You are reminded that it is an academic offence to use the work of others without acknowledgement. It is impossible to consider any of these in isolation. • Summarising numeric and categorical data. Students must pass this course to successfully complete the MSc degree. We will make extensive use of UCL’s virtual learning platform, Moodle. In the lecture this week, we provide an introduction to the course and discuss where quantitative methods fit within the broader process of doing social science research. Load the non-western foreigners dataset from week 2. load ("non_western_foreingners.RData") Exercise 4. You should download and install both R and RStudio on your personal computers before the course starts. Attendance during these seminar hours is mandatory and we will take a register at the beginning of the session. However, they will expect you to present logical arguments supported by evidence. You may disagree with some facts or views that you read or hear in the classroom. The introductory methods course has two primary aims. Introduction to Quantitative Methods. Note that we will not be answering substantive questions over e-mail. Home; My Lists; My Bookmarks; Feedback; Log In; Accessibility ; Browse Hierarchy PUBL0055: PUBL0055: Introduction to Quantitative Methods. Root finding - bisection and Newton-Raphson Method. This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. Each week we will add new material to the site, and we expect you to review this material before each class. 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. • Numeric and … Numerical integration – trapezoidal, Simpson and midpoint rules. You are also explicitly prohibited from aiding or abetting in any of these actions. All of the main course content will be delivered by pre-recorded “lectures” which will be hosted on the course website. Instead, we will hold virtual seminars via Zoom. 2.1 Overview; 2.2 Seminar; 2.3 Homework; 3 Describing quantitative data. 2nd ed. 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. 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. About this course. There are two lectures for this module – Dr Blumenau and Professor Lauderdale – each of whom will deliver 5 lectures. Students taking this module are not permitted to take PUBLG088 Advanced Quantitative Methods. Back to POLSC_SHS: Political Science. 