SOCn6206 Regression models for categorical dependent variables

Faculty of Social Studies
Autumn 2021

The course is not taught in Autumn 2021

Extent and Intensity
1/1/0. 10 credit(s). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
prof. Martin Kreidl, Ph.D. (lecturer)
Guaranteed by
prof. Martin Kreidl, Ph.D.
Department of Sociology - Faculty of Social Studies
Contact Person: Ing. Soňa Enenkelová
Supplier department: Department of Sociology - Faculty of Social Studies
Prerequisites
! NOW ( SOC561 Regression models for categ. ) && ! SOC561 Regression models for categ.
Reasonable exposure to OLS regression and multi-purpose statistical software (such as STATA). Students should complete SOC662, or SOC591, or SOC660 (quantitative variant), or equivalent, prior to enrolling in SOC561. Solid knowledge of English is necessary - some lectures/seminars may be presented by an English-speaking guest lecturer
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 18 student(s).
Current registration and enrolment status: enrolled: 0/18, only registered: 0/18
fields of study / plans the course is directly associated with
Course objectives
This course introduces students into the filed of qualitative dependent-variable models (such as binary and polynomial logistic regression).
Learning outcomes
Students will be able to independently utilize categorical-dependent variable models in their own, theoretically-driven quantitative data analyses. They will be able to identify a proper analytical tool for a given substantive problem/available data, set up the data, carry out the analysis, evaluate, present and interpret the results.
Syllabus
  • binary logistic regression and its applications
  • - school-continuation model
  • - discrete-time event-history model
  • - analysis of response-based samples
  • - logit model for contingency tables
  • - logit model for grouped/blocked data
  • discrete-choice models
  • ordinal logistic regression and its applications (model for adjacent categories)
  • multinomial logistic regression
  • logit analysis for longitudinal and other clustered data
Literature
    required literature
  • LONG, J. Scott and Jeremy FREESE. Regression models for categorical dependent variables using Stata. 3rd ed. College Station, TX: Stata press, 2014. xxiii, 589. ISBN 9781597181112. info
  • TREIMAN, Donald J. Quantitative data analysis : doing social research to test ideas. Edited by Deirdre D. Johnston - Thomas J. Grites. San Francisco: Jossey-Bass, 2008. xxxii, 443. ISBN 9780470380031. info
    recommended literature
  • ACOCK, Alan C. A gentle introduction to Stata. 6th edition. College Station, Texas: A Stata press publication, StataCorp LLC, 2018. xl, 570. ISBN 9781597182690. info
  • CLEVES, Mario Alberto, William GOULD and Yulia V. MARCHENKO. An introduction to survival analysis using Stata. Revised third edition. College Station, Texas: Stata Press, 2016. xxx, 428. ISBN 9781597181747. info
  • RABE-HESKETH, Sophia and Anders SKRONDAL. Multilevel and longitudinal modeling using stata. 3rd ed. College Station: Stata Press, 2012. xxii, 501-. ISBN 9781597181044. info
  • RABE-HESKETH, Sophia and Anders SKRONDAL. Multilevel and longitudinal modeling using stata. 3rd ed. College Station: Stata Press, 2012. xxx, 497. ISBN 9781597181037. info
  • LONG, J. Scott. The workflow of data analysis using stata. 1st ed. Texas: Stata Press, 2009. xxvii, 379. ISBN 9781597180474. info
  • RABE-HESKETH, Sophia. A handbook of statistical analyses using Stata. Edited by Brian Everitt. 4th ed. Boca Raton, Fla.: Chapman & Hall/CRC, 2007. ix, 342. ISBN 1584887567. info
Teaching methods
lectures, PC sessions, homework, final paper
Assessment methods
graded homework, final empirical paper
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2023.
  • Permalink: https://is.muni.cz/course/fss/autumn2021/SOCn6206