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School of Social Sciences

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BAEcon Economics
Learn how the social sciences can help you to understand today's world.

BAEcon Economics

Year of entry: 2018

Course unit details:
Modelling Criminological Data

Unit code LAWS20452
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Offered by Law
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 practivices and peer learning

Learning outcomes

Understand some of the basic principles underlying statistical analysis, including: samples and populations, normal distribition, 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 methods will combine lab sessions, lectures, group discussion, interactive teaching and private study. Each week we will have a two-hour lab session and a feedback workshop to discuss homework solutions and to clarify understanding. We used something close to the “flip teaching” method. This means that there is a greater expectation that you will come prepared to class (i.e., having done the required reading) and it also means that you will spend most of the contact time working through a set of computer exercises trying to put to practice the knowledge acquired through your reading.

There is one two-hour lab session per week (from week 1 to week 10). Lab sessions will introduce you to some of the statistical principles and the concepts underlying our use of software for data analysis. During the lab period, students will work on computing exercises to develop and test their understanding of the material presented online. The course coordinator and the teaching assistants will help you to resolve problems in dealing with the software and the interpretation of results.

Although there won’t be any formal lecturing during most of these lab sessions, the materials you will be provided during these interactive sessions will contain hyperlinks to video presentations or reading material that you will be able to consult for further conceptual clarification of the topics being explored. The computer clusters do not have headphones attached to the computers. Therefore, you are strongly recommended to bring your own headphones so that you can watch (and listen to) these videos during the lab sessions.

From week 2 to 11 of the semester we will have one-hour Feedback Support Sessions led by the course coordinator. These sessions will focus on explaining the answers to the previous week homework and further clarifying concepts.

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
Juan Medina-Ariza 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 ti availability of space (in the computer clusters we use). Also available to all students across Humanities subject to the availability of places, preference will ve 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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