Statistical approaches to causal analysis
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), Pro…
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- Full title :Statistical approaches to causal analysis / Matthew McBee.
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.
Read More ↓Edition Statements
- Publication Date:©2021
Publication Statements
- Publisher(s):SAGE Publications Ltd
- Place of Publication:London
- Language:eng
- Publication Date:©2021
The Physical Object
- Description:xxix, 234 pages : : illustrations ; : 25 cm.
Edition Identifiers
- ISBN:1526424738, 9781526424730
- oclc:on1107840658
- LC Classification:HA29
- Dewey Decimal:300.72/7
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