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BASS Philosophy and Criminology

Year of entry: 2021

Course unit details:
Modelling Criminological Data

Unit code CRIM20452
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Offered by School of Social Sciences
Available as a free choice unit? No

Overview

H.G. Wells is often cited as saying that statistical understanding will one day be as important as being able to read or write. This course aims to provide you with some basic statistical literacy, the ability to understand statistics. Data is ubiquitous today and affects all aspects of your everyday life. Our goal here is to introduce you to some basic principles and ideas that are required to understand how data analysis works so that you develop a better appreciation of the stories you read about in the media, the arguments made by politicians, and the claims made by scientists around a variety of issues.

 Moreover, criminal justice agencies are increasingly adopting a "problem solving" and "evidence-led" discourse that requires them to employ individuals with the skills required to perform basic data analytical tasks in order to document patterns of problems, factors associated with them, and to evaluate responses to these problems. For particular positions, for example, crime analyst jobs, this type of skills are absolutely essential. More generally there is an increasing recognition that data analysis skills are helpful in many other professional sectors.

This course will further develop students’ own quantitative skills. It aims to equip you with the skills to explore and analyse data, encourage your curiosity, and in the process provide you with a set of abilities that are very desirable in the job market. The course will also introduce you to R, a free program for data analysis used by the likes of Facebook and Google and considered as the best tool for working with data.

 Taking this course is an eligibility criterion to benefit from the University of Manchester Q-Step summer internships.

Aims

1.   To develop students’ skills in manipulating, analysing and interpreting quantitative data;

2.   To develop skills necessary to undertake some simple data analysis and interpretation on issues relevant to criminology and criminal justice using a variety of datasets;

3. To provide an introduction to statistical inference and regression analysis

4.   To introduce students to the programming language R and the RStudio interface

5.   To provide students with the skills necessary to critically evaluate accounts of quantitative research;

6.   To develop basic skills on data carpentry

7. To develop students’ autonomy and independence as learners whilst encouraging collaborative practices and peer learning

Learning outcomes

Understand some of the basic principles underlying statistical analysis, including: samples and populations, normal distribution, confidence intervals, statistical significance, hypothesis testing, and statistical measures of association;

Read and interpret quantitative information resulting from statistical tests in the form of tables and charts; 

Learn how to recode variables and manipulate different types of R objects; 

Be able, at an introductory level, to apply statistical tests appropriate to the data, including chi-square, t-tests, regression analysis;

Have the skills necessary to produce reproducible research reports using markdown;

Take an active approach to their learning, participate in class, and take responsibility for finding help for difficulties they encounter in the coursework. 

 

 

Teaching and learning methods

Teaching in academic year 20/21 will reflect both University policy and local and national lockdown restrictions operating at the time of delivery. We will offer face-to-face teaching where possible and provide a like for like on-line experience for those unable to be on campus. 

Our teaching models will be flexible and allow us to adapt to changing conditions, however, the common intention across units is to provide (1) media, activities and other learning material that should be engaged with before scheduled teaching; (2) a timetabled 2-hour online lecture/workshop slot used for a range of online Q&A and follow-up activities; (3) a timetabled weekly 1-hour seminar/activity slot that will be face-to-face if possible and ‘live’ online if not/preferred; (4) weekly opportunity for 1:1 support. In total, there will be the opportunity for up to 30 hours of contact time.

Knowledge and understanding

Understand some of the basic principles underlying statistical analysis, including: samples and populations, normal distribution, confidence intervals, statistical significance, hypothesis testing, and statistical measures of association; Understand the different levels at which social characteristics (variables) are measured and how resulting data are distributed;

Intellectual skills

Be able to interpret the findings of statistical analysis;

Practical skills

Read and interpret quantitative information in the form of tables and charts; Be able to produce basic descriptive statistics for a dataset; Be able, at an introductory level, to apply statistical tests appropriate to the data, including chi-square, t-tests, correlations; Have the skills necessary to produce reports using word-processing, including colour charts, tables and graphs in various software packages;

Transferable skills and personal qualities

Take an active approach to their learning, participate in class, and take responsibility for finding help for difficulties they encounter in the coursework.

Assessment methods

20% of the final mark will be awarded for submission of weekly course exercises that are carried out in the lab sessions. There will be nine exercises.

80% of the final mark will be based on an assignment that requires students to analyse an existing dataset, to be provided. Students will be required to devise some research questions and analyse them with the dataset provided. Students will be required to produce a report of 3,000 words incorporating charts, tables and graphs, to be produced to a good quality standard.

Recommended reading

We will be using a combination of different materials.

For this course, you are expected to read the free online Statistical Reasoning materials of the Open Learning Initiative at Carnegie Mellon University as part of this module. You can think of it as the “required textbook” for this module (although it is a set of online textual materials with some exercises and videos to aid comprehension): https://oli.cmu.edu/jcourse/webui/guest/join.do?section=statreasoning

There is also a Workbook for Learning R Commander and Deducer that has been written specifically for this course unit by myself and also constitutes require reading material. This workbook is available through Blackboard as a collection of pdf files, one per course unit session. It is a guide that, through various practical exercises to be carried out primarily in our lab sessions, teaches you different aspects of the software we use for data analysis in this course. This guide complements the more conceptual territory covered by Statistical Reasoning. 

Study hours

Scheduled activity hours
Supervised time in studio/wksp 20
Tutorials 10
Independent study hours
Independent study 0

Teaching staff

Staff member Role
Reka Solymosi Unit coordinator

Additional notes

Information
Open to BA (Criminology) students for which this subject is compulsory. LLB (Law with Criminology) if not choosing LAWS20412 or LAWS20692 can also take this module subject to availability of space (in the computer clusters we use). Also available to all students across Humanities subject to the availability of places, preference will be given to BASS students in the criminology pathway. 

Pre-requisites: 

We assume students have taken LAWS20441 Making Sense of Criminological Data or a course unit that covers similar material (such as SOST10021 Unequal Societies or SOS Applied Statistics). If in doubt, do not hesitate to contact the course director before enrolling. Students that have not taken a more basic data analysis course (such as those) beforehand will find the materials in this course unit very challenging. Although all the examples in this course are taken from the field of criminology, criminological knowledge is not a requirement for this course. In fact, this unit can be a good option for those UG (social science) students that want to benefit from an introduction to R. 

 

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