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

• Superior document: Life Course Research and Social Policies Series ; v.10
• Author: 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
• Physical Description: 1 online resource (300 pages)

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.