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Do you despise data cleaning? You’re not alone. Too many researchers find data cleaning to be time-consuming, tedious, and tiring. However, starting your analysis with…

Popular amongst researchers is the importance vs. performance scatterplot. This is where you have a series of items that you place in a grid, where…

Snake plot is the term used to describe a line chart on its side. So rather than the lines running left-right (x-axis) they run up-down…

Working with dates and times can be challenging at the best of times, let alone when they are in the wrong time zone or format,…

In recent posts we’ve discussed how you can reference source tables using R in Q to manipulate the statistics into a new custom table. In…

After you make a table, you might like to modify the contents of the cells. Perhaps you want to make the table easier to read…

Many researchers like to suppress statistics that have small sample sizes. This often prevents clients from making false interpretations from the data. In this post,…

Have you been in a situation where you need to update a KPI summary, and you want everything within the one table? This is particularly…

It can sometimes be useful to calculate the amount of time, be it in minutes, hours, or even seconds, between two points in time.  In…

Most market researchers know the pain of having to hunt through huge numbers of crosstabs to find the interesting ones. In my younger days (shhh…),…

There are often occasions in market research when you look at your survey data and find that in order to analyse certain questions properly, you’ll…

Want to save a heap of time narrowing down thousands of crosstabs to just the ones you need? This post describes which buttons to push…

Q users are used to setting the base of their tables by applying filters. However, there are other handy ways to rebase data that you…

If you can get an SPSS file for your study then Q will usually be able to set up all the data for you. However,…

Choice-based conjoint (CBC) studies usually specify a fixed number of levels for each attribute. The resulting attribute then becomes categorical. But that doesn’t mean you…

Modern tools for analyzing conjoint analysis, such as hierarchical Bayes, produce rich data showing preferences for each person in a market. The main deliverable from…

Indifference curves are a way of showing relative preferences for quantities of two things (e.g., preferences for price versus delivery times for fast food). This…

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…