# Mixed-Mode Tree

Related Online Training modules | |
---|---|

Trees | |

Generally it is best to access online training from within Q by selecting Help > Online Training |

A *Mixed-Mode Tree* can be created which predicts the **Questions to analyze** using predictor questions.

## Contents

## Example

The image below shows a tree which predicts brand choice and number of long-distance phone calls (questions to analyze) by income, age and education (questions to split by).

## How to create a Mixed-Mode Tree

- The first step depends on which version of Q you are using:
- In Displayr, select
**Insert > Machine Learning > Mixed-Mode Tree**. - In Q
**Create > Classifier > Mixed-Mode Trees** - Older versions of Q:
**Create**and Segments- Select
**splitting by questions (tree)**

- In Displayr, select
- Select the questions to be used to form the segments in the
**Questions to analyze**dialog box. - Select the predictor variables:
- Select the variables into the lower box (that is, the box that is blow the box for
**Questions to analyze**.

- Select the variables into the lower box (that is, the box that is blow the box for
- If necessary, modify the default options. Note that:

- By default Q will automatically select the number of segments using the
*Bayesian information criterion*. You can alternatively specify a specific number of segments by selecting the**Manual**option. Alternatively, you can select a different information criteria by clicking**Advanced**. - The Question Type of the questions that are analyzed determines how the latent class model is conducted. For example, when analyzing a Pick One - Multi any scale points are ignored and Q treats the data as categorical; if it is converted to a Ranking Q focuses on understanding relativities; if it is converted to Number - Multi Q treats the data as being numeric.

- By default Q will automatically select the number of segments using the

## Buttons, Options and Fields

Refer to Segments for a description of the buttons, options, and fields prior to Q5.0.

**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.

**Predictors**This is the box beneath **Questions to analyze**.

**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.

## See also

- Latent Class Analysis for a discussion of interpretation of outputs (each level of the tree is a Latent Class model).
- Missing Values in Latent Class Analysis, Mixed-Mode Tree, and Mixed-Mode Cluster Analysis
- Statistical Model for Latent Class Analysis, Mixed-Mode Tree, and Mixed-Mode Cluster Analysis
- How to Allocate Observations to Segments in Excel
- For a very general discussion of how to interpret trees, which is focused more on Latent Class Analysis, see How to Interpret Trees.
- The difference between Trees, CHAID, CART and other tree-based models

Further reading: Latent Class Analysis Software