Sequence Analysis and Related Approaches : : Innovative Methods and Applications.

Sequence Analysis and Related Approaches : : Innovative Methods and Applications.

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Bibliographic Details
Superior document:Life Course Research and Social Policies Series ; v.10
:
Ritschard, Gilbert.
TeilnehmendeR:
Studer, Matthias.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2018.
©2018.
Year of Publication:2018
Edition:1st ed.
Language:English
Series:Life Course Research and Social Policies Series
Online Access:
Physical Description:1 online resource (300 pages)
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Table of Contents:
  • Intro
  • Preface
  • How to Read the Book
  • Acknowledgments
  • Review Committee
  • Associated Reviewers
  • Contents
  • Contributors
  • Sequence Analysis: Where Are We, Where Are We Going?
  • 1 Sequence Analysis: Optimal Matching and Much More
  • 2 Towards Stronger Interaction with Related Approaches
  • 3 Directions for the Future: The Chapters of this Book
  • 4 Conclusion
  • References
  • Part I About Different Longitudinal Approaches in Longitudinal Analysis
  • Do Different Approaches in Population Science Lead to Divergent or Convergent Models?
  • 1 Introduction
  • 2 Different Approaches
  • 2.1 An Approach Based on Duration Models
  • 2.2 An Event Sequences Approach
  • 2.3 A Level Based Approach
  • 2.4 A Network Based Approach
  • 3 Toward a Synthesis
  • 4 Conclusion
  • References
  • Case Studies of Combining Sequence Analysis and Modelling
  • 1 Introduction
  • 2 Case Study 1: Prediction of Excess Depressive Symptoms and Life Events
  • 2.1 Multistate Models
  • 2.2 Sequence Analysis
  • 3 Case Study 2: Antecedents and Consequences of Transitional Pathways to Adulthood
  • 3.1 Model for Strategies Accounting for Depressive Symptoms
  • 3.2 Model for Transitional Pathways Accounting for Strategies
  • 3.3 Model for Depressive Symptoms When Accounting for Pathways
  • 4 Case Study 3: Pathways to Social Exclusion
  • 4.1 Sequence Analysis
  • 4.2 Risk Pattern Analysis
  • 4.3 Predictions of Positive Trajectories
  • 5 Discussion
  • References
  • Part II Sequence Analysis and Event History Analysis
  • Glass Ceilings, Glass Escalators and Revolving Doors
  • 1 Introduction
  • 2 Theoretical Considerations and Hypotheses
  • 2.1 Gender and Upward Occupational Mobility
  • 2.2 Gender Composition and Upward Occupational Mobility
  • 2.3 Gender Composition and Upward Occupational Mobility, by Gender
  • 3 Data and Methods
  • 3.1 Data and Sample
  • 3.2 Variables.
  • 3.2.1 Upward Occupational Mobility
  • 3.2.2 Gender and Gender-Type of Occupation
  • 3.3 Methods
  • 4 Results
  • 4.1 Leadership Position by Gender and Gender-Typical Occupation
  • 4.2 Access to Leadership Positions
  • 4.2.1 Kaplan-Meier Survivor Function
  • 4.2.2 Regression Results
  • 4.3 Leaving Leadership Positions
  • 4.3.1 Kaplan-Meier Survivor Function
  • 4.3.2 Regression Results
  • 5 Discussion
  • References
  • Modelling Mortality Using Life Trajectories of Disabled and Non-Disabled Individuals in Nineteenth-Century Sweden
  • 1 Introduction
  • 2 Methods
  • 3 Data
  • 3.1 Area Selected for Analysis
  • 3.2 Digitised Parish Registers Indicating Disabilities
  • 4 Results
  • 4.1 Sequence Analysis Results
  • 4.2 Kaplan-Meier Curves
  • 4.3 Cox Regression Results
  • 5 Discussion
  • References
  • Sequence History Analysis (SHA): Estimating the Effect of Past Trajectories on an Upcoming Event
  • 1 Introduction
  • 1.1 Sequence History Analysis: A Combination of Sequence Analysis and Event History Analysis
  • 1.2 Sequence History Analysis: Operationalizing Previous Trajectories
  • 1.3 Event History Analysis: Estimating the Effect of Typical Past Trajectories on the Event Under Study
  • 2 Empirical Application: Childhood Co-residence Trajectories and Leaving Home
  • 3 Data
  • 3.1 Control Variables
  • 4 Analysis
  • 4.1 Sequence Analysis: Operationalizing Previous Co-residence Trajectories
  • 4.2 Event History Analysis: Estimating the Effect of Typical Past Trajectories on the Event Under Study
  • 5 Discussion
  • 6 Conclusion
  • References
  • Part III The Sequence Network Approach
  • Network Analysis of Sequence Structures
  • 1 From Sequence Pathways to Sequence-Networks
  • 2 Sequence Pathways in Everyday Life
  • 2.1 Activity Sequences in Networks
  • 2.2 Organizing the Data as a Sequence-Network
  • 3 Analyzing Sequence-Network Structure.
  • 3.1 Describing Sequence-Network Structure
