XX50219: AQM 2 - Advanced modelling techniques for social sciences
[Page last updated: 15 October 2020]
Academic Year: | 2020/1 |
Owning Department/School: | Faculty of Humanities & Social Sciences (units for MRes programmes) |
Credits: | 6 [equivalent to 12 CATS credits] |
Notional Study Hours: | 120 |
Level: | Masters UG & PG (FHEQ level 7) |
Period: |
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Assessment Summary: | XX50219-CV19 |
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Supplementary Assessment: |
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Description: | Aims: This is an advanced level quantitative methods course designed to equip students with a range of technical skills covering the a number of major techniques of data analysis used in social sciences but not covered in XX50218 (AQM 1 - Experimental and Quasi-experimental Quantitative Methods in Social Science). The primary aims of the unit are to: * Introduce a number of approaches to data analysis used in different disciplinary backgrounds across the social sciences. * Provide students with both the theoretical understanding of these techniques and practical experience in utilizing them. * Facilitate critical appraisal of research findings using these techniques. * Provide students with an insight into how these various quantitative methods could be applied in their own field of interest. Learning Outcomes: Students will: * acquire knowledge of and competence in the use of advanced quantitative techniques drawn from a range of social science disciplines; * be able to produce, use and interpret the results from structural equation models; * understand and be able to implement path analysis; * understand and be able to implement social network analysis; * understand and be able to implement latent class models; * understand and be able to implement linear mixed models; * understand and be able to implement meta analyses. Skills: * Ability to develop rigorous arguments through precise use of concepts and models; * ability to critically evaluate different research approaches and apply appropriate design principles and advanced quantitative techniques to particular disciplinary contexts; * ability to evaluate research findings produced by a range of different advanced empirical methods; * proficiency in using data from large scale surveys; * proficiency in construction of new data sets; * proficiency in descriptive and inferential statistics and ability to use, model and interpret multivariate statistical data and analysis using the range of techniques covered on the unit. Content: Topics to be covered include: structural equation models, path analysis, social network analysis, latent class models, linear mixed models and meta analysis techniques. |
Programme availability: |
XX50219 is a Designated Essential Unit on the following programmes:Department of Education
XX50219 is Optional on the following programmes:Department of Computer Science
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