Member Training: Cluster Analysis–Hierarchical and KMeans

by Karen Grace-Martin

Cluster analysis classifies individuals into two or more unknown groups based on a set of numerical variables.

It is related to, but distinct from, a few other multivariate techniques including discriminant Function Analysis, which classifies individuals into known groups, factor analysis, which groups variables based on individual’s responses, and Latent Class Analysis, which groups individuals based on categorical variables.

In this webinar, we’ll discuss the different types of cluster analysis, and when each one is most useful.  We’ll delve into some of the options for measuring similarity among individuals, and show some useful plots.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Not a Member Yet?

It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.

Just head over and sign up for Statistically Speaking.

You’ll get access to this training webinar and 85+ other stats trainings — plus the expert guidance you need to progress with live Q&A sessions and an ask-a-mentor forum.

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