Latent GOLD software description
Latent GOLD is a powerful latent class and finite mixture program. Latent GOLD contains separate modules for estimating three different model structures:
- LC Cluster models
- DFactor models
- LC Regression models
Latent GOLD 4.5 has a Basic and Advanced version.
Differences between Latent GOLD 4.0 and 4.5 GUI Module
* On the output tab, slightly different names are used for the classification and prediction options.
* In version 4.5, joint classification information is also provided in the output file when classification information is written to a file
* In version 4.5, true ML estimates for missing values categorical variables are also provided on models with local dependencies.
* In addition, version 4.5 uses memory somewhat more efficiently than version 4.0. As a result, some large models with many parameters that may have run out of memory in version 4.0, may not run out of memory in version 4.5.
More information on Latent GOLD 4.5:
1. Latent GOLD® 4.5: 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.
- 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:
- 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.
- 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:
-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’
3. Latent GOLD 4.5 Demo version & Tutorials
For all software, guides and other documentation, please check the website of www.statisticalinnovations.com
4. 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
Latent GOLD Syntax module
Discover the full power of the Advanced versions Latent GOLD® and Latent GOLD Choice with the new LG-Syntax TM module. This new Syntax module will allow you to estimate many new models including hidden Markov models, score new data files, submit runs in batch-mode, impute missing data, include only selected cases and obtain new output. Do not miss it!
LG-SyntaxTM Module Features:
- Run in interactive or batch mode
- Use saved models to score new cases
- Simulate data from any model
- Perform power calculations
- Impute (fill in) missing values in input data file
- Obtain additional output without re-estimating model
- Powerful / more flexible control over parameter restrictions
- Seamless data fusion, regress with several dependent variables to develop
- hybrid LC Choice / rating models
- hybrid MaxDiff / rating models
- Scale-factor adjusted LC choice models
- Latent path models
- Hidden Markov models
Ordering
For price information and ordering, please visit the Prices and Ordering Page
Manufacturer page
Science Plus Group is a distributor for this product. You can also visit the Statistical Innovations website, the manufacturer of this product.
