Veranstaltungsdetails

Multilevel Analysis (AMS, Teil I) (AMS)

Kurs-Nr.: 080393 | Zeit: Mo 16-18 | Raum: FNO 02/074 | Semester: SoSe 2018

Gerhartz

Vorraussetzungen

Registration in CampusOffice. The registration for this course starts on 26th February,2018.
Participants should have a basic understanding of linear regression models. A software-focused recap is given at the beginning of the course. Prior knowledge of the statistical software used in the practicals (i.e., Stata) is not assumed.

Beschreibung

Social scientists are often confronted with hierarchical structured data: Textbook examples include students grouped into classes (belonging to schools belonging to geographical region) as well as individuals living in neighbourhoods (grouped into cities grouped into regions) or, in a comparative research perspective, individuals grouped into countries. Theoretical models in these settings often assume cross-level interactions between the individual level and higher levels. A common assumption is that the social composition of a school has an effect on the individual student performance or that the neighborhood context influences the individual probability of delinquent behavior.
Statistical models referred to as multilevel (linear) models, mixed-effects models, covariance component models or random-effects models have been proposed in the literature for this kind of data and are often rated superior to simple OLS regression. The course will cover an introduction into practical application and interpretation of multilevel models for a range of different data structures. In addition to computer exercises, research examples and scientific papers using multilevel analysis in different fields will be discussed. Please note that the course will be held in English.

Voraussetzungen für Studiennachweise / Modulprüfungen

Modulprüfung: active participation, completion of exercises and term paper
Studiennachweis: active participation, completion of exercises

Literatur

  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.
  • Hox, J. (2002). Multilevel analysis. Mahwah, NJ [u.a.]: Erlbaum.
  • Kreft, I., & Leeuw, J. (2002). Introducing multilevel modeling. London [u.a.]: Sage.
  • Rabe-Hesketh, S. & Skrondal, A. (2012): Multilevel and Longitudinal Modeling Using Stata. Volume I: Continuous Responses. College Station, TX: Stata Press
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed). Thousand Oaks: Sage Publications.