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In this blog post, I describe the Efficient algorithm for generating choice model designs. This algorithm is used for generating choice model designs with partial profiles,…

While choice-based conjoint analysis represents one of the more sophisticated techniques used in market research, presentation of its results commonly consists only of a simulator,…

In a stated preference discrete choice experiment, respondents are asked a number of questions. Each question asks them to choose between a number of alternatives…

Today, you can produce a wide range of choice model experimental designs with numerous different algorithms. But with all this design diversity, how do you…

Choice experiments, also known as choice-based conjoint (CBC), are widely used for predicting the performance of new products and changes to products’ designs and portfolios.…

This post introduces the key concepts in designing experiments for choice-based conjoint analysis (also known as choice modeling). I use a simple example to describe…

Working out the sample size required for a choice-based conjoint study is a mixture of art and science. What makes it tricky is that the…

The hard bit of designing a choice-based conjoint analysis (choice modeling) study is creating the experimental design. However, there are a few others parts of…

Ready to dive further into conjoint analysis? In this post I describe the main applications of choice-based conjoint analysis (choice modeling; CBC). If you haven’t…

Choice-based conjoint analysis is a technique for quantifying how the attributes of products and services affect their performance. It is used to help decision makers…

This page lists the key frameworks and processes for identifying relevant variables to use when segmenting a market. The best way to identify relevant segmentation…

Golden questions are questions used to allocate people to segments. They are also known as self-selection questions. The main applications of golden questions are: As discovery…

When segmenting a market, a practical challenge is to work out the number of segments. There are eight approaches to choosing the number of segments:…

Coding open-ended and ‘other/specify’ responses can often be a tedious task for market researchers. In this post, I will instead show you how to reuse…

Coding open-ended responses can often be a tedious and time-consuming task for market researchers, especially when you include an ‘Other/specify’ option and then need to…

Coding open-ended responses can often be a tedious task for market researchers. In this post, I will show you how to easily code a single…

Coding open-ended responses can often be a tedious task for market researchers. In this post, I will show you how to easily code multiple open-ended…

Coding open-ended responses can often be a tedious task for market researchers. In this post, I will show you how to easily code a single…

Box plots are a tidy way to illustrate statistical properties of a set of numeric data. A box plot will typically show you the median…

Q enables you to flexibly mix data from different tables. The mixing process creates a new table as an R Output. Consider the example table…

Principal Components Analysis (PCA) is a technique for taking a large number of variables and creating a new, smaller set of variables. These aim to…

In constructing crosstabs, it can be helpful to see more than one question on an individual axis. Other instances may require labels to be “nested”,…

Having the ability to connect to a SQL database can provide greater opportunities for analyzing data sources. In this blog I will show you how…

Automatically sorting your tables and charts is a key thing researchers like to do. Q has lots of ways to do this for you automatically…