EH137-7-SU-CO:
Multilevel Statistical Models For The Social Sciences Using Stata
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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Statistical models are important tools for analysing quantitative datasets. In the social sciences, it is also common to refine or adjust models, beyond their standard formulations, in order to take account of the complexities of 'real life' social data. Participants in course 1E will learn about statistical models in the social sciences and about certain popular strategies of using models to analyse complex or multilevel data. Students will learn:
– how to specify, formulate and interpret common types of statistical model
– how to understand, implement and interpret multilevel models
– how to assess and compare other ways of taking account of complex and multilevel data within a modelling framework
– how to enhance complex data such as by merging variables or datasets and analysing them with appropriate statistical models
No information available.
The course seeks to provide participants with a strong understanding of how statistical models can be applied in the social sciences when data is complex and/or multilevel in its nature.
Participants should learn
how relevant statistical models are formulated and interpreted
the relative attractions and limitations of different model strategies
practical skills in fluently handling and analysing complex data using one or more relevant software packages
There are a number of benefits to learning how to understand and to implement statistical models for complex and multilevel data. Multilevel models are widely used in the social sciences so there are many good reasons to learn in detail about their theory and their practical implementation. Further course materials explore several other important but under-utilised options in the specification of statistical models and in making good use of complex datasets. Training in these areas should provide course participants with the confidence to compare between the strengths and limitations of different plausible models, and equip them with valued practical skills in using software to work with data and run statistical models.
Course Prerequisites:
This is an introductory course, designed for people who have little or no previous experience in applying models to multilevel or complex data. It is expected that participants will have had some previous training in social statistics – for example, the course is best suited to participants who are fluent in popular descriptive analytical techniques and some of the statistical tests behind them (e.g. chi-square tests; correlation values), and who have had at least some previous exposure to using regression models in the social sciences (e.g. multiple regression and/or logistic regression). Teaching sessions will take basic versions of these regression models as a starting point, and build onwards to multilevel models and other related extension topics in statistical modelling. Most participants are likely to benefit from preparatory study or revision of materials which cover generating and interpreting regression outputs.
The course is also best suited to participants with at least some previous experience in using statistical software packages for social science data analysis. The course features lab materials available in several packages (Stata, SPSS, R and MLwiN, with Stata used most often). The lab materials also make use of several different social science datasets. Previous exposure to the ‘syntax’ languages of at least one of these packages will be an advantage. Course materials include some introductory documentation to help with using software, and for this reason the course should still be accessible to people who have little previous experience, however students without any background in the programming of software using syntax should be prepared that extra effort will probably be needed near the start of the course in order to make good use of the lab exercises.
Required texts
This text will be provided by ESS: Hox, J., Moerbeek, M. and van de Schoot, R. (2017). Multilevel Analysis: Techniques and Applications, Third edition. London: Routledge.
For Stata users, we also recommend accessing or purchasing the following:
Rabe-Hesketh, S. and Skrondal, A. (2022) Multilevel and Longitudinal Modelling Using Stata, 4th Edition (2 volume set). College Station, Tx: Stata 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 |
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Coursework weighting |
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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