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Paul Allison

President, Statistical Horizons

Paul Allison, Ph.D., is professor emeritus of the University of Pennsylvania where he taught graduate courses in methods and statistics for more than 35 years. He is widely recognized as an extraordinarily effective teacher of statistical methods who can reach students with highly diverse backgrounds and expertise.

After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 75 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.

Much of his early research focused on career patterns of academic scientists. At present, his principal methodological research is on the analysis of longitudinal data, especially with determining the causes and consequences of events, and on methods for handling missing data.

A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”

You can visit his university webpage here.

Google Scholar Citation Page

Resources

Articles by Paul Allison

Books by Paul Allison

Data Sets by Paul Allison

SAS Macros written by Paul Allison

Unpublished papers by Paul Allison

Paul's Seminars
On-Demand

Linear Regression

Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. It’s also the essential foundation for understanding more advanced methods like logistic regression, survival analysis, multilevel modeling, structural equation modeling, and even machine learning. Without...

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Introduction to Structural Equation Modeling

Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows...

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Logistic Regression

Logistic regression is by far the most widely used statistical method for the analysis of categorical data. In this seminar, you’ll learn virtually everything you need to know to become a skilled user of logistic regression. We’ll cover the theory...

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On-Demand

Longitudinal Data Analysis Using SAS

For many years, Dr. Paul Allison has been teaching his acclaimed two-day seminar on Longitudinal Data Analysis Using SAS to audiences around the world. This course covers several popular methods for the analysis of longitudinal data with repeated measures: robust standard errors, generalized...

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On-Demand

Longitudinal Data Analysis Using Structural Equation Modeling

For the past six years, Dr. Paul Allison has been teaching his acclaimed seminar on Longitudinal Data Analysis Using Structural Equation Modeling to audiences around the world. This seminar develops a methodology that integrates two widely used approaches to the analysis of...

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Missing Data

If you’re using conventional methods for handling missing data, you may be missing out. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems.

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Missing Data Using R

This course will cover the theory and practice of both maximum likelihood and multiple imputation.

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Structural Equation Modeling: Part 1

Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences.  First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows...

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On-Demand

Structural Equation Modeling: Part 1

Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. This seminar covers structural equation modeling (SEM) basics. It is is an introductory course, and no previous knowledge of SEM...

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Livestream

Structural Equation Modeling: Part 2

Since 2015, hundreds of researchers have taken Paul Allison’s annual 5-day summer course on Structural Equation Modeling. This summer we are doing things a little differently. The course has been divided into two parts, and each part will be taught...

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On-Demand

Structural Equation Modeling: Part 2

For the last several years, Dr. Paul Allison has been teaching his acclaimed two- and five-day seminars on Structural Equation Modeling to audiences around the world. This Part 2 seminar covers advanced SEM topics, like instrumental variables, alternative estimation methods, multiple group...

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On-Demand

Survival Analysis

For the last 25 years, Dr. Paul Allison has been teaching his acclaimed two-day seminar on Survival Analysis to audiences around the world. Now you can take that course in a convenient, on-demand format. This seminar covers both the theory and...

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Survival Analysis Using R

This seminar covers both the theory and practice of statistical methods for event-time data. Participants receive a thorough introduction to such topics as censoring, Kaplan-Meier estimation, Cox regression, discrete-time methods, competing risks, and repeated events.

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