cbcRESP.sav - a simulated choice experiment

• Tutorial 1: Using LG Choice 4.5 to Estimate Discrete Choice Models (1.91 MB)
• Tutorial 1A: Using CHAID to Profile Latent Class Segments (677 KB)

brandAB.sav - a simulated brand-price choice experiment

• Tutorial 3: Estimating Brand and Price Effects (183 KB)

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

• Tutorial 2: Using LG Choice to Predict Future Choices (473 KB)

brandsAB1file.sav - a simulated brand-price choice experiment

• Tutorial 4: Using the 1-file Format (350 KB)

bank45.sav & bank9-1-file.sav - real data from a bank segmentation study

• Tutorial 5: Analyzing Ranking Data (1.35 MB)
• Tutorial 6: Using LG Choice to Estimate max-diff (best-worst) and Other Partial Ranking Models (1.52MB)

conjoint.sav, ratingRSP.sav, ratingALT.sav, ratingSET.sav: simulated data utilizing a 5-point ratings scale

• Tutorial 7: LC Segmentation with Ratings-based Conjoint Data (1.72MB)
• Tutorial 7A: LC Segmentation with Ratings-based Conjoint Data (374KB)