MRes Criminology (Social Statistics) / Course details

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.

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.

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

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.

Weekly tests (x8, 30% total)

Exam (70%)

Feedback available via Turnitin

Preliminary main reading

• Agresti, A. (2018) Statistical Methods for the Social Sciences (5th 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)

Information
Compulsory for SRMS