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Set-Theoretic Multi-Method Research
Paperback

Set-Theoretic Multi-Method Research

$44.99
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A state-of-the-art comprehensive exposition of combining Qualitative Comparative Analysis (QCA) and case studies, this book facilitates the efficient use and independent learning of this form of SMMR (set-theoretic multi-method research) with the best available software. It will reduce the time and effort required when performing both QCA and case studies within the same research project. This is achieved by spelling out the conceptual principles and practices in SMMR, and by introducing a tailor-made R software package. With an applied and practical focus, this is an intuitive resource for implementing the most complete protocol of SMMR. Features include Learning Goals, Core Points, and Empirical Examples, as well as boxed examples of R codes and the R output it produces. There is also a glossary for key SMMR terms. Additional online material is available, comprising machine-readable datasets and R scripts for replication and independent learning.

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MORE INFO
Format
Paperback
Publisher
Cambridge University Press
Country
United Kingdom
Date
29 February 2024
Pages
276
ISBN
9781009307192

A state-of-the-art comprehensive exposition of combining Qualitative Comparative Analysis (QCA) and case studies, this book facilitates the efficient use and independent learning of this form of SMMR (set-theoretic multi-method research) with the best available software. It will reduce the time and effort required when performing both QCA and case studies within the same research project. This is achieved by spelling out the conceptual principles and practices in SMMR, and by introducing a tailor-made R software package. With an applied and practical focus, this is an intuitive resource for implementing the most complete protocol of SMMR. Features include Learning Goals, Core Points, and Empirical Examples, as well as boxed examples of R codes and the R output it produces. There is also a glossary for key SMMR terms. Additional online material is available, comprising machine-readable datasets and R scripts for replication and independent learning.

Read More
Format
Paperback
Publisher
Cambridge University Press
Country
United Kingdom
Date
29 February 2024
Pages
276
ISBN
9781009307192