You are here

Due to global supply chain disruptions, we recommend ordering print titles early.

 

Sequence analysis (SA) was developed to study social processes that unfold over time as sequences of events. It has gained increasing attention as the availability of longitudinal data made it possible to address sequence-oriented questions. This volume introduces the basics of SA to guide practitioners and support instructors through the basic workflow of sequence analysis. In addition to the basics, this book outlines recent advances and innovations in SA.

 The presentation of statistical, substantive, and theoretical foundations is enriched by examples to help the reader understand the repercussions of specific analytical choices. The extensive ancillary material supports self-learning based on real-world survey data and research questions from the field of life course research.

Data and code and a variety of additional resources to enrich the use of this book are available on an accompanying website at https://sa-book.github.io.

 
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

This book provides a comprehensive and updated introduction to sequence analysis, I highly recommend it for anyone who wants to learn the topic systematically.

Tim F. Liao
University of Illinois at Urbana-Champaign
Key features
  • This volume introduces the basics of SA to guide practitioners and support instructors through the workflow of sequence analysis.
  • The presentation of statistical, substantive, and theoretical foundations is enriched by examples to help the reader understand the advantages and disadvantages of specific analytical choices.
  • The extensive ancillary material supports self-learning by offering exercises and solutions based on real data and research questions in the field of life course research.

For instructors

Select a Purchasing Option

Electronic version
Prices from
$20.00*
*180 day rental