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Statistical approaches to casual analysis / Matthew Mcbee.

By: Material type: TextTextSeries: The Sage quantitative research kitPublisher: Los Angeles : SAGE, 2021Description: xi, 234 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781526424730
Subject(s): DDC classification:
  • 519.53 M459s 23
LOC classification:
  • HA29 .M4384 2021
Contents:
Introduction -- Conditioning -- Directed acyclic graphs -- Rubin's causal model and the propensity score -- Propensity score analysis -- Instrumental variable analysis -- Regression discontinuity design -- Conclusion.
Summary: "A practical, up-to-date, step-by-step and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involve in carrying out various types of statistical causal analysis. Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to data, discussing key concepts such as: directed acyclic graphs (DAGs), Rubin’s Causal Model (RCM), Propensity Score Analysis, and Regression Discontinuity Design."
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Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books Main Library Graduate School Library GRD 519.53 M459s 2021 (Browse shelf(Opens below)) 1-2 Available 028399
Books Books Main Library Graduate School Library GRD 519.53 M459s 2021 (Browse shelf(Opens below)) 2-2 Available 030247

Includes bibliographical references and index.

Introduction -- Conditioning -- Directed acyclic graphs -- Rubin's causal model and the propensity score -- Propensity score analysis -- Instrumental variable analysis -- Regression discontinuity design -- Conclusion.

"A practical, up-to-date, step-by-step and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involve in carrying out various types of statistical causal analysis. Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to data, discussing key concepts such as: directed acyclic graphs (DAGs), Rubin’s Causal Model (RCM), Propensity Score Analysis, and Regression Discontinuity Design."

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