FSS:SOCn6206 Regression models - Course Information
SOCn6206 Regression models for categorical dependent variablesFaculty of Social Studies
The course is not taught in Spring 2023
- Extent and Intensity
- 1/1/0. 10 credit(s). Type of Completion: zk (examination).
- 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
- ! 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 5 student(s).
Current registration and enrolment status: enrolled: 0/5, only registered: 0/5
- fields of study / plans the course is directly associated with
- Gender studies (programme FSS, N-SOC)
- Population studies (programme FSS, N-SOC)
- Social Anthropology (programme FSS, N-SOC)
- Sociology (programme FSS, N-SO)
- Sociology (programme FSS, N-SOC)
- 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.
- 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
- 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
- Further Comments
- The course is taught once in two years.
The course is taught: every week.
- Permalink: https://is.muni.cz/course/fss/spring2023/SOCn6206