Blog Archives

Customer Satisfaction and Loyalty: Structural Equation Model or One-Dimensional Dissonance

May 9, 2014
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Customer Satisfaction and Loyalty: Structural Equation Model or One-Dimensional Dissonance

Causal thinking is seductive. Product experience comes first, then feelings of satisfaction, and finally intentions to continue as a customer. Although customer satisfaction and loyalty data tend to be collected all at one time within the same question...

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Warning: Clusters May Appear More Separated in Textbooks than in Practice

March 23, 2014
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Warning: Clusters May Appear More Separated in Textbooks than in Practice

Clustering is the search for discontinuity achieved by sorting all the similar entities into the same piles and thus maximizing the separation between different piles. The latent class assumption makes the process explicit. What is the source of variat...

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Context Matters When Modeling Human Judgment and Choice

January 28, 2014
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Context Matters When Modeling Human Judgment and Choice

Herbert Simon was succinct when he argued that judgment and choice "is shaped by a scissor whose two blades are the structure of the task environment and the computational capabilities of the actor" (Simon, 1990, p.7). As a marketing researcher, I take...

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Metaphors Matter: Factor Structure vs. Correlation Network Maps

January 17, 2014
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Metaphors Matter: Factor Structure vs. Correlation Network Maps

The psych R package includes a data set called "bfi" with self-report ratings on 25 personality items along a 6-point agreement scale. All the details are provided in the documentation accompanying the package. My focus is how to represent the correlat...

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Finding the R community a barrier to entry, Python looks elsewhere for lunch

January 10, 2014
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Tal Yarkoni's post on "The homogenization of scientific computing, or why Python is steadily eating other languages' lunch" is an enjoyable read of his transition from R to Python. He makes a good case, and I have no argument with his reasoning or the ...

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An Introduction to Statistical Learning with Applications in R

January 6, 2014
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An Introduction to Statistical Learning with Applications in R

Statistical learning theory offers an opportunity to those of us trained as social science methodologists to look at everything we have learned from a different perspective. For example, missing value imputation can be seen as matrix completion and rec...

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Latent Variable Mixture Models (LVMM): Decomposing Heterogeneity into Type and Intensity

December 19, 2013
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Adding features to a product can be costly, so brands have an incentive to include only those features most likely to increase demand. In the last two posts (first link and second link), I have recommended what could be called a "features stress t...

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The Complexities of Customer Segmentation: Removing Response Intensity to Reveal Response Pattern

December 15, 2013
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The Complexities of Customer Segmentation: Removing Response Intensity to Reveal Response Pattern

At the end of the last post, the reader was left assuming respondent homogeneity without any means for discovering if all of our customers adopted the same feature prioritization. To review, nine features were presented one at a time, and each time res...

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Feature Prioritization: Multiple Correspondence Analysis Reveals Underlying Structure

December 10, 2013
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Feature Prioritization:  Multiple Correspondence Analysis Reveals Underlying Structure

Measuring the Power of Product Features to Generate Increased DemandProduct management requires more from feature prioritization than a rank ordering. It is simply not enough to know the "best" feature if that best does not generate increased demand. W...

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Maximizing Return from Every Item in the Marketing Research Questionnaire

December 3, 2013
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Maximizing Return from Every Item in the Marketing Research Questionnaire

Consumers will not complete long questionnaires, so marketing research must get the most it can from every item.  In this post, we look into the toolbox of R packages and search for statistical models that enable us to learn a great deal about eac...

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