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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: Latent Class Cluster models; Discrete
Factor (DFactor) models; Latent Class Regression models. The Advanced
option includes additional advanced features for continuous latent
variables (CFactors), multilevel modeling, and survey options for
complex sample data.
- Full windows implementation - point and click
- Interactive graphics provide new insights into data and powerful
model diagnostic capabilities
- Flexible model structures can handle variables of different
- Automatic generation of sets of random starting values
- Fast, efficient maximum likelihood and posterior mode estimation
based on EM and Newton Raphson algorithms
- Use of Bayes constants to eliminate boundary solutions
- Bivariate residual diagnostic for local dependencies
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
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.