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What is latent class modeling and why is it important?
Latent class (LC) modeling, also known as Finite Mixture Modeling, provides a powerful way of identifying latent groups (types) for which parameters in a specified model differ. Latent GOLD® , the most windows-friendly program for latent class modeling, focuses on the three most important kinds of statistical models used in practice - cluster, factor and regression.
How does LC analysis, as implemented by Latent GOLD® 3.0, compare with traditional procedures?
CLUSTER - Traditional clustering procedures (K-Means, hierarchical clustering) are not model-based and therefore quite limited. LC clustering consistently recovers true structural groups where the traditional algorithms fail. See article: Latent Class Cluster Analysis.
1. Adequate
Assumptions
K-Means makes assumptions such as local independence or equal within class variance that often conflict with the real world.
Latent GOLD® 3.0 can be used to test these and relax them if they are found to be invalid. This yields much better and simpler (=less clusters) solutions in practice.
Back to Cluster Method table
2. Different Scale Types
Latent GOLD® 3.0 allows for variables to be nominal, ordinal, continuous, count or any mixture of these, any of which may contain missing values. Different scale types are handled by automatically specifying the appropriate distribution.
Back to Cluster Method table
3. Simultaneous Covariate-Based Descriptions
After doing a traditional clustering, discriminant analysis or cross-tabs are often used to describe the resulting clusters, an approach confounded by misclassification and other errors. Latent GOLD® 3.0 allows the inclusion of covariates for simultaneous parameter estimation (based on indicators) and description based on covariates. Covariate based prediction/classification is now available so that new cases for which indicators are not present may be classified based solely on the covariates. Covariates can be continuous as well as categorical.
Back to Cluster Method table
4. Optimal determination of number of clusters
In traditional clustering procedures, rules of thumb and ad-hoc guess-work are used to determine the number of clusters. Since LC is based on a statistical model, statistics are available to help determine the number of clusters.
Back to Cluster Method table
REGRESSION - Traditional regression assumes homogeneity across an entire population, which does not allow for the existence of different segments. LC or mixture regression involves estimating a regression model under the assumption that the regression coefficients differ across unobserved (latent) segments, yielding improved predictions.
1. Accounts for Heterogeneity
Traditional regression programs assume that the model holds true for the entire population. Latent GOLD® explores whether model heterogeneity can be explained by unobserved latent segments.
Back to Cluster Method table
2. Allows differing dependent variable scale types
Latent GOLD® 's mixture regression module is in the General Linear Models (GLM) framework. It allows for dependent variables that are dichotomous, nominal, ordinal, continuous or count. Just select the scale type and the appropriate model is used (logit, multinomial logit, ordinal logit, normal, poisson or binomial count).
Back to Cluster Method table
3. Repeated measures structure
Repeated measures structure allows for latent class growth models, latent class conjoint models, Rasch type IRT models, survival models, and many other repeated measure type applications.
Latent GOLD® 3.0 uses a non-parametric random-coefficient model - the random effects are not assumed to come from a multivariate normal distribution.. Besides less restrictive assumptions, the LC regression model has the advantage of being extremely fast compared to parametric random-coefficient models, when the outcome variable is non-normal. There are several special outputs for this: mean and standard deviation of coefficients, as well as individual effects.
Back to Cluster Method table
4. Inclusion of Covariates
Latent GOLD® allows the inclusion of covariates for simultaneous estimation (based on indicators) and description based on covariates. Covariate based prediction is also available so that new cases for which indicators are not present may be classified based solely on the covariates.
Back to Cluster Method table
5. Monotonicity Restrictions
The predictor effects may be restricted to be 0, monotonic increasing, monotonic decreasing, or equal across 2 or more latent segments.
Back to Cluster Method table
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