  • 3.2 Comparing Sequence-Networks
  • 4 Illustrative Analysis: Activity Sequencing by Age
  • 4.1 The Activity Sequence Data
  • 4.2 Sequence-Network Analysis Findings
  • 5 Discussion and Conclusion
  • References
  • Relational Sequence Networks as a Tool for Studying Gendered Mobility Patterns
  • 1 Introduction
  • 2 Method
  • 2.1 Basic Concepts
  • 2.2 Data
  • 2.3 Software Tools
  • 3 Results
  • 3.1 Personal Networks
  • 3.2 Sequence Networks
  • 4 Conclusion
  • References
  • Part IV Unfolding the Process
  • Multiphase Sequence Analysis
  • 1 Introduction
  • 2 Sequences as Multiphase Structures
  • 2.1 Characteristics of Multiphase Sequences
  • 2.2 Two Formal Properties of Phases and Two Methodological Assumptions
  • 3 Division into Phases: Reference Frame, Alphabet(s) and Phase-Structure
  • 3.1 A First Hint: The Extended Example
  • 3.2 Three Aspects of Division into Phases
  • 4 Rendering Multiphase Sequences
  • 4.1 Simple Alignment on a Specific Event
  • 4.2 Multiple Alignment by Sliced Representation
  • 5 Measure and Interpretation of Pairwise Distances Between Multiphase Sequences: Multiphase Optimal Matching
  • 5.1 Analytical Logic
  • 5.2 MPOM Applied to Careers of Participants in `Pâtissier' Competitions
  • 5.3 MPOM Compared
  • 6 Conclusion
  • References
  • Unpacking Configurational Dynamics: Sequence Analysis and Qualitative Comparative Analysis as a Mixed-Method Design
  • 1 Introduction
  • 2 Sequence Analysis and Qualitative Comparative as a Sequential Mixed-Methods Design
  • 3 Empirical Illustration
  • 3.1 Background
  • 3.2 Empirical Analysis
  • 3.2.1 Step 1: Sequence Analysis
  • 3.2.2 Step 2: Qualitative Comparative Analysis
  • 4 Concluding Remarks
  • References
  • Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
  • 1 Introduction
  • 2 Hidden Markov Model.
  • 3 Combining Sequence Analysis and Hidden Markov Models for Complex Life Sequences
  • 4 Data
  • 4.1 Sequences
  • 5 Analysis
  • 5.1 Sequence Analysis and Clustering
  • 5.2 Hidden Markov Models for Clusters
  • 5.3 Software
  • 6 Results
  • 7 Discussion
  • References
  • Part V Advances in Sequence Clustering
  • Markovian-Based Clustering of Internet Addiction Trajectories
  • 1 Introduction
  • 2 Data and Methods
  • 2.1 Data
  • 2.2 Clustering Using the HMTD Model
  • 2.3 GMM as a Gold Standard Alternative
  • 2.4 Statistical Analyses
  • 3 Results
  • 3.1 HMTD Clustering
  • 3.2 Usefulness of the Covariates
  • 3.3 GMM Clustering
  • 4 Comparison of HMTD and GMM
  • 5 Conclusion
  • References
  • Divisive Property-Based and Fuzzy Clustering for Sequence Analysis
  • 1 Introduction
  • 2 Sample Issue
  • 3 Property-Based Clustering
  • 3.1 Principle
  • 3.2 Property Extraction
  • 3.3 Running the Analysis in R
  • 4 Fuzzy Clustering
  • 4.1 Fanny Algorithm
  • 4.2 Plotting and Describing a Fuzzy Typology
  • 4.2.1 Most Typical Members
  • 4.2.2 Weight-Based Presentation
  • 4.3 Analyzing Cluster Membership Using Dirichlet Regression
  • 4.4 Running the Analysis in R
  • 5 Conclusion
  • References
  • From 07.00 to 22.00: A Dual-Earner Couple's Typical Day in Italy
  • 1 Introduction
  • 2 The Lexicographic Index
  • 3 The Data, Their Organization and the Coding of the Activities in a Multichannel Approach
  • 4 From 7.00 to 22.00: A Typical Working Dayof a Dual-Earner Couple in Italy
  • 5 Conclusions
  • References
  • Part VI Appraising Sequence Quality
  • Measuring Sequence Quality
  • 1 Introduction: The Quality of Binary Sequencesof Successes and Failures
  • 2 Common Methods for Studying Sequence Trajectories
  • 3 Developing a Measure of Sequence Quality: Formal Properties
  • 4 Using S-Positions: Successes Weighed by Frequency and Recency.
  • 5 An Application: The Quality of Labor Market Careers Among the Unemployed
  • 5.1 Data
  • 5.2 Method
  • 5.3 Findings
  • 6 Conclusion and Discussion
  • References
  • An Index of Precarity for Measuring Early Employment Insecurity
  • 1 Introduction
  • 2 Rising Precarity Among Young People
  • 3 Conceptualising Precarity
  • 4 The Precarity Index
  • 4.1 Defining the Index
  • 4.2 Tuning the Index
  • 4.3 Behavior of the Precarity Index
  • 4.4 Relaxing the Strict State Ordering Requirement
  • 5 Application to the School to Work Transition
  • 6 Conclusion
  • References
  • Correction to: Unpacking Configurational Dynamics: Sequence Analysis and Qualitative Comparative Analysis as a Mixed-Method Design
  • Index.

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