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E-grāmata: Sequence Analysis

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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.

Recenzijas

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

Series Editor's Introduction xiii
Acknowledgments xv
Preface xvii
About the Authors xviii
Chapter 1 Introduction
1(8)
1.1 Sequence Analysis in the Social Sciences
1(2)
1.2 Organization of the Book
3(4)
1.3 Software, Data, and Companion Webpage
7(2)
Chapter 2 Describing and Visualizing Sequences
9(42)
2.1 Basic Concepts and Terminology
9(4)
2.1.1 Sequences With Recurrent States
9(1)
2.1.2 Episodes and Transitions
10(2)
2.1.3 Subsequences
12(1)
2.2 Defining Sequences
13(7)
2.2.1 The Alphabet
13(1)
2.2.2 Sequence Length and Granularity
14(3)
2.2.3 Sequences of Unequal Length: Censoring and Missing Data
17(3)
2.3 Description of Sequence Data I: The Basics
20(6)
2.3.1 Time Spent in Different States and Occurrence of Episodes
20(1)
2.3.2 Transition Rates
20(2)
2.3.3 State Distribution and Shannon Entropy at Different Positions
22(2)
2.3.4 Modal and Representative Sequences
24(2)
2.4 Visualization of Sequences
26(14)
2.4.1 Data Summarization Graphs
29(4)
2.4.2 Data Representation Graphs
33(7)
2.5 Description of Sequences II: Assessing Sequence Complexity and Quality
40(11)
2.5.1 Unidimensional Measures
40(2)
2.5.2 Composite Indices
42(9)
Chapter 3 Comparing Sequences
51(32)
3.1 Dissimilarity Measures to Compare Sequences
52(1)
3.2 Alignment Techniques
53(11)
3.2.1 Optimal Matching
53(3)
3.2.2 Assigning Costs to the Alignment Operations
56(5)
3.2.3 Critiques of Classical OM
61(3)
3.3 Alignment-Based Extensions of OM
64(9)
3.4 Nonalignment Techniques
73(1)
3.5 Comparing Dissimilarity Matrices
74(3)
3.6 Comparing Sequences of Different Length
77(1)
3.7 Beyond the Standard Full-Sample Pairwise Sequence Comparison
78(5)
Chapter 4 Identifying Groups in Data: Analyses Based On Dissimilarities Between Sequences
83(32)
4.1 Clustering Sequences to Uncover Typologies
83(12)
4.1.1 The Rationale Behind Clustering Sequences
86(2)
4.1.2 Crisp (or Hard) Clustering Algorithms
88(3)
4.1.3 Partitional Clustering
91(1)
4.1.4 Using Cluster Quality Indices to Choose the Number of Clusters
92(3)
4.2 Illustrative Application
95(9)
4.2.1 Hierarchical Clustering: Ward's Linkage
95(3)
4.2.2 Partitional Clustering: Partitioning Around Medoids
98(6)
4.3 "Construct Validity" for Typologies From Cluster Analysis to Sequences
104(6)
4.4 Using Typologies as Dependent and Independent Variables in a Regression Framework
110(5)
4.4.1 Clusters as Outcomes
111(2)
4.4.2 Clusters as Predictors
113(2)
Chapter 5 Multidimensional Sequence Analysis
115(13)
5.1 Accounting for Simultaneous Temporal Processes
115(2)
5.2 Expanding the Alphabet: Combining Multiple Channels Into a Single Alphabet
117(1)
5.3 Cross-Tabulation of Groups Identified From Different Dissimilarity Matrices
118(1)
5.4 Combining Domain-Specific Dissimilarities
119(1)
5.5 Multichannel Sequence Analysis
120(8)
Chapter 6 Examining Group Differences Without Cluster Analysis
128(12)
6.1 Comparing Within-Group Discrepancies
128(3)
6.2 Measuring Associations Between Sequences and Covariates
131(7)
6.2.1 Discrepancy Framework--Pseudo R- and Permutation F-Test
131(4)
6.2.2 Bayesian Information Criterion and the Likelihood Ratio Test
135(3)
6.3 Statistical Implicative Analysis
138(2)
Chapter 7 Combining Sequence Analysis With Other Explanatory Methods
140(6)
7.1 The Rationale Behind the Combination of Stochastic and Algorithmic Analytical Tools
140(1)
7.2 Competing Trajectories Analysis
141(2)
7.3 Sequence Analysis Multistate Model Procedure
143(1)
7.4 Combining SA and (Propensity Score) Matching
144(2)
Chapter 8 Conclusions
146(8)
8.1 Summary of Recommendations: An Extended Checklist
146(6)
8.1.1 Define, Describe, and Visualize the Sequences
147(1)
8.1.2 Computing Sequence Dissimilarities
148(1)
8.1.3 Clustering Sequences
149(1)
8.1.4 Multidimensional Sequence Analysis
150(1)
8.1.5 Group Comparisons Without Clustering
151(1)
8.1.6 Added Value of Sequence Analysis
151(1)
8.2 Achievements, Unresolved Issues, and Ongoing Innovation
152(2)
References 154(13)
Index 167
Marcel Raab is Senior Researcher at the State Institute for Family Research at the University of Bamberg and Deputy Managing Director of the Journal of Family Research. Previously, he worked as a research assistant at the National Educational Panel Study and the Professorship of Demography at the University of Bamberg, as a research fellow in the research group Demography and Inequality at the WZB Berlin Social Science Center, and Assistant Professor for Sociology at the University of Mannheim. In 2011 he was a visiting pre-doctoral fellow at the Center for Research on Inequalities and the Life Course (CIQLE) at Yale University, New Haven. In 2014, he obtained a doctorate in sociology at the University of Bamberg with his dissertation on Family Effects on Family Formation. Currently, he is a member of the Advisory Board of the SAA Sequence Analysis Association.

Emanuela Struffolino is Assistant Professor of Economic Sociology at the University of Milan, Department of Social and Political Sciences. Between 2020 and 2021, she was guest Professor of Macrosociology at the Institute of Sociology at the Freie Universität Berlin and then guest Professor of Social Policy at the Humboldt-Universität zu Berlin. From 2015 to 2019 she was postdoctoral fellow at the research group Demography and Inequality" Research Group at the WZB Berlin Social Science Center. After obtaining her PhD in Sociology at the University of Milano-Bicocca, in 2014 she worked as research fellow at the Swiss National Centre for Competence in Research LIVES - Overcoming vulnerability: A life-course perspective" at the University of Lausanne. She is a member of the Executive Board of the SAA-Sequence Analysis Association.
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