MRes Criminology (Social Statistics)

Year of entry: 2021

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Course unit details:
Statistical Foundations

Unit code SOST70151
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Social Statistics
Available as a free choice unit? Yes

Overview

To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions.

 

Aims

To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions.

 

Learning outcomes

On successful completion of this course unit, students will

•understand and do calculus involving fundamental concepts in probability theory such as independence and conditional probabilities

•have working handle on random variables and their properties

•have a basic understanding of estimators and how these relate to a model for data

•being able to perform basic tests of hypothesis and being able to generalise this understanding beyond the standard cases

•critically assess the extent to which a statistical analysis meets required assumptions

•have a broad knowledge general issues in statistical inference

Teaching and learning methods

Twelve teaching occasions comprising a lecture component and a practical. The practical element may involve computer based activities and/or discussion sessions. Computer exercises will be done using the R environment and will not be scheduled every week. A number of extra tutorials led by the course TAs will be scheduled in addition.

Assessment methods

Assessment task

Length

Weighting within unit (if relevant)

Examination at the end of semester 1.

2 hours

 60%

Set Exercise

 

 30%

Other

 

 10%

 

Recommended reading

Agresti, A. and Finlay, B. (2018). Statistical Methods for the Social Sciences (4th Edition). Pearson International Edition.

Online learning modules on R

https://www.datacamp.com/swirl-r-tutorial

http://eclr.humanities.manchester.ac.uk/index.php/R

Additional readings may include excerpts from

·       Bluman, A. G. (2012). Probability demystified. McGraw-Hill. 

 .         Gill, J. (2006) Essential Mathematics for Political and Social Research. Cambridge University Press. (Electronic version available in UoM library)

·       Dekking, F.M., Kraaikamp, C., Lopuhaä, H.P., Meester, L.E. (2007). A Modern Introduction to Probability and Statistics: Understanding Why and How. Springer

·       Crawshaw, J., Chambers. J. (2014). A concise course in Advanced level statistics: with worked examples. Nelson Thornes Ltd; 4th  Revised edition 

 

Study hours

Scheduled activity hours
Lectures 20
Tutorials 8
Independent study hours
Independent study 122

Teaching staff

Staff member Role
Eduardo Fe Rodriguez Unit coordinator
Alexandru Cernat Unit coordinator

Additional notes

Information
Compulsory for SRMS

Thursday 10-12 remote study.

 

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