Modelling Criminological Data

CRIM20452 Modelling Criminological Data 20 Semester 2 Undergraduate Level 2 Ana Maria Nicoriciu Unit coordinator Thiago Oliveira Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 Data is ubiquitous today and affects all aspects of everyday life. This course aims to provide the student with basic statistical literacy, the ability to understand statistics. In doing so, you will develop a better appreciation of the crime (and many other) stories you read in the media, the arguments made by politicians, and the claims made by high mark in this module will render you eligible for paid Q-Step <a href="https://www.humanities.manchester.ac.uk/q-step/internships/">summer internships.</a>Indicative content: (1) Introduction to the course; (2) Causality in social science; (3) Data visualisation with ggplot2; (4) Data carpentry; (5) Statistical inference; (6) Hypothesis testing; (7) Relationships between categorical variables (8) Regression models; (9) Logistic regression; (10) Course review. Data is ubiquitous today and affects all aspects of everyday life. This course aims to provide the student with basic statistical literacy, the ability to understand statistics. In doing so, you will develop a better appreciation of the crime (and many other) stories you read in the media, the arguments made by politicians, and the claims made by high mark in this module will render you eligible for paid Q-Step <a href="https://www.humanities.manchester.ac.uk/q-step/internships/">summer internships.</a>Indicative content: (1) Introduction to the course; (2) Causality in social science; (3) Data visualisation with ggplot2; (4) Data carpentry; (5) Statistical inference; (6) Hypothesis testing; (7) Relationships between categorical variables (8) Regression models; (9) Logistic regression; (10) Course review. The unit aims to (1) enhance students' understanding of criminological theory in context with particular forms of violence; (2) develop students' awareness of the links between approaches to research, theory construction and policy surrounding violence; (3) explore the complex relationships between power, inequality and violence, drawing upon examples such as ethnicity and gender; (4) examine knowledge and understanding of various forms of violence through critical discussion. On completion of the course, the student will be able to (1) read and interpret quantitative information in the form of tables and charts; (2) understand basic principles underlying statistical analysis; (3) produce basic descriptive statistics for a dataset; (4) apply statistical tests appropriate to the data; (5) interpret statistical analysis; (6) produce high-quality reports.   Other (i) analyse, critique and (re-)formulate a problem or issue; (ii) rapidly and thoroughly review/rate argument and evidence from targeted bibliographic searches; (iii) plan, structure and present arguments in a variety of written formats and to a strict word limit, (iv) express ideas verbally and organise work effectively in small teams for a variety of written and oral tasks; (v) obtain, manipulate and (re-)present different forms of data; (vi) manage time effectively; (vii) reflect on and improve performance through feedback. Teaching and learning across course units consists of: (1) preparatory work to be completed prior to teaching sessions, including readings, pre-recorded subject material and online activities; (2) a weekly whole-class lecture or workshop; (3) a tutorial; and (4) one-to-one support via subject specific office hours. 0 Other 100% The course is assessed by means of weekly homework submissions (20%) and a 2500-word project report (worth 80%) Formative feedback (both individual and collective) will be given on tasks and contribution in class. Summative feedback will be given on both assessed components via Blackboard (Grademark). CRIM20441 Making Sense of Criminological Data Co-Requisite Compulsory CRIM20452 course requirement Y Kosuke Imai (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press. Practical classes & workshops 20 Seminars 10 0 70 Across their course units each semester, full-time students are expected to devote a ‘working week’ of around 30-35 hours to study. Accordingly each course unit demands around 10-11 hours of study per week consisting of (i) 3 timetabled teacher-led hours, (ii) 7-8 independent study hours devoted to preparation, required and further reading, and note taking.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. This course is available to study abroad students if they are able to demonstrate sufficient quantitative training ideally R software to engage successfully with the course. 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.