May 2014 Member Webinar: 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 webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.

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?

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Just head over to our enrollment page to sign up for Statistically Speaking.

You’ll get exclusive access to this month’s webinar live, weekly live Q&A sessions, a private stats forum, 60+ recordings of past webinars (including this one), and more.

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