This book provides an up-to-date and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involved in carrying out various types of statistical causal analysis. In turn, helping you apply these methods to your own research.
It contains guidance on:
- Selecting the most appropriate conditioning method for your data.
- Applying the Rubin’s Causal Model to your analysis, a mathematical framework for understanding and ensuring accurate causation inferences.
- Utilising various techniques and designs, such as propensity scores, instrumental variables analysis, and regression discontinuity designs, to better synthesise and analyse different types of data.
Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
€37.99
Table des matières
IntroductionConditioning
Directed Acyclic Graphs
Rubin′s Causal Model and the Propensity Score
Propensity Score Analysis
Instrumental Variable Analysis
Regression Discontinuity Design
Conclusion
A propos de l’auteur
Matthew Mc Bee 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.Achetez cet ebook et obtenez-en 1 de plus GRATUITEMENT !
Langue Anglais ● Format PDF ● Pages 160 ● ISBN 9781529711127 ● Taille du fichier 11.1 MB ● Maison d’édition SAGE Publications ● Lieu London ● Pays GB ● Publié 2022 ● Édition 1 ● Téléchargeable 24 mois ● Devise EUR ● ID 7817579 ● Protection contre la copie Adobe DRM
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