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Multiverse Analysis: Computational Methods for Robust Results (Analytical Methods for Social Research)

Multiverse Analysis: Computational Methods for Robust Results (Analytical Methods for Social Research)

Multiverse Analysis: Computational Methods for Robust Results (Analytical
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Multiverse Analysis: Computational Methods for Robust Results (Analytical Methods for Social Research) Hardback - 2025

by Young, Cristobal,Cumberworth, Erin

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Cambridge University Press, 3/6/2025 12:00:01 AM. hardcover. Very Good. 0.6900 9.0000 6.0000.
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Details

  • Title Multiverse Analysis: Computational Methods for Robust Results (Analytical Methods for Social Research)
  • Author Young, Cristobal,Cumberworth, Erin
  • Binding Hardback
  • Condition Used - Very good
  • Pages 286
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 3/6/2025 12:00:01 AM
  • Features Bibliography, Index
  • Bookseller's Inventory # mon0003752257
  • ISBN 9781316518786 / 1316518787
  • Weight 1.21 lbs (0.55 kg)
  • Dimensions 9 x 6 x 0.69 in (22.86 x 15.24 x 1.75 cm)
  • Size 0.6900 9.0000 6.0000
  • Category Sociology
  • Library of Congress subjects Social sciences - Research
  • Library of Congress Catalogue Number 2024013549
  • Dewey Decimal Code 300.721
  • Quantity available 1
  • Bookseller catalogues Book

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Reader reviews for Multiverse Analysis: Computational Methods for Robust Results (Analytical Methods for Social Research)

From the publisher

There are many ways of conducting an analysis, but most studies show only a few carefully curated estimates. Applied research involves a complex array of analytical decisions, often leading to a 'garden of forking paths' where each choice can lead to different results. By systematically exploring how alternative analytical choices affect the findings, Multiverse Analysis reveals the full range of estimates that the data can support and uncovers insights that single-path analyses often miss. It shows which modelling decisions are most critical to the results and reveals how data and assumptions work together to produce empirical estimates. Focusing on intuitive understanding rather than complex mathematics, and drawing on real-world datasets, this book provides a step-by-step guide to comprehensive multiverse analysis. Go beyond traditional, single-path methods and discover how multiverse analysis can lead to more transparent, illuminating, and persuasive empirical contributions to science.
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