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Analyzing Political and Social Sequences

Philippe Blanchard
p.blanchard@warwick.ac.uk

University of Warwick

Philippe Blanchard, University of Warwick, works on green politics, political communication, and methods for social and political sciences: multivariate statistics, longitudinal methods, interviewing, content analysis and digital data. He has taught methods in Austria, Denmark, France, Germany, Singapore, Switzerland, the UK and the USA.

He is currently director of Warwick's BA in Politics, International Studies and Quantitative Methods and MA in Politics, Big Data & Quantitative Methods.

Philippe is the Chair of the ECPR Methods School Academic Advisory Board.


Course Dates and Times

Monday 5 to Friday 9 March 2018
09:00-12:30
15 hours over 5 days

Prerequisite Knowledge

Two prior knowledges are requested:

  • Basic applied statistics and use of data sets, through either an Excel-like software, statistics software like SPSS or Stata, or R. Participants who have never practiced any statistics should attend an introductory course or at least read an introductory textbook and practice by themselves.
  • Basic knowledge of R: data manipulation and basic descriptive statistics. Most of the hands-on work will be done in R. R-beginners may prepare themselves on their own, or, ideally, by attending beforehand an introductory course prior to the course, such as the one offered in Bamberg.


Short Outline

Sequence analysis is the systematic descriptive and causal study of sequences, that is, successions of standard categorical states or events. Sequence analysis is a unique method for representing, comparing and clustering sequences, for extracting prototypical sequences and for mining sequence populations. Its core tool, the optimal matching algorithm, imported from genetics and bio-computing, screens and discriminates longitudinal processes according to the nature of events, their duration and their order.

Numerous fields in the social and political sciences are concerned with sequences, for example: life course analysis (e.g. family and residential transition from youth to adulthood), sociology of professional careers (such as gendered careers or transition to retirement),  political sociology (elite and activist careers), evolution of regimes (stages of development or transition to democracy), speech analysis (rhetorical strategies), geopolitics (stages in crises or democratic transition), comparative studies (stages of diffusion of reforms or mobilizations), elections (stages of public opinion formation during campaigns), human geography (distribution of space occupation from city centre) and ethnographic practices (rituals).

Tasks for ECTS Credits

  • Participants attending the course: 2 credits (pass/fail grade) The workload for the calculation of ECTS credits is based on the assumption that students attend classes and carry out the necessary reading and/or other work prior to, and after, classes.
  • Participants attending the course and completing one task (see below): 3 credits (to be graded)
  • Participants attending the course, and completing two tasks (see below): 4 credits (to be graded

The following tasks will be proposed:

  • Writing a 5,000 to 6,000-characters report within two weeks after the course about how sequence analysis is contributing and/or will contribute to your personal research project(s).
  • A seated (or take-home, in case seated is not doable) examination based on knowledge gained from the course and personal readings.

Long Course Outline

Sequence analysis (SA) is the systematic descriptive and causal study of sequences, that is, successions of standard states or events. It is a unique method for representing, comparing and clustering sequences, for extracting prototypical sequences, for describing groups of sequences and mining sequence populations. Its core tool, the optimal matching algorithm, imported from genetics and bio-computing, screens and discriminates longitudinal processes according to the nature of events, their duration and their order.

The course will be both theoretical and practical. A project will be provided by the instructor as a running example throughout the course. For participants who come with their own data and/or who have a precise research project in mind, the course will provide some help to set up a treatment strategy and to articulate SA with other methods.

The course is mainly designed for beginners in sequences, but researchers who have already used R or Stata on sequences may attend so as to improve their theoretical and/or practical knowledge, as well as take advantage of the confrontation with varied fieldworks from students and instructor.

Participants will be invited to do exercises on their own or by pairs if level or fieldwork match. Students are invited to bring their own laptop and to install R prior to the course (http://cran.r-project.org/), as well as the following packages: boot, cluster, colorspace, foreign, graphics, RcolorBrewer, rgrs and TraMineR. All kinds of hardware and operating systems fit.

The course is made of the following sections. Each might be developed more or less, according to the needs of the audience.

  1. What is a sequence? This first part proposes definitions of sequences and subsequences, which are very common objects for social scientists at large, but seldom considered by social statisticians.
  2. What are the principals of sequence analysis? After a short retrospective glance at the origins of SA, the second part develops the main statistical principles of the method.
  3. What is the specificity of sequence analysis? Other longitudinal methods, popular in the social and political science, may attempt at treating longitudinal objects in the social sciences. Yet, none of these approaches can treat categorical time series in a comprehensive approach to time-related phenomena.
  4. What data can be treated by means of sequence analysis? Several concrete examples are given, from numerous scientific fields. I also show how different kinds of data may best be investigated. Reference data from personal questionnaire surveys are given as examples, as well as large national database such as the Swiss Household Panel. Students are also encouraged to bring their own data, so that their suitability may be discussed collectively.
  5. How should the data be prepared and coded? Due to the diverse origins of sequence data, they need to be cleaned and formatted so as to be properly processed.
  6. How are the data processed? This part presents the core tools of sequence analysis, the optimal matching algorithm, its variants and its alternatives.

Sequence analysis is still an emerging method in the field of political science and social sciences at large. If you wonder about its usefulness for your own research project, do not hesitate to contact the instructor.

