Clustering and Classification, Spring 2006 (KU)

Upcoming Workshops:

Thank you for visiting my course notes. Here are some upcoming opportunities to learn from me and my colleagues in person:

Currently no workshops are planned

Methods for Clustering and Classification (Psychology 993)
Spring Semester, 2006; University of Kansas

Course Documents


Slides Other Files
Lecture 1 – Introduction to SAS
Lecture 2 – Introduction to Clustering and Classification
Lecture 3 – Cluster Validation
Lecture 4 – Discriminant Analysis
Lecture 5 – How To Do Discriminant Analysis Lecture 5 Data #1
Lecture 5 Data #2
Lecture 5 R Code
Lecture 6 – Tree Models of Similarity and Association
Lecture 7 – More Tree Models of Similarity and Association
Lecture 8 Data
Lecture 8 R Code
Lecture 9 – K-Means Clustering Lecture 9 R Code
Lecture 10 R Code
Lecture 11 – Empirical Article/Model Based Clustering
Lecture 12 – Latent Class Analysis
Lecture 13 – Latent Class Analysis
Lecture 14 – LCA: Evaluating Model Fit
Lecture 15 -Latent Profile Analysis Lecture 15 Data #1
Lecture 15 Data #2
Lecture 15 Mplus Code
Lecture 15 Mplus Output
Lecture 16 – Absolute Measures of Fit in Finite Mixture Models Lecture 16 R Code #1
Lecture 16 R Code #2
Lecture 17 – Cognitive Diagnosis
Lecture 18 – Growth Mixture Models Lecture 18 Data
Lecture 18 Mplus Code
Lecture 19 – Adaptations of CDMs

One comment on “Clustering and Classification, Spring 2006 (KU)
  1. Cynthia says:

    Dear Dr. Templin,

    Thank you for sharing these lecture notes!;)

    I do have one question about LCA/LPA. The results for each class provide mean, sd, and p-value for each item. Is the significance level comparing across the n classes, or within a class?

    Thank you for your help!

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