EH165-7-SU-CO:
Categorical Data Analysis
2023/24
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer
Postgraduate: Level 7
Current
Monday 22 April 2024
Friday 28 June 2024
30
03 February 2023
Requisites for this module
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This course offers an application-oriented introduction to maximum likelihood (ML) based models for categorical, discrete choice, and count data. We begin with the basics of ML estimation and a discussion of the theoretical foundations of categorical, discrete choice, and count-data models. We then focus on exploring logistic and probit regression models and learn how to apply them in the statistical software package Stata. Afterwards, we cover interpretation and hypothesis for testing these kinds of models. Against this background, we will consider more complicated estimation strategies, including ordered logit and probit regression models, multinomial logits, count models, or discrete duration models. The course concludes with an overview of advanced techniques of models for time-series cross-section (TSCS) categorical, discrete choice, and count data.
No information available.
After this course, participants will be able to understand models for categorical, discrete choice, and count data most commonly used in the social sciences, and properly apply and interpret these models in their own work.
Course Prerequisites
Participants are assumed to have a basic knowledge of multiple linear regression. Some familiarity with linear algebra and experience estimating regression models with statistical software might also be helpful, but not essential to success in the course.
Representative Background Readings
Gujarati, Damodar N., and Dawn C. Porter. 2009. Essentials of Econometrics. Fourth Edition. New York: Irwin/McGraw-Hill.
Kohler, Ulrich, and Frauke Kreuter. 2012. Data Analysis Using Stata. Third Edition. College Station, TX: Stata Press.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications.
Long, Scott J., and Jeremy Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata. Third Edition. College Station, TX: Stata Press.
Required Reading – this text will be provided by ESS:
Train, Kenneth E. 2009. Discrete Choice Models with Simulation. Second Edition. Cambridge: Cambridge University Press.
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assessment one |
|
50% |
| Coursework |
Assessment Two |
|
50% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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