Seminar „Regression Models for Hierarchical Data“

Semester: Sommer 2023
Course Number: 300250
Lecturer: Marvin Bürmann
Location: X-D2-105 (remote access: CIP-Pool Room 1)
Time: mondays 14-16h c.t., 03.04.2023- 14.07.2023

Notice:
All important information about the schedule of the seminar, the tools to be used and the requirements for the completion of study and examination achievements can be found in this document in the corresponding sections. If necessary, the document will be adapted during the semester. The current version of the document is always available at this URL.
URL (with “http://”" or without “www.”): lehre.mbuermann.de/syllabus_rmhd.html

The seminar

Content description

Many questions in the social sciences are best answered with data that have a hierarchical structure, i.e., data available at different levels. For example, to investigate whether differences between countries are systematically related to individual attitudes, information is needed at the individual level (attitudes) and at the country level (operationalization of country differences). Similarly, a hierarchical data structure is needed to study the consequences of life course changes at the individual level. In this case, the additional level besides the individual is the temporal level. While data in which individuals are nested in different contexts - such as individuals in countries - are usually analyzed with so-called multilevel models, panel models are usually applied for changes in individual life courses. In cases where individuals are observed in different contexts over time, hybrid forms of the two types of models are also used.

The seminar will first discuss in which cases a “normal” regression model is not the best method for answering research questions and what advantages multilevel and panel models offer in comparison. The mathematical and statistical foundations of each of these model types will be explained and practical exercises will be conducted using teaching data sets. Emphasis is placed on practical applications and the differences and similarities among the model types.

Learning objectives

Students should acquire the competencies to analyze hierarchical data sets adequately. This includes the correct modeling of hierarchical data in the context of multilevel and panel models as well as the model construction corresponding to a specific research question (selection of analysis steps and presentation of central results).

Participation requirements, necessary previous knowledge

Students should have a good understanding of linear regression analysis in order to participate adequately in the course. However, lack of prior knowledge can be compensated by self-study during the first weeks. The practical exercises will be carried out with the help of guided exercise sheets. Appropriate knowledge of the syntax in “Stata” is helpful in order to be able to participate adequately in the practical exercises. In principle, the exercises can also be completed using “R”, but the solutions for the exercise sheets will be provided and discussed as “Stata” syntax.

Digital tools

Moodle course

The Moodle course of the seminar is one central tool. It can be accessed via the normal learning room (“Moodle-Kurse” tab on the left-hand side) and via the following URL: https://moodle.uni-bielefeld.de/course/view.php?id=762. The Moodle course provides slides & excercises sheets for the respective meetings. The files for the credits are also to be submitted here.

Seminar folder on faculty server

Data as well as the solution files will be provided in a folder on the “CIPUX_veranstaltungen” drive (usually the “K” drive). In the folder (probably “buermann_rmhd”) there will be a “readonly” and a “public” folder. In the “readonly” folder the data and the solution files will be provided. In the “public” folder you can create your own subfolder (e.g. with the name of your access code) in which you work during the practical meetings. As subfolders for this folder I recommend: “do”, “log”, “derived” & “results”. The access to the drive is also possible via remote-access (see next section).

Stata remote access

External access to Stata and the CIP pool drive is possible. Instructions for CIP Pool Remote Access (Stata use and access to the teaching data version of SOEP) can be found on the Faculty of Sociology IT website: https://www.uni-bielefeld.de/soz/it/cip-pool.html. The password for access will be announced during the seminar. CIP-Pool 1 is the remote-access room for this seminar. An active VPN connection is required to remotely access the CIP pool (see next section).

VPN connection to the university network

Many services of the university are only available within the university network. This includes e.g. the access to paywalled literature. To access literature from home, a VPN connection is required. Appropriate instructions can be found on the following page of the university: https://www.uni-bielefeld.de/einrichtungen/bits/services/netzzugang/vpn/.

How to earn credit points

Participation points / Studienleistung (SL)

The study credits (Studienleistung (SL)) can be obtained by submitting solution files for the exercise sheets. The number of solution files to be submitted for the study credits can be reduced by a presentation on a scientific paper analyzing hierarchical data like the ESS. Without such a presentation, at least 4 of the 6 exercise sheets must be completed. A presentation (10-15 minutes) can reduce the number of solution dofiles to be submitted to 2. The solution files to the exercise sheets have to be uploaded to the corresponding submission folder of the Moodle course at the latest on Sunday evening before the seminar (the submission folder closes after 23:59!).

Exam points / Prüfungsleistung (PL)

The exam points (Prüfungsleistung(PL)) can be obtained by a seminar paper (Hausarbeit) on an own research question using the methods of analysis discussed in the seminar.
The data must be obtained, prepared and analyzed by the student. Information on the adequate preparation of seminar papers at
the AG Kroh can be found at http://www.uni-bielefeld.de/soz/personen/kroh/lehre.html.

1. Deadline for submission of seminar paper: 31.07.2023
2. Deadline for submission of seminar paper: 30.09.2023

Seminar schedule

Literature

Multilevel models

Hox, Joop J.; Moerbeek, Mirjam; van de Schoot, Rens (2010): Multilevel analysis: Techniques and applications: Routledge.

Hox, Joop J.; Roberts, J. Kyle (Hg.) (2011): Handbook of advanced multilevel analysis. ebrary, Inc. New York: Routledge (European Association of Methodology Series).

Rabe-Hesketh, Sophia; Skrondal, Anders (2008): Multilevel and longitudinal modeling using Stata: Stata Press.

Snijders, Tom; Bosker, Roel (1999): Multilevel analysis. An introduction to basic and applied multilevel analy-sis: London: Sage.

Panel models

Allison, Paul D. 2009. Fixed effects regression models: SAGE Publications.

Andreß, Hans-Jürgen, Katrin Golsch, und Alexander W. Schmidt. 2013. Applied Panel Data Analysis for Economic and Social Surveys: Springer.

Wooldridge, Jeffrey M. 2010. Econometric analysis of cross section and panel data: MIT press.

Special issues regarding panel models

Giesselmann, M., & Schmidt-Catran, A. W. (2022). Interactions in fixed effects regression models. Sociological Methods & Research, 51(3), 1100-1127.

Special issues regarding multilevel models

Heisig, Jan Paul; Schaeffer, Merlin; Giesecke, Johannes (2017): The Costs of Simplicity. Why Multilevel Models May Benefit from Accounting for Cross-Cluster Differences in the Effects of Controls. In: American sociological review 82 (4), S. 796–827.

Schmidt-Catran, Alexander W.; Fairbrother, Malcolm (2015): The random effects in multilevel models: getting them wrong and getting them right. In: European Sociological Review, jcv090.