Webinar: Avoiding Common Pitfalls in Conjoint Analysis

Take advantage of decades of experience working with Sawtooth Software customers from our technical support team to learn about common pitfalls and how to avoid them. This webinar is geared towards beginners and those with a few studies under their belts. We will cover topics from attributes and levels, experimental designs, to fielding your survey and running analysis.

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New Technical Paper: Consistency Cutoffs to Identify "Bad" Respondents in CBC, ACBC, and MaxDiff

Over the last few years, the incidence of bad respondents is increasing. Conjoint analysis and MaxDiff have a fit statistic called RLH when using HB estimation that helps identify bad respondents. As long as the conjoint or MaxDiff questionnaire has enough questions relative to the number of levels or items in the study, random responders can be identified with a high degree of accuracy. This paper, authored by Bryan Orme of Sawtooth Software, gives instructions for generating random data to identify the RLH cutoff that has a high probability of identifying random respondents. It is available in the middle of the General Conjoint section of the website's Technical Papers library.

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Testing for Managerial Significance

You’ve often seen researchers report a finding as “statistically significant.”  Even if the finding is very unlikely to occur by chance, is the difference in ratings or size of the regression effect big enough to matter a hill of beans to a decision-maker? 

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We've updated the MaxDiff Analyzer!

With a new look, new home, and new features, there's a lot to love about the new MaxDiff Analyzer.

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How MaxDiff Is a Better Measurement Technique than Rating Scales

At the April 2019 Quirk’s Event in Chicago, David Hengehold (P&G) and Megan Peitz (Numerious) showed how MaxDiff (best-worst scaling) often is a better survey measurement technique vs. ratings scales. David shared how P&G uses MaxDiff with great results. In Chicago, David & Megan showed an interactive MaxDiff survey demo for Ice Cream preferences, very similar to this:

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