Overview

SI-CHAID is a 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.

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Features

Tree Diagram

Tree nodes have detailed information which may be customized using the Tree Node Display panel. From this view the SI-CHAID model may be modified by growing, pruning, or restoring previously saved tree branches or by rearranging category groupings. Operations on the tree take place on the “current” node which is the highlighted (active) node. Clicking on a node makes it the current node. Multiple Tree Diagram windows may be open, each displaying different node contents or other customized views.

Gains Chart

The Gains chart provides various tabular representations of the terminal nodes (segments) from the SI-CHAID tree which may be customized using the Gains Items panel. Multiple Gains Chart windows may be open, each with its unique customized appearance.

Table

The table provides tabulation of a single predictor by the dependent variable. The cell entries can be customized using the Table Items panel.

Linking with Latent GOLD and/or LG Choice

Each of our flagship modeling tools Latent GOLD 4.5 and Latent GOLD Choice 4.5 provide a direct link to SI-CHAID 4.0. With this option, a CHAID Definition (.chd) file is automatically generated immediately following model estimation which can then be used as input to SI-CHAID 4.0. Learn More

Read the groundbreaking article “An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables”, Magidson and Vermunt (2005). and follow along with Tutorial 4.

Tutorials

All sample datasets and sample .chd files used the tutorials below are downloaded to your computer when you install the demo version of SI-CHAID.

Tutorial 1: Beginning a CHAID Analysis download PDF          + overview - overview In this Tutorial we illustrate the basic functions and uses of SI-CHAID. We will show how to set up an analysis (.chd) file and grow a CHAID tree by using the standard CHAID algorithm, which is designed for a dichotomous or nominal dependent variable. In our example, we show how to determine CHAID segments that differ on response rates, and how gains charts can be used to predict the expected response from mailing/ targeting the most responsive segments.

Tutorial 2: Using SI-CHAID to identify profitable segments download PDF          + overview - overview This tutorial shows how to use the CHAID ordinal algorithm to segment based on profitability scores. We will again use the magazine subscription data set, subscribe.sav, used previously in Tutorial 1. However, our dependent variable will now be RESP3, coded 1 (paid responder), 2 (unpaid responder) and 3 (nonresponder). We'll compare a default nominal CHAID segmentation of RESP3 to the ordinal CHAID analysis that takes into account the gain (or loss) associated with each response group. For simplicity, we utilize the SI-CHAID option settings used in Magidson (1993).

Tutorial 3: Using SI-CHAID with a Hold-out Sample download PDF          + overview - overview Sometimes cases on the analysis file are randomly assigned to a ‘hold-out’ sample and not used in the development of the segmentation tree. Instead, such cases are reserved for the purpose of ‘validating’ the tree. In this tutorial we utilize the data file holdout.sav to illustrate the use of SI-CHAID in this way. In particular, from each dependent category (‘paid respondents’, ‘unpaid respondents’ and ‘nonresponders’) we randomly assigned each case in the ‘subscrib.sav’ file to one of two equally likely groups by generating the variable SAMPLE (1=test, 2 = holdout).

Tutorial 4: Using CHAID with Multiple Correlated Dependent Variables download PDF          + overview - overview Often a segmentation is desired that is predictive of not one but multiple criteria. For example, in database marketing, dependent variables might include 1) response to the most recent mailing (responder vs. nonresponder), 2) response to past mailings, 3) the amount spent, 4) profitability, and possibly others. Magidson and Vermunt (2005) described an extended CHAID algorithm for such situations, which has been implemented in SI-CHAID 4.0. A copy of that article, entitled An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables, is included with the SI-CHAID 4.0 manual, and may also be obtained in the Articles section.

Related Products

Link SI-CHAID with Latent GOLD and/or LG Choice

Using SI-CHAID, a CHAID analysis may be performed following the estimation of any LC model in Latent GOLD (or LG Choice) to profile the resulting LC segments based on demographics and/or other exogenous variables (Covariates). Learn More

Latent GOLD

Latent GOLD 4.5 is a powerful latent class and finite mixture program. Latent GOLD contains separate modules for estimating three different model structures -- LC Cluster models, Discrete Factor (DFactor) models, and LC Regression models. Learn More

LG Choice

LG Choice 4.5 is a specialized program designed strictly for estimating discrete choice models. Learn More