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 4.5 with the Known Class Option.
In this tutorial, we illustrate the use of the 멾nown class? feature in Latent GOLD 4.5 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 physician뭩 diagnosis of the patient as 멏epressed? or merely 멦roubled?, 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.

Overdispersed (Count and Binomial Count in Regression)

Overdispersion is a common phenomenon in count data. It means that, as a result of unobserved heterogeneity, the variance of the count variable is larger than estimated by the Poisson (binomial) model. The overdispersed option makes it possible to account for unobserved heterogeneity by assuming that the rates (success probabilities) follow a gamma (beta) distribution. This yields a negative-binomial model for overdispersed Poisson counts and a negative-binomial model for overdispersed binomial counts. Note that this option is conceptually similar to including a normally distributed random intercept in a regression model for a count variable.

The overdispersion option is useful if one wishes to analyze count data using mixture or zero-inflated variants of (truncated) negative-binomial or beta-binomial models (Agresti, 2000; Long, 1997; Simonoff, 2003). The negative-binomial model is a Poisson model with an extra error term coming from a gamma distribution. The beta-binomial model is a variant of the binomial count model that assumes that the success probabilities come from a beta distribution. These models are common in fields such as criminology, political sciences, medicine, biology, and marketing.

2. Latent GOLD? 4.5: 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 멿evel 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 4.5. Download Tutorial 5 - coming soon!

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.

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 멶esign effect?

3. Latent GOLD? 4.5 Demo version & Tutorials

Download a demo version of Latent GOLD? 4.5 and see the new version for yourself!

• Demo version

Video Tutorials - Follow our tutorials live on your computer!

We would like to thank our colleagues at the Statistical Consulting Group at UCLA Academic Technology Services for creating video seminars for our tutorials. Tutorials for which video is available are marked with the icon.

Go to Video Tutorials

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This tutorial demonstrates the use of the CHAID link in Latent GOLD 4.5:

• Tutorial 4: Profiling LC Segments using the CHAID Option (1.56 MB)

Use these other tutorials to explore the demo version:

• Tutorial 1: Using Latent GOLD? 4.5 to Estimate LC Cluster Models (1.69 MB) ?
• Tutorial 2: Using Latent GOLD? 4.5 to Estimate DFactor Models (1.63 MB) ?
• Tutorial 3: LC Regression with Repeated Measures (1.67 MB)
• Tutorial 6A: Comparing Segments obtained from LC Cluster and DFactor Models in a Consumer Preference Study (281 KB)

Note: The following additional tutorials are currently under development. Check back shortly for updates.

• Tutorial 5: Using Latent GOLD? 4.5 with the Known Class Option
  

  In this tutorial, we illustrate the use of the 멾nown class? feature in Latent GOLD 4.5 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 physician뭩 diagnosis of the patient as 멏epressed? or merely 멦roubled?, corresponding to 2 of the 3 latent classes.

• Tutorial 6B: Estimating a Random Intercept Regression Model
  

  In this tutorial, we illustrate the use of continuous factors (CFactors) to control for the 멿evel 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 4.5.

• Tutorial 7A: Latent Class Growth Model
  A reanalysis of a longitudinal data set on counts of epileptic seizures using latent class growth models. (Data source: Thall and Vail, 1990)
  
  

  In this tutorial, we show how to estimate a latent class growth model (Poisson mixture model). The results indicate that patients receiving the drug treatment were significantly more likely than the placebo group to improve (class 2 above) and significantly less likely to show no change (class 1) over their baseline seizure rate.

• Tutorial 7B: Latent Class Growth Model Using an Active Covariate

Latent GOLD Advanced Tutorials

• Latent GOLD 4.5 and IRT Modeling (.pdf)
  
  This tutorial shows that various IRT/latent trait models can be estimated with the Cluster and/or Regression modules in Latent GOLD Advanced (LGA), by including a continuous factor (CFactor) in the model. While LGA uses a somewhat different parameterization of these models, they can be easily transformed to obtain the traditional parameters (item locations, difficulties, threshholds, etc). The .pdf shows how to do this, by illustrating the equivalences between the LGA and standard IRT parameterizations. We also show how latent-class based IRT models can be defined using the DFactor module, as well as how these relate to standard IRT models.
  
