Publications by Joel Cadwell
Network Visualization of Key Driver Analysis
Whatever happened to those evaluations that your airline asked you to complete after taking a flight? They ask you for a number of ratings about buying your ticket, attributes of the plane, the service you received, and if you were satisfied, if you would recommend, and if you would fly again. The airline is certainly concerned about tracking cha...
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Gamification Quantification
Surveys become engaging when they become games, or at least, take on some of the characteristics of games. This is the argument made by those advocating the gamification of marketing research [http://researchaccess.com/2011/12/market-research-trends-2012-part-one-gamification/].Because it is a new and evolving approach to writing a survey, the...
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Archetypal Analysis
Thinking Strategically about Customer HeterogeneityIronically, market segmentation, whose motto is “one size does not fit all,” seems to rely almost exclusively on one definition of what constitutes a segment. Borrowing its definition from cluster analysis, segments are groupings of objects such that similar objects are gathered together...
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The Relative Importance of Predictors – Let the Games Begin!
What’s the one thing we need to do?Marketing researchers are asked this question frequently whenever they analyze customer satisfaction data. A company wishing to increase sales or limit churn wants to focus only on the most important determinants of those outcomes. Given the limitations imposed by the available customer survey data, thi...
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Halo Effects and Multicollinearity: Separating the General from the Specific
In the last post, The Relative Importance of Predictors, I showed how difficult it can be to assess the independent contribution that each predictor makes to the overall R-squared when the predictors are highly correlated. We spent some time looking at one example where the predictors were ratings from an airline satisfaction study. As is c...
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Structural Equation Modeling: Separating the General from the Specific (Part II)
As promised in Halo Effects and Multicollinearity (my last post), I will show how to run a confirmatory factor analysis in R to test our bifactor model. In addition, I will include a dependent variable and fit a structural equation model to illustrate how the general and specific components in a rating contribute to an outcome such as overall...
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Item Response Theory: Developing Your Intuition
Suppose that you accepted my argument from the last two posts on halo effects and bifactor models. As you might recall, I argued that when respondents complete rating scales, they predominating rely on their generalized impression with a more minor role played by the specific features that the ratings were written to measure. Consequently, we...
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A Brief Tip on Generating Fractional Factorial Designs in R
A number of marketing researchers use the orthoplan procedure in SPSS to generate fractional factorial designs. It is not surprising, then, that I received a number of questions concerning the recent article in the Journal of Statistical Software by Hideo Aizaki on “Basic Functions for Supporting an Implementation of Choice Experiments in R.�...
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How Microsoft Can Use Windows 8 to Dominate the Tech Industry
Windows 95 transformed the PC software market and established Microsoft as the dominant player. Can Microsoft do it again? Can it use the release of Windows 8 to elevate its entire brand image? More importantly to some of us, can Microsoft use statistical modeling to help it achieve its goal?Of course, the answer depends on what you mean by...
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Link to Item Response Theory Presentations Using R
After my post on item response theory, a number of you have asked for links to applications that provide R code. As I noted in that post, a good deal of work is being done in an area of research called patient-related outcome measurement (PROM or PROMIS with additional letters for information system). Since that post was publish...
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