It is one of the fastest growing statistical software packages, one of the most popular data science software packages, and, importantly, it is open source (free!). This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. However, they will expect you to present logical arguments supported by evidence. It is impossible to consider any of these in isolation. The introductory methods course has two primary aims. In the lecture this week, we discuss the concept of causality and particularly focus on distinguishing between observational and experimental strategies for making causal claims from quantitative data. The introductory course has two primary aims. Department policy requires that penalty points are deducted for essays that are late and does not allow individual lecturers to grant extensions. You can also access this site with a short URL: http://bit.ly/PUBLG100. Browse Hierarchy PUBL0055: PUBL0055: Introduction to Quantitative Methods. This module (or the Advanced Quantitative Methods module) is required of all students pursuing an MSc from the School of Public Policy, including degrees in Democracy and Democratization, European Public Policy, Global Governance and Ethics, International Public Policy, Public Policy, and Security Studies. Solutions 3. First, students will be introduced to statistical models that researchers and policymakers use in answering social, political and economic questions. Seminar 2.2. PUBLG100A and PUBLG100B . 2nd ed. By the end of the course, students should be able to understand the quantitative tools employed in political, social, and economic research; to perform data analysis using the statistical software R and interpret results; and to fruitfully employ introductory quantitative methods in their dissertation research and in subsequent careers. Students must pass this course to successfully complete the MSc degree. Home; My Lists; My Bookmarks; Feedback; Log In; Accessibility ; Browse Hierarchy PUBL0055: PUBL0055: Introduction to Quantitative Methods. We will introduce the “potential outcomes” framework for thinking about causal inference, and describe the “fundamental problem of causal inference”. Garner R. The Joy of Stats: A Short Guide to Introductory Statistics in the Social Sciences. If you experience any difficulties that mean you are not able to study to the best of your ability and struggle to meet deadlines, then you should speak to your personal tutor for help filling out and submitting an Extenuating Circumstances Form. This form of teaching is very poorly suited to online delivery, as it would entail large groups of us sitting on a video call, mostly in silence, as you worked your way through a problem set. Justify your choice. Quantitative applications in the social sciences Bray, James H., and Scott E. Maxwell, Multivariate Analysis of Variance (Beverly Hills: Sage Publications, 1985), A Sage university paper. Introduction to Quantitative Methods. Back to POLSC_SHS: Political Science. All materials for the course will be hosted here on the dedicated course website (https://uclspp.github.io/PUBL0055/). Module leader for the Introduction to Quantitative Methods for the On-line MRes in Educational & Social Research. In other years, we would all sit in computer labs together and we would correct issues with your code, and we would discuss common problems as they come up. We strongly encourage all students to complete these assignments in advance of the solutions being released each Monday. Every quantitative social scientist needs to know how to operate at least one piece of statistical software. • Numeric and … They will not censor any topics, and they will expose you to controversial issues, questions, facts, views, and debates. Numerical Methods: The need for numerical techniques when analytical solutions are not available. Back to POLSC_SHS: Political Science. Your lecturer will not limit what can be discussed in the seminar, as long as it is relevant to the subject. By the end of the course, you should be able to understand basic research methods, apply them to real world problems and evaluate their use in published research. The goal of these seminars is to provide you with ample time to ask questions about the problem set, and particular issues that relate to coding in R. During your allocated seminar time, you will be able to ask questions of the teacher either by message or by requesting a video call; speak with other students about the problem set; and watch short live demonstrations from your seminar teacher. Instead, we will hold virtual seminars via Zoom. Introduction to Quantitative Methods. The midterm coursework will review basic theory – testing whether students have done all the required reading and the assignments – and also include a practical component which will require students to complete tasks using R. The midterm will be set on Friday 6th November at 6pm and will be due on Wednesday 11th November at 2pm. Linear systems – solving systems of linear equations and methods from numerical linear algebra. 3.1 Overview. 1.1 Overview; 1.2 Seminar; 1.3 Homework; 2 Causality. It is important to note that we do not expect any student to have prior programming experience. Seminar 1.2. In this course, we will be teaching you how to use R. R is statistical software that allows one to manipulate data and estimate a wide variety of statistics. You are encouraged to engage with these facts and views in a respectful manner. This will make the seminars more engaging, as you will spend less time working on trivial technical details, and more time talking about the substantive importance of the statistical results.