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Sequence analysis / Marcel Raab and Emanuela Struffolino.

By: Contributor(s): Material type: TextSeries: Quantitative applications in the social sciencesPublisher: Los Angeles : SAGE, 2022Description: 1 online resource. 20 centimetresContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781071801895 (ePub ebook) :
Subject(s): Additional physical formats: Print version :: No titleDDC classification:
  • 301.015195 23
LOC classification:
  • QA279 RAA
Contents:
Series Editor’s Introduction; Acknowledgments; Preface; About the Authors; Chapter 1.
Introduction; 1.1 Sequence Analysis in the Social Sciences; 1.2 Organization of the Book; 1.3 Software, Data,
and Companion Webpage; Chapter 2: Describing and Visualizing Sequences; 2.1 Basic Concepts and Terminology; 2.1 Basic Concepts and Terminology; 2.3 Description of Sequence Data I: The Basics; 2.4 Visualization of Sequences; 2.5 Description of Sequences II: Assessing Sequence; Chapter 3: Comparing Sequences; 3.1 Dissimilarity Measures to Compare Sequences; 3.2 Alignment Techniques; 3.3 Alignment-Based Extensions of OM; 3.4 Nonalignment Techniques; 3.5 Comparing Dissimilarity Matrices; 3.6 Comparing Sequences of Different Length; 3.7 Beyond the Standard Full-Sample Pairwise Sequence Comparison; Chapter 4: Identifying Groups in Data: Analyses Based On Dissimilarities Between Sequences; 4.1 Clustering Sequences to Uncover Typologies; 4.2 Illustrative Application; 4.3 “Construct Validity” for Typologies From Cluster Analysis to Sequences; 4.4 Using Typologies as Dependent and
Independent Variables in a Regression Framework; Chapter 5: Multidimensional Sequence Analysis; 5.1 Accounting for Simultaneous Temporal Processes; 5.2 Expanding the Alphabet: Combining Multiple Channels Into a Single Alphabet; 5.3 Cross-Tabulation of Groups Identified From Different Dissimilarity Matrices; 5.4 Combining Domain-Specific Dissimilarities; 5.5 Multichannel Sequence Analysis; Chapter 6: Examining Group Differences Without Cluster Analysis; 6.1 Comparing Within-Group Discrepancies; 6.2 Measuring Associations Between Sequences and Covariates; 6.3 Statistical Implicative Analysis; Chapter 7: Combining Sequence Analysis With Other Explanatory Methods; 7.1 The Rationale Behind the Combination of Stochastic and Algorithmic Analytical Tools; 7.2 Competing Trajectories Analysis; 7.3 Sequence Analysis Multistate Model Procedure; 7.4 Combining SA and (Propensity Score) Matching; Chapter 8:
Conclusions; 8.1 Summary of Recommendations: An Extended Checklist; 8.2 Achievements, Unresolved Issues, and Ongoing Innovation; References;
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Books GHU Library QA279 RAA (Browse shelf(Opens below)) 1 Available GHU1122903741

Series Editor’s Introduction; Acknowledgments; Preface; About the Authors; Chapter 1.

Introduction; 1.1 Sequence Analysis in the Social Sciences; 1.2 Organization of the Book; 1.3 Software, Data,

and Companion Webpage; Chapter 2: Describing and Visualizing Sequences; 2.1 Basic Concepts and Terminology; 2.1 Basic Concepts and Terminology; 2.3 Description of Sequence Data I: The Basics; 2.4 Visualization of Sequences; 2.5 Description of Sequences II: Assessing Sequence; Chapter 3: Comparing Sequences; 3.1 Dissimilarity Measures to Compare Sequences; 3.2 Alignment Techniques; 3.3 Alignment-Based Extensions of OM; 3.4 Nonalignment Techniques; 3.5 Comparing Dissimilarity Matrices; 3.6 Comparing Sequences of Different Length; 3.7 Beyond the Standard Full-Sample Pairwise Sequence Comparison; Chapter 4: Identifying Groups in Data: Analyses Based On Dissimilarities Between Sequences; 4.1 Clustering Sequences to Uncover Typologies; 4.2 Illustrative Application; 4.3 “Construct Validity” for Typologies From Cluster Analysis to Sequences; 4.4 Using Typologies as Dependent and

Independent Variables in a Regression Framework; Chapter 5: Multidimensional Sequence Analysis; 5.1 Accounting for Simultaneous Temporal Processes; 5.2 Expanding the Alphabet: Combining Multiple Channels Into a Single Alphabet; 5.3 Cross-Tabulation of Groups Identified From Different Dissimilarity Matrices; 5.4 Combining Domain-Specific Dissimilarities; 5.5 Multichannel Sequence Analysis; Chapter 6: Examining Group Differences Without Cluster Analysis; 6.1 Comparing Within-Group Discrepancies; 6.2 Measuring Associations Between Sequences and Covariates; 6.3 Statistical Implicative Analysis; Chapter 7: Combining Sequence Analysis With Other Explanatory Methods; 7.1 The Rationale Behind the Combination of Stochastic and Algorithmic Analytical Tools; 7.2 Competing Trajectories Analysis; 7.3 Sequence Analysis Multistate Model Procedure; 7.4 Combining SA and (Propensity Score) Matching; Chapter 8:

Conclusions; 8.1 Summary of Recommendations: An Extended Checklist; 8.2 Achievements, Unresolved Issues, and Ongoing Innovation; References;

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