  Data files:
  Download all data files for this example
  
  The .lgf files show how to use LGA to estimate various IRT and IRT mixture models. Simply open the .lgf files from within the (demo or standard) LGA program and select 'Estimate All' from the Model Menu. Several IRT models, appropriately labeled will be estimated, so you can view the Parameters and other Output files.
  
  Note: These .lgf files are designed to show how to set up these types of models. Since the data sets are small, they do not make good examples for 'mixture' IRT models (i.e., IRT models containing 2 or more latent classes). As a result, when estimating mixture variants of the IRT models, you may well encounter local solutions, even with many random startsets and many start iterations.

4. Latent GOLD? 4.5 User's Guide

The complete user's guide provides a comprehensive look at the program with easy-to-understand, step-by-step instructions and hundreds of screenshots. Download by chapter:

• Chapter 1: Overview (64 KB)
• Chapter 2: General Program Structure (105 KB)
• Chapter 3: Data Files and Formats (91 KB)
• Chapter 4: Working with Output (53 KB)
• Chapter 5: Basic Steps for Model Development (574 KB)
• Chapter 6: Model and Model Summary Output (744 KB)
• Chapter 7: Tutorials 1-4 (1.2 MB)
• Chapter 8: Additional Tutorials and Associated Data Sets (58 KB)
  
• Download the complete User's Guide (3.44 MB)
  

5. Latent GOLD? 4.5 Technical Guide: Basic and Advanced

This is the companion manual for Latent GOLD 4.5, an important work which provides a guide to the proper use of the program. It introduces the equations for all models, formulae for all statistics, describes all technical options, and discusses applications and proper interpretation of the output.

• Download the Technical Guide (557 KB)

SI-CHAID® 4.0 add-on to Latent GOLD 4.5 is now available!

Whenever covariates are available to describe latent classes obtained from Latent GOLD 4.5, the SI-CHAID 4.0 add-on can provide an especially valuable alternative treatment to the use of active and/or inactive covariates in Latent GOLD 4.5 under any of the following conditions:

• when many covariates are available and you wish to know which ones are most important
• when you do not wish to specify certain covariates as active because you do not wish them to affect the model parameters, but you still desire to assess their statistical significance with respect to the classes (or a specified subset of the classes)
• when you wish to develop a separate profile for each latent class
• when you wish to explore differences between 2 or more selected latent classes using a tree modeling structure
• when the relationship between the covariates and classes is nonlinear or includes interaction effects, or
• when you wish to profile order-restricted latent classes or discrete factors (Dfactors) - new in Latent GOLD 4.5

This option is illustrated in the following tutorials:

• Latent GOLD 4.5 Tutorial 4: Profiling LC Segments using the CHAID Option
• SI-CHAID 4.5 Tutorial 4: Using CHAID with Multiple Correlated Dependent Variables
• Also, see this article: 밃n Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables?, Magidson and Vermunt (2005)

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More information on SI-CHAID? 4.0:

1. SI-CHAID? 4.0 Demo version & Tutorials

Download a demo version of SI-CHAID? 4.0 and see the new version for yourself!

• Demo version

This tutorial demonstrates the use of the Latent GOLD link in SI-CHAID 4.0:

• Tutorial 4: Using CHAID with Multiple Correlated Dependent Variables (1.41 MB)

Use these other tutorials to explore the demo version:

• Tutorial 1: Beginning a CHAID Analysis (1.07 MB)
• Tutorial 2: Using SI-CHAID to identify profitable segments (935 KB)
• Tutorial 3: Using SI-CHAID with a Hold-out Sample (573 KB)

2. SI-CHAID? 4.0 User's Guide

The complete user's guide provides a comprehensive look at the program with easy-to-understand, step-by-step instructions and hundreds of screenshots.

• SI-CHAID? 4.0 User's Guide (1.59 MB)

3. SI-CHAID? 4.0 Graphical Interface

SI-CHAID®'s graphical interface makes it easy to analyze relationships between categories. View trees, gains charts, and tables on the same screen: