||Latent GOLD 功能强大的潜在类别和有限混合建模分析软件
Latent GOLD 4.5: 高级模块
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 ‘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 4.5.
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 ‘design effect'
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