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XX50219: AQM 2 - Advanced modelling techniques for social sciences

Follow this link for further information on academic years Academic Year: 2019/0
Further information on owning departmentsOwning Department/School: Faculty of Humanities & Social Sciences (units for MRes programmes)
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 50%, EX 50%
Further information on unit assessment Assessment Detail:
  • Exam (EX 50% - Qualifying Mark: 40)
  • Essay (CW 50% - Qualifying Mark: 40)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Further information on descriptions 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.
Further information on programme availabilityProgramme availability:

XX50219 is a Designated Essential Unit on the following programmes:

Department of Social & Policy Sciences
  • THXX-AFM54 : MRes Advanced Quantitative Methods in Social Sciences
  • THXX-AFM76 : MRes Advanced Quantitative Methods in Social Sciences (Leading to PhD)
  • THXX-APM54 : MRes Advanced Quantitative Methods in Social Sciences

XX50219 is Optional on the following programmes:

Department of Computer Science
  • RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence

Notes: