In Q4.2 (currently in Beta) you can customize the names of statistics for selected tables (Edit | Table/Plot Options | Output Text) or for the entire project (Edit | Project Options | Output Text). For example, change:

• Average to NPS.

• Column n to Base.

• Average to 均值 (if you are Chinese).

Correspondence analysis creates plots showing the relationship between multiple variables.

Q automatically works out the right type of map based on the data you choose to analyse.  It can create maps showing the rows and columns of a table, between individual respondents in the raw data and between three or more sets variables (multiple correspondence analysis). 

The maps are exported to Microsoft Office applications as fully-editable charts.  You can export either the traditional scatterplot or a moon plot.

Create maps by clicking on the Create menu and selecting Map.  

A series of online tutorials explain how to create maps.  From the Help menu, select Online Training, login and go Multivariate Techniques and Maps.

Max-Diff, sometimes known as a best-worst scaling, presents people with short lists of attributes and asks which they like best and which they like worst.  This is done multiple times, with different sets of attributes each time.  The sets of attributes are determined from an experimental design.

You can analyse Max-Diff questions in Q just like any other type of question, such as by crosstabbing with other questions, trees, latent class analysis, smart tables, mixture models, individual-level parameters and mapping. 

Max-diff and anchored max-diff experiments are setup in Q as Ranking questions, unless they are best-worst conjoint, in which case they are setup as an Experiment.  

This feature is only available in Q4; contact support@q-researchsoftware.com for a free upgrade.

Trees show the relationship between two sets of questions as an  upside-down tree.

While most tree-based methods, such as CHAID and CART only predict a single categorical or numeric variable, Q’s approach can predict all the different types of data: numeric, categorical, rankings, multiple response, conjoint and other types of experiments.   

The figure below shows Q’s graphical representation of segmentation, with bars showing categorical data and histograms representing numeric data.  

In Q4, trees are created by clicking on the Create menu, selecting Segments, selecting Split by questions (tree) and choose the questions to split by.  Contact support@q-researchsoftware.com for a free upgrade to Q4.

An online tutorial explains how to create trees.  From the Help menu in Q, select Online Training, sign in, then go to Multivariate Techniques, Segments and Trees.

Latent class analysis finds groups in data.  It is most commonly used for market segmentation. 

Latent class analysis is a big improvement on cluster analysis.  It automatically deals with missing data; even if there are lots of “don’t know” responses, each person is still assigned to a segment.  It accommodates all the different types of data: numeric, categorical, rankings, multiple response, conjoint and other types of experiments.   

The figure below show’s Q’s graphical representation of segmentation, with bars showing categorical data and histograms representing numeric data. 

In Q4, latent class analysis is conducted by clicking on the Create menu and selecting Segments; contact support@q-researchsoftware.com for a free upgrade to Q4.

An online tutorial explains how to use latent class analysis.  From the Help menu in Q, select Online Training, sign in, then go Multivariate Techniques, Segments and Latent Class Analysis.