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FSS - Finding Typologies in Data

FSS - Finding Typologies in Data

The course will focus on conducting exploratory data analysis and finding typologies in the data using cluster analysis and latent class modelling.

The course will focus on conducting exploratory data analysis and finding typologies in the data using cluster analysis and latent class modelling.  The course will consist of a theoretical part that in which students will learn how cluster analysis and latent class modelling work and of a practical part in which students will apply these methods (to their own data) using SPSS and R.

Dr. Paulina Pankowska (p.k.p.pankowska@vu.nl)

Dr. Paulina Pankowska (p.k.p.pankowska@vu.nl)

https://research.vu.nl/en/persons/paulina-karolina-pankowska
Portrait of Paulina Pankowska

Course description

Course objectives:

• Learn how to conduct exploratory data analysis and find typologies in the data using cluster analysis and latent class modelling. 

• Learn how to use obtained typologies in further analysis (e.g., to establish associations between individual- characteristics and cluster membership). 

• Focus on cluster analysis in SPSS and latent variable modelling (specifically latent profile analysis – LPA) in R. 

Contents:

The course will consist of 10 meetings.

An overview of the topics covered in each meeting is provided below:

• 1st meeting: An introduction to cluster analysis (incl. general description, applications, and distinction between different clustering algorithms – i.e. deterministic vs. probabilistic and connectivity-based, centroid-based, distribution-based vs. density-based). 

• 2nd meeting: Running k-means clustering in SPSS (using the PhD’s own data or a dataset available online e.g., World Values Survey)

• 3rd meeting: Running hierarchical clustering in SPSS (again, using the PhD’s own data or a dataset available online)

• 4th meeting: Running robustness checks and sensitivity analyses for k-means and hierarchical clustering results; addressing issues and errors 

• 5th meeting: Running a multinomial logistic regression analysis using cluster memberships as dependent variable and a set of (individual-level) characteristics as independent variables

• 6th meeting: Introduction to latent variable modelling in general and latent profile analysis (LPA) specifically 

• 7th and 8th meeting: Introduction to R

• 9th meeting: Running LPA in R 

• 10th meeting: Q&A

Study Characteristics

  • Discipline: Quantitative Methods
  • Language: English
  • ECTS: 3
  • Type of education: in class
  • Academic skill: research
  • Graduate School: Graduate School of Social Sciences
  • Start date: 29.10.2021
  • End date: 03.12.2021
  • Min. number of students: 3
  • Max. number of students: 10
  • Admission criteria: Basic statistical literacy (i.e., familiarity with at least one statistical software such as SPSS, Stata or R; familiarity with basic statistical analysis techniques, e.g., regression, anova, etc.)
  • Concluding assessment: yes
  • Assessment type: Take home assignment; students can choose one of the following three assignments:

• Run a k-means cluster analysis, interpret and report results

• Run a hierarchical cluster analysis, interpret and report results

• Run LPA, interpret and report results

Each assignment should also include some robustness checks and mention the limitations of the method used

  • With certificate: yes
  • Roster/schedule info:

Session 1:  Friday, 29 October 2021 (14.00-16.00)

Session 2:  Friday, 5 November 2021 (14.00-16.00)

Session 3:  Friday, 12 November 2021 (14.00-16.00)

Session 4:  Friday, 19 November 2021 (14.00-16.00)

Session 5:  Friday, 26 November 2021 (14.00-16.00)

Session 6:  Friday, 3 December 2021 (14.00-16.00)

Session 7:  Friday, 10 December 2021 (14.00-16.00)

Session 8:  Friday, 17 December 2021 (14.00-16.00)

Session 9:  Friday, 14 January 2022 (14.00-16.00)

Session 10:  Friday, 21 January 2022 (14.00-16.00)

  • Registration deadline: 30.09.2021
  • Available to: FSS PhD candidates and other PhD’s in the Social Sciences. No fee for FSS, VU and AISSR PhD candidates. Fee for others € 450,-
  • FSS – Statistics: Finding Typologies in Data (An Introduction to Cluster Analysis and Latent Variable Modelling)

    Course description

    Course objectives:

    • Learn how to conduct exploratory data analysis and find typologies in the data using cluster analysis and latent class modelling. 

    • Learn how to use obtained typologies in further analysis (e.g., to establish associations between individual- characteristics and cluster membership). 

    • Focus on cluster analysis in SPSS and latent variable modelling (specifically latent profile analysis – LPA) in R. 

    Contents:

    The course will consist of 10 meetings.

    An overview of the topics covered in each meeting is provided below:

    • 1st meeting: An introduction to cluster analysis (incl. general description, applications, and distinction between different clustering algorithms – i.e. deterministic vs. probabilistic and connectivity-based, centroid-based, distribution-based vs. density-based). 

    • 2nd meeting: Running k-means clustering in SPSS (using the PhD’s own data or a dataset available online e.g., World Values Survey)

    • 3rd meeting: Running hierarchical clustering in SPSS (again, using the PhD’s own data or a dataset available online)

    • 4th meeting: Running robustness checks and sensitivity analyses for k-means and hierarchical clustering results; addressing issues and errors 

    • 5th meeting: Running a multinomial logistic regression analysis using cluster memberships as dependent variable and a set of (individual-level) characteristics as independent variables

    • 6th meeting: Introduction to latent variable modelling in general and latent profile analysis (LPA) specifically 

    • 7th and 8th meeting: Introduction to R

    • 9th meeting: Running LPA in R 

    • 10th meeting: Q&A

    Study Characteristics

    • Discipline: Quantitative Methods
    • Language: English
    • ECTS: 3
    • Type of education: in class
    • Academic skill: research
    • Graduate School: Graduate School of Social Sciences
    • Start date: 29.10.2021
    • End date: 03.12.2021
    • Min. number of students: 3
    • Max. number of students: 10
    • Admission criteria: Basic statistical literacy (i.e., familiarity with at least one statistical software such as SPSS, Stata or R; familiarity with basic statistical analysis techniques, e.g., regression, anova, etc.)
    • Concluding assessment: yes
    • Assessment type: Take home assignment; students can choose one of the following three assignments:

    • Run a k-means cluster analysis, interpret and report results

    • Run a hierarchical cluster analysis, interpret and report results

    • Run LPA, interpret and report results

    Each assignment should also include some robustness checks and mention the limitations of the method used

    • With certificate: yes
    • Roster/schedule info:

    Session 1:  Friday, 29 October 2021 (14.00-16.00)

    Session 2:  Friday, 5 November 2021 (14.00-16.00)

    Session 3:  Friday, 12 November 2021 (14.00-16.00)

    Session 4:  Friday, 19 November 2021 (14.00-16.00)

    Session 5:  Friday, 26 November 2021 (14.00-16.00)

    Session 6:  Friday, 3 December 2021 (14.00-16.00)

    Session 7:  Friday, 10 December 2021 (14.00-16.00)

    Session 8:  Friday, 17 December 2021 (14.00-16.00)

    Session 9:  Friday, 14 January 2022 (14.00-16.00)

    Session 10:  Friday, 21 January 2022 (14.00-16.00)

    • Registration deadline: 30.09.2021
    • Available to: FSS PhD candidates and other PhD’s in the Social Sciences. No fee for FSS, VU and AISSR PhD candidates. Fee for others € 450,-

Graduate School of Social Sciences