Profile

Peer-Eric Keßler

Peer-Eric Keßler

Research Fellow

Sociology
Geisteswissenschaftliches Zentrum
Beethovenstraße 15, Room 4103
04107 Leipzig

Phone: +49 341 97 - 35652
Fax: +49 341 97 - 35669

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Professional career

  • since 10/2022
    Research associate (PhD student) at the Department of Sociology, University of Leipzig
  • 03/2019 - 09/2022
    Lecturer for statistical software (SPSS and R)
  • 10/2019 - 09/2022
    Tutor for "Introduction to Statistics" and "Applied Statistics"

Education

  • 10/2020 - 09/2022
    Sociology, Leipzig University, Germany (Master of Arts)
  • 10/2017 - 09/2020
    Sociology, Leipzig University, Germany (Bachelor of Arts)
  • 01/2017 - 06/2017
    Semester abroad with focus on international criminal law, Lund University, Sweden
  • 10/2015 - 09/2017
    Law studies, University of Greifswald, Germany (Interim Examination)
  • 10/2011 - 09/2015
    Law studies "Good Governance", University of Rostock, Germany (Bachelor of Laws)

  • Cognitive Sociology
  • Discrimination
  • Criminology


  • Keßler, P.-E.; Dittrich, F.; Heyden, A.; Meißelbach, C.; Melcher, R.
    Then They Simply Go Somewhere Else: An Exploratory Study of Crime Displacement in a German Context [Presentation] (2023)
    show details
  • Mühler, K.; Dittrich, F.; Fleps, T.; Grohmann, P.; Heyden, A.; Keßler, P.-E.; Radici, J.
    Die Leipziger Waffenverbotszone: Analysen zu Kriminalitätsverlauf, Akzeptanz und Sicherheitsgefühl
    Rothenburg (Oberlausitz): Eigenverlag der Hochschule der Sächsischen Polizei (FH). 2022.
    show details

more publications

Methods of Empirical Social Research

  • Reasearch Seminar (Part 1 and 2)

    The research seminar serves to impart knowledge and techniques for independently conducting empirical research. In the "Research Seminar II", data entry and statistical analysis take place. At the end of the module, theory and empirical results are combined in a research report.

  • Practice Seminar on Regression Methods

    This module serves to reinforce previous knowledge of methods and introduces the basic ideas and procedures for estimating regressions. The focus is on least squares estimation and the associated linear regression analysis, as well as on the maximum likelihood estimation procedure. The module is designed as an interplay between theoretical coverage of the models and their practical implementation on the PC using sample data sets.