Segmentation & Cluster Analysis Certification (Displayr)

This learning path provides a comprehensive introduction to segmentation and cluster analysis using Displayr. Segmentation is a powerful analytical technique used to identify distinct groups within a population based on shared attitudes, behaviors, or characteristics, enabling deeper insights and more targeted strategies.

Throughout the course, learners will explore the key methodologies used in modern segmentation studies. The learning path begins with a foundational overview of segmentation concepts and when segmentation is most useful in research. Learners will then dive into the primary segmentation techniques used in Displayr: Latent Class Analysis, k-means Cluster Analysis, and Hierarchical Cluster Analysis.

After learning how segments are created, the course focuses on how to interpret, explore, and visualize segments to uncover meaningful insights. Finally, learners will learn how to build a segmentation typing tool, allowing new respondents to be classified into segments using a small set of “golden questions.”

Modules include:

• Introduction to Segmentation/Cluster Analysis

• Latent Class Analysis

• k-means Cluster Analysis

• Hierarchical Cluster Analysis

• Exploring and Visualizing Segments

• Creating a Segmentation Typing Tool

Learners who successfully complete this learning path will receive a certificate in conducting segmentation and cluster analysis in Displayr, demonstrating their ability to build, interpret, and apply segmentation models in real-world research.

MaxDiff Analysis Certification (Displayr)

This learning path provides a comprehensive introduction to MaxDiff (Maximum Difference Scaling) using Displayr. MaxDiff is a powerful survey technique for measuring the relative importance, preference, or appeal of a set of items, overcoming the limitations of traditional rating scales where respondents rate everything as "important."

Throughout the course, learners will explore the full MaxDiff workflow from first principles. The learning path begins with a foundational overview of what MaxDiff is and when it's the right tool for a research question, followed by the practical mechanics of building a MaxDiff study: designing a statistically sound experiment and working with the different data formats MaxDiff studies can arrive in.

Learners then move into core analysis — counts analysis, ranking experiments, Hierarchical Bayes, and Latent Class Analysis in Displayr — before advancing to more sophisticated techniques: comparing and combining models, incorporating covariates, alternative logit specifications, multi-class HBA, and anchored MaxDiff designs.

Modules include:

• Introduction to MaxDiff

• MaxDiff: Experimental Design

• MaxDiff Data Formats

• MaxDiff Analysis in Displayr

• MaxDiff: Advanced Topics

Learners who successfully complete this learning path will receive a certificate in MaxDiff analysis in Displayr, demonstrating their ability to design, run, and interpret MaxDiff studies in real-world research.