Friday, 2 November 2012

Categorical Data - Applied Modeling

Technique-driven statistical analysis can be like the blind men sent by the king to examine an elephant.  The man who feels the leg says an elephant is like a pillar, the one who feels the tails says it is like a rope, the one who feels the tusk says it is like a pipe.  It is left to the king to integrate the accounts, which he does masterfully (this tale is told by the king).

"Categorical Data - Applied Modeling," which approaches analysis from the data end - recognize the type of data you have, and then assess what different techniques are appropriate. After taking this course, students will know how to perform logistic regression (with both binomial and multinomial response), probit, logit and loglinear analysis using statistical software. Model diagnostics and interpretation of results are also covered, and longitudinal analysis is introduced. A perfect course is Dr. Brian Marx's "Categorical Data - Applied Modeling" online at For more details please visit at

Who Should Take This Course:
Any researcher or analyst who encounters categorical data and needs to analyze or model it.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Logistic Regression Review
  • Interpretation of parameters and odds ratio
  • Standard errors
  • Probit analysis
  • Variable selection
  • Multiple logistic regression with categorical predictors
  • Building and applying logit models
  • Model selection (backward elimination)
  • Model checking
  • Sparse data

SESSION 2: Multicategory Logit Models
  • Logistic regression with multinomial response
  • proportional odds models

SESSION 3: Loglinear Models for Contingency Tables
  • Loglinear models for independence
  • Association
  • 3-way and 4-way tables
  • Connections
  • Logit models
  • Analysis of deviance
  • Graphical modeling
  • Linear by linear association
  • Models for ordinal responses

SESSION 4: Model for Matched Pairs
  • McNemar test
  • Symmetry models
  • Quasi-symmetry
  • Ordinal quasi-symmetry
  • Quasi-independence models
  • Rater agreement
  • Bradley-Terry model

Dr. Brian Marx is Professor of Statistics at Louisiana State University, and has taught Categorical Data Analysis for over ten years. He is currently serving as Chair of the Statistical Modelling Society and is the Coordinating Editor of Statistical Modelling: An International Journal. Dr. Marx has numerous publications in peer reviewed journals.

You will be able to ask questions and exchange comments with Dr. Brian Marx via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 66009116


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