Multilevel Modeling

This course covers basic principles of data analysis involving multiple levels of membership, including basic research principles in multilevel modeling, hierarchical linear modeling, moderation, longitudinal designs, and missing data .

Note

The articles written in English have blue links. The articles written in Thai have red links.

Fall 2019 (Intro to Multilevel Modeling; Graduate, CU)

Course Syllabus

Lectures

  1. Introduction. PowerPoint. R Script.
  2. Multiple Regression (1). PowerPointR ScriptData 1Data 2Data 3.
  3. Multiple Regression (2). PowerPointR ScriptData 1Data 2Data 3Data 4.
  4. Random Intercept Model. PowerPointR ScriptData 1Data 2Data 3.
  5. Random Slope Model. PowerPointR Script.
  6. Centering. PowerPointR ScriptData 1.
  7. Model Building and Effect Size. PowerPointR ScriptData 1.
  8. Growth Curve Model. PowerPointR ScriptData 1Data 2.
  9. Growth Curve Model (2). PowerPointR ScriptData 1Data 2Data 3.
  10. Missing Data. PowerPointR ScriptData 1Data 2Data 3.

Homeworks

  1. Multiple Regression (1). PaperData.
  2. Multiple Regression (2). PaperData.
  3. Random Intercept Model. PaperData.
  4. Random Slope Model. PaperData 1. Data 2.
  5. Cross-level Interaction. PaperData.
  6. Centering. PaperData.
  7. Model Building. PaperData.
  8. Growth Curve Model. PaperData.
  9. Growth Curve Model (2). PaperData.
  10. Missing Data. PaperData.

Spring 2013

Notes

Interpreting Multilevel Models.

Using R for Multilevel Models. Paper. Data set. R script. Supplemental Material for Interpreting Multivariate Growth Curve Model.

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