How to Quickly Sift Through 3,050 Crosstabs and Find the Important Ones in Q
01 May 2019 | by Tim Bock

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

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Tips for Rebasing and Filtering Data in Q
29 April 2019 | by Chris Facer

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

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Guide for Working with Excel Files in Q
28 April 2019 | by Chris Facer

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,

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Testing Whether an Attribute Should be Numeric or Categorical in Conjoint Analysis
11 March 2019 | by Tim Bock

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

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Using Substitution Maps to Understand Preferences in Conjoint Analysis
10 March 2019 | by Tim Bock

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

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Using Indifference Curves to Understand Trade-offs in Conjoint Analysis
10 March 2019 | by Tim Bock

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

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The Efficient Algorithm For Choice Model Experimental Designs
10 March 2019 | by Justin Yap

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,

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Data Visualization for Conjoint Analysis
10 March 2019 | by Tim Bock

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,

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Algorithms to Create your Choice Model Experimental Design
10 March 2019 | by Tim Bock

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

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How Good is your Choice Model Experimental Design?
27 February 2019 | by Jake Hoare

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

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12 Techniques for Increasing the Accuracy of Forecasts from Conjoint Analysis
27 February 2019 | by Tim Bock

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.

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Experimental Design for Conjoint Analysis: Overview and Examples
27 February 2019 | by Tim Bock

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

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What Sample Sizes do you Need for Conjoint Analysis?
27 February 2019 | by Tim Bock

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

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Writing a Questionnaire for a Conjoint Analysis Study
27 February 2019 | by Tim Bock

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

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Main Applications of Conjoint Analysis
27 February 2019 | by Tim Bock

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

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Conjoint Analysis: The Basics
27 February 2019 | by Tim Bock

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

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How to Identify Relevant Variables for Market Segmentation
05 February 2019 | by Tim Bock

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

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How to Write “Golden Questions” for Market Segmentation
05 February 2019 | by Tim Bock

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

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How to Work Out the Number of Segments For a Market Segmentation
05 February 2019 | by Tim Bock

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:

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How to Reuse Code Frames in Q
22 January 2019 | by Oliver Harrison

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

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How to Code Other/Specify Responses in Q
22 January 2019 | by Oliver Harrison

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

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How to Code an Open-Ended Question into a Single Response Question in Q
22 January 2019 | by Oliver Harrison

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

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How to Code Open-Ended Responses with Multiple Mentions in Q
22 January 2019 | by Oliver Harrison

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

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How to Code an Open-Ended Question into a Multiple Response Question in Q
22 January 2019 | by Oliver Harrison

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

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