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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : New. 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. N° de réf. du vendeur LU-9781526424730
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