Advanced Statistics | Content

Advanced Statistical Analysis:
A Primer

Adam J. McKee, Ph.D.


DRAFT - Do Not Distribute

This content is released as a draft version for comment by the scholarly community.  Please do not distribute as is.  


Section 1:  Advanced Statistics and Social Science


Section 2:  Linear Models and Least Squares


Section 3:  Regression Core Concepts


Section 4:  Analysis of Variance

  • Section 4.1:  One-Way Analysis of Variance
  • Section 4.2:  Two-Way Analysis of Variance
  • Section 4.3:  Analysis of Covariance
  • Section 4.4:  Examining Interactions
  • Section 4.5:  Repeated Measures Designs

Section 5:  Linear Model Diagnostics

  • Section 5.1:  Dealing with Outliers
  • Section 5.2:  Examining Nonlinearity
  • Section 5.3:  Error Variance
  • Section 5.4:  Nonnormality
  • Section 5.5:  Collinearity

Section 6:  Logit and Probit Models

  • Section 6.1:  The Purpose of Logistic Regression
  • Section 6.2:  Models for Dichotomous Data
  • Section 6.3:  The Linear Probability Model
  • Section 6.4:  Models for Polytomous Data
  • Section 6.5:  Assumptions and Limitations

Section 7:  Advanced Regression Models

  • Section 7.1:  Causal Modeling
  • Section 7.2:  Path Analysis
  • Section 7.3:  Assumptions
  • Section 7.4:  Structural Equation Models with Latent Variables
  • Section 7.5:  Does the Model Fit?

Section 8:  Other Advanced Linear Models

  • Section 8.1:  Canonical Correlation
  • Section 8.2:  Multivariate Analysis of Variance
  • Section 8.3:  Discriminate Analysis
  • Section 8.4:  Factor Analysis
  • Section 8.5:  Some Final Thoughts

 

File Created: 08/09/2018
Last Modified:  08/27/2019

This work is licensed under an Open Educational Resource-Quality Master Source (OER-QMS) License.

Open Education Resource--Quality Master Source License


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