You are here

Statistical Approaches to Causal Analysis

Statistical Approaches to Causal Analysis

Additional resources:

March 2022 | 264 pages | SAGE Publications Ltd
A practical, up-to-date, step-by-step guidance on causal analysis for advancing students, this volume of the SAGE Quantitative Research kit features worked example datasets throughout to clearly demonstrate the appication of these powerful techniques, giving students the know-how and the confidence to succeed in their quantitative research journey.

Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to your data, discussing key concepts such as:

·       Directed acyclic graphs (DAGs)

·       Rubin’s Causal Model (RCM)

·       Propensity Score Analysis

·       Regression Discontinuity Design

Directed Acyclic Graphs
Rubin's Causal Model and the Propensity Score
Propensity Score Analysis
Instrumental Variable Analysis
Regression Discontinuity Design

Matthew McBee

Matthew McBee is a Data Scientist with Eastman Chemical Company (Kingsport, TN, USA). Prior to that, he was a faculty member in the department of psychology at East Tennessee State University (Johnson City, TN, USA) for nine years, where he taught graduate and undergraduate statistics and data analysis courses. He served as a statistician at the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. Matthew holds a Ph.D. in Educational Psychology from the University of Georgia. More About Author