Day Topic Details
Monday Definitions and principles
Tuesday Review and preparation of sequence data

Hands-on session 1

Wednesday Case study. Optimal matching (1)

Hands-on session 2

Thursday How to make the best use of optimal matching

Hands-on session 3

Friday Treatment of OM outputs

Hands-on session 4

Day Readings

Participants should choose their priority readings in the list below according to their disiciplinary interests. However all recommended readings are representative of some aspects of the method that are useful for all disciplines. It is strongly recommended to start reading before the course kicks off, so that you may focus during the course on exercises and in-class discussions.

Monday

Compulsory

  1. Gauthier J.-A, F. Bühlmann and P. Blanchard. 2014. “Introduction: Sequence Analysis in 2014” Pp. 1-17 in Blanchard P., F. Bühlmann and J.-A. Gauthier (eds.). Advances in Sequence Analysis: Methods, Theories and Applications. London: Springer
  2. Abbott Andrew, Alexandra Hrycak. 1990. Measuring ressemblance in sequence data : an optimal matching analysis of musicians’ careers. American Journal of Sociology 96: 144-185.

Optional

  1. Accominotti Fabien. 2009. Creativity from interaction: Artistic movements and the creativity careers of modern painters. Poetics 27: 267-294.
  2. Abbott Andrew. 1992. From Causes to Events: Notes on Narrative Positivism. Sociological Methods & Research 20: 428-455 (republished in Abbott Andrew. 2001. Time Matters. Chicago: University of Chicago Press).
Tuesday

Compulsory

  1. Casper Gretchen and Matthew Wilson. 2014. Using Sequences to Model Crises. Political Science Research and Methods 3 (2): 381-397
  2. Abbott Andrew and Stanley Deviney. 1992. The Welfare State as Transnational Event: Evidence from Sequences of Policy Adoption. Social Science History 16 (2) 245-274.

Optional

  1. Halpin Brendan and Tak Wing Chan. 1998. Classs Careers as Sequences: An Optimal Matching Analysis of Work-Life. European Sociological Review 14 (2): 111-130.
  2. An introductory course in R, for example:
  • Venables W. N., D. M. Smith and the R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. Reference Index, http://cran.r-project.org/doc/manuals/R-intro.pdf
  • or: Crawley Michael J. 2005. Statistics: An Introduction using R. Chichester: Wiley and Sons, chapters 1 and 2.
  • or: Zuur Alain F., Elena N. Ieno and Erik H.W.G. Meesters. A Beginner's Guide to R. Springer: Dordrecht, chapters 1 to 3.

see http://www.r-project.org/doc/bib/R-books.html for more possibilities, in several languages.

Wednesday

Compulsory

  1. Widmer Eric and Gilbert Ritschard. 2008. The De-Standardization of the Life Course: Are Men and Women Equal? Geneva: University of Geneva.

Optional

  1. Brzinsky-Fay Christian, Ulrich Kohler and Magdalena Luniak. 2006. Sequence analysis with Stata. The Stata Journal 6 (4): 435-460.
  2. Gabadinho Alexis, Gilbert Ritschard, Matthias Studer and Nicolas Müller. 2009. Mining sequence data in R with the TraMineR package: A user's guide. Department of Econometrics and Laboratory of Demography, Geneva: University of Geneva.
Thursday

Compulsory

  1. Buton François, Lemercier Claire, Mariot Nicolas. 2012. The household effect on electoral participation. A contextual analysis of voter signatures from a French polling station (1982–2007), Electoral Studies (2012), doi:10.1016/j.electstud.2011.11.010

Optional

  1. Han Shin-Kap and Phyllis Moen. 1999. Clocking Out: Temporal Patterning of Retirement. American Journal of Sociology 105 (1): 191-236.
  2. Lesnard Laurent and Man Yee Kan. 2009. Two-Stage Optimal Matching Analysis of Workdays and Workweeks. Sociology Working Papers 2009-04, Department of Sociology, University of Oxford, http://hal.archives-ouvertes.fr/halshs-00435422.
  3. Salmela-Aro Katariina et al..
    http://www.unil.ch/ 16: 25–41: Advances in Life Course Research2010. Mapping pathways to adulthood among Finnish university students: Sequences, patterns, variations in family- and work-related roles,

sequences2012/files/2012/04/SalmelaAroEtAl-LaCOSA-Paper.pdf

Friday

Compulsory

  1. Pollock Gary. 207. Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. Journal of the Royal Statistical Society Series A 170, part 1: 167-183.

Optional

  1. Lelièvre Eva and Nicolas Robette, A Life Space Perspective to Approach Individual Demographic Processes. Canadian Studies of Population 37 (1-2): 207-244.
  2. Piccarreta Raffaella and Orna Lior. 2010. Exploring sequences: a graphical tool based on multi-dimensional scaling, Journal of the Royal Statistical Society Series A 173, part 1 : 165–184.
  3. Colombi D. and S. Paye. 2014. “Synchronising Sequences. An Analytic Approach to Explore Relationships Between Events and Temporal Patterns” Pp. 249-264 in Blanchard P., F. Bühlmann and J.-A. Gauthier (eds.). Advances in Sequence Analysis: Methods, Theories and Applications. London: Springer

Software Requirements

R plus the following packages (to be installed prior to the course): boot, cluster, colorspace, foreign, graphics, RcolorBrewer, questionr and TraMineR.

Hardware Requirements

Participants to bring own laptops

Literature

See 'Optional readings' in Day to Day Readings list above.

Recommended Courses to Cover Before this One

<p><strong>Summer School</strong><br /> Introduction to R<br /> Data management with R</p> <p><strong>Winter School</strong><br /> Introduction to R<br /> Introduction to Statistics for Political and Social Scientists</p>

Recommended Courses to Cover After this One

<p><strong>Summer School</strong><br /> Intermediate R</p>


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.