# FSS:SOCn6206 Regression models - Course Information

## SOCn6206 Regression models for categorical dependent variables

**Faculty of Social Studies**

Spring 2023

The course is not taught in Spring 2023

**Extent and Intensity**- 1/1/0. 10 credit(s). Type of Completion: zk (examination).
**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**- !
**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.
**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**- 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

*required 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

*recommended literature*- LONG, J. Scott and Jeremy FREESE.
**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 once in two years.

The course is taught: every week.

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