## Introduction

This article describes how to use the Segments option in versions of Q prior to Q5.0. Please refer to Segmentation for an overview of methods for conducting segmentation in Q5 and beyond.

## Requirements

A version of Q prior to version 5.0

## Method

## Create > Segments

In older versions of Q, the main segmentation algorithms are accessed by **Create > Segments**. This forms segments and displays them in the form of a *tree* in the Outputs Tab. This dialog box is used to create three related but distinct statistical analyses:

- Latent Class Analysis
- Trees
- Mixture Models for Regression

## Buttons, Options and Fields

**Questions to analyze **The questions to use to form the classes. Grid questions cannot be selected (you need to first change their Question Type to another type.

**Form segments by...**

**splitting by individuals (latent class analysis, cluster analysis, mixture models)**Uses latent class analysis, cluster analysis or mixture models to segment individuals.

**splitting by questions (tree)**Forms a tree, splitting according to the selected questions.

**Number of segments per split**

**Automatic**Evaluates segments in the specified range, starting with**Minimum**and finishing when either the*Information Criterion*increases (see**Advanced**), or, the**Maximum**is reached.

**Manual**The specified**Number**of classes are created. When creating a tree, Q uses this setting for each*split*('branch') of the tree. (You can specify the**Maximum number of tree levels**and the**Minimum node size per split**in the**Advanced**options.

## Advanced

**Iterations **The number of iterations of the estimation algorithms.

**Initial classification** A question that is used as a starting point for latent class analysis.

**Starting values **Initial values to be used in the first iteration of the algorithm.

**Number of starts **Number of times that the latent class algorithm is run for a given number of classes. Cases are randomly allocated to segments each time (a common seed is used to that you will get the same results each time you run it, unless you change the input data.)

**Number of draws **Number of draws used in computing the simulated likelihood (for **Conjoint **and **Ranking **questions with non-**Finite **distributions).

**Draw generation method **The method used for pseudo-random generation for computation of the simulated likelihood.

**Maximum number of tree levels **The maximum size of the tree (the tree may be smaller if the **Information Criteria** fails to support a split as being appropriate).

**Minimum node size to split** Nodes on the tree (i.e., segments) are not split if their sample size is smaller than this number.

**Objective** This setting can be used to make Q mimic the behavior of other data analysis tools (see also Statistical Model for Latent Class Analysis, Mixed-Mode Tree, and Mixed-Mode Cluster Analysis):

**Mixture**uses a mixture model (e.g., latent class analysis), where units of analysis are assigned to segments probabilistically. This is the standard assumption in modern work on classification and is the setting used as the default in all latent class analysis programs, including Q (when**Form segments by**is set to**splitting by individuals (latent class analysis, cluster analysis, mixture models)**.**Discrete**uses the latent class log-likelihood but assigns units of analysis discretely. For example, if a unit of analysis has a 40% probability of being assigned to class 1 and 30% to class 2 and class 3, the unit of analysis is assigned to class 1 at each stage of the estimation process. This is used in Q when**Form segments by**is set to**splitting by splitting by questions (tree)**.**Clustering**uses the*Classification Likelihood*, where units of analysis are assigned to one-and-only-one segment and segments are assumed to all be of the same size.

**Model selection criterion** Rule used to select the number of classes when **Number of segments per split** is set to **Automatic** (see **Options**). Determines which of the Information Criteria is used. The various information criteria that are used are ordered from the one that will create the biggest trees (the AIC) through to the one that will create the smallest trees (CAIC). There is no clear statistical theory to guide the choice of information criteria.

**Question-specific assumptions**

**Weight**Modifies the contribution of a particular question in determining the final solution by modifying its weight.

**Distribution**See Distribution.

## Next

How to do Latent Class Regression

How to Do Mixture Models for Regression

## Comments

0 comments

Article is closed for comments.