Advanced Statistical Analysis & Design II
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  • Stats for Psychology and Human Factors I
  • Shane T. Mueller, Ph.D.

1: Libraries and functions for reading data

  • Unit covers reading data from excel and other file formats using a number of libraries.
  • Day 1 Supplemental materials

2: Data Programming

  • Unit covers dplyr, reshape2, etc..
  • Day 2 Supplemental materials
  • Resource: RStudio Data Wrangling cheatsheet

3. ggPlot2

  • Unit covers qplot and ggplot functions.
  • Supplemental materials

4. Power analysis

  • Unit covers power analysis functions in R.
  • Supplemental materials

5. Principal Components analysis and Eigen decomposition and SVD

  • Unit covers PCA, SVD, eigen decomposition, and projection into subspaces.
  • Supplemental materials

6. Psychometrics

  • Unit covers psychometrics, including test-retest reliability, ICC, Cronbach's alpha, and Cohen's Kappa.
  • Supplemental materials

7. Generalized Linear Regression

  • Unit covers Generalized linear regression, ML estimation, weighted regression,.
  • Supplemental materials

8. Logistic regression

  • Unit covers logistic regression, over/under-dispersion, and probit regression.
  • Supplemental materials

9. Testing Multiple Dependent Variables, Hotellings T2, the MANOVA Procedure, and Canonical Correlation Analysis (CCA)

  • Unit covers MANOVA and methods for testing multiple DVs.
  • Supplemental materials

9b. Multinomial regression model

  • Unit a short example on predicting multiple different classes using a variation on the logistic model.
  • Supplemental materials

10-11. Item Response Theory

  • Unit covers using Item Response theory and Rasch Model to do psychometrics.
  • Supplemental materials

Tutorials on Shiny

  • Basic materials covering Shiny
  • Supplemental materials

12. Classification with LDA and QDA

  • Unit covers using LDA and QDA for simple classifier models.
  • Supplemental materials

13. Classification with trees and random forests

  • Unit covers using decision/partition trees, random forests, and KNN classification methods.
  • Supplemental materials

13b. K-nearest neighbor

  • Unit covers KNN classification methods.
  • Supplemental materials

14. Naive Bayes Classification

  • Unit covers using Naive Bayes classification.
  • Supplemental materials

14b. Support Vector Machines

  • Unit covers support vector machines.
  • Supplemental materials

15a. Artificial Neural Networks

  • Unit covers simple artificial neural networks.
  • Supplemental materials

16. Multi-dimensional Scaling

  • Unit covers metric and non-metric MDS.
  • Supplemental materials

18. Clustering Methods

  • Unit covers hierarchical and finite clustering methods.
  • Supplemental materials

19. Model-based Clustering Methods

  • Unit covers Model-based clustering and finite mixture modeling using mclust and flexmix.
  • Supplemental materials

20. Mixtures of regression models

  • Unit covers mixtures of regresions with flexmix.
  • Supplemental materials

21. Exploratory Factor Analysis

  • Unit covers EFA using factanal and psych::fa, including rotations. li>
  • Supplemental materials

22. Confirmatory Factor Analysis

  • Unit using lavaan to do confirmatory factor analysis and latent variable models.
  • Supplemental materials

23. Correspondence Analysis and Multiple Correspondence Analysis

  • Unit using mass::corresp and ca::ca ca::mjca to do correspondence analysis.
  • Supplemental materials
This material was developed By Shane Mueller for PSY 5220, an statistical analysis course for the Applied Cognitive Science and Human Factors program at Michigan Technological University.

Other Useful Resources and Books

  • Faraway's Practical Regression and ANOVA in R
  • Baron & Li's Notes on the use of R for psychology experiments and questionnaires
  • Robert Kabacoff's R in Action
  • Fox & Weisberg's An R Companion to Applied Regression

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