SI-CHAID option in Latent GOLD

SI-CHAID is a separate, stand alone program for performing CHAID (CHi-squared Automatic Interaction Detector) analyses. Results can be displayed simultaneously in the form of an intuitive tree diagram, crosstabulations, and a gains chart summary. Learn More

With this option, a CHAID (CHi-squared Automatic Interaction Detector) analysis may be performed following the estimation of any LC model in Latent GOLD 4.5, to profile the resulting LC segments based on demographics and/or other exogenous variables (Covariates). By selecting 'CHAID' as one of the output options, a CHAID input file is constructed upon completion of the model estimation, which can then be used as input to SI-CHAID 4.0.

This option provides an alternative treatment to the use of active and/or inactive covariates in Latent GOLD 4.5. In addition to standard Latent GOLD output to examine the relationship between the covariates and classes/DFactors, SI-CHAID provides a tree-structured profile of selected classes/DFactors based on the selected Covariates. In addition, chi-square measures of statistical significance are provided for all covariates (Latent GOLD does not provide such for inactive covariates). Either the standard (nominal) algorithm or the ordinal CHAID algorithm may be used to profile the classes, the latter useful with order-restricted classes or the levels of a DFactor to take into account the ordered nature of the classes (DFactor levels).

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

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SI-CHAID is a separate, stand alone program. Learn More