Sawtooth Software: The Survey Software of Choice

Quick and Easy Power Analysis for Choice Experiments

Four clients have asked about this topic in the past month, so perhaps it's worth a post. It turns out that there is a quick way to use Lighthouse Studio to do power analysis for choice-based conjoint experiments.

Say for example that a client wants a choice-based conjoint experiment with four 3-level and one 2-level attributes. The client plans to show each respondent 10 tasks each with two alternatives and a none and wants to know what sizes of significant coefficients (utilities) the design will be able to detect (at a given level of confidence and power). To answer the client’s questions, follow these four steps:

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Call for Papers and Presentations: Sawtooth Software Conference 2019

The 2019 Sawtooth Software Conference will be held September 25-27, 2019 in San Diego, California at the Hilton San Diego Resort & Spa. It is held only once every 18 months and 175 to 250 people are expected. 

We're looking to increase diversity in the program in terms of demographics, industries, and new perspectives.  Even if you’ve never presented at a conference before, we welcome your proposal!  If you know of great speakers in underrepresented groups, please let us know and we'll reach out to invite them to submit.

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A Discrete Choice Take on Halloween: Results

As promised in my fun Halloween article, we have the results!  With a significantly smaller network than @fivethirtyeight, our sample size is on the lower end at n=73. That being said - MaxDiff does an amazing job with small sample sizes and the data proves it!  Comparing our n=73 results to the n=8,300+ of @fivethirtyeight, we arrive at the same top 5 candies. Now we just have to argue over who is right!

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MaxDiff Simulator, TURF Analysis, and Export to PDF now available in Discover

Our team has been hard at work making Discover better. Discover now has a MaxDiff Simulator, a powerful TURF analysis tool for MaxDiff, and the ability to export your survey to PDF. 

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With HB, the Goal Is NOT to Maximize Individual-Level Fit (RLH)

Hierarchical Bayes (HB) estimates high-quality individual-level utilities for CBC and MaxDiff (Best-Worst Scaling) despite sparse data for each individual. HB’s goal is not to maximize the fit to the individual-level choices (the RLH statistic, or Root Likelihood). This could lead to overfitting and the utilities may do poorly in predicting new choices (such as holdouts or real market choices) outside the scope of the data used in utility estimation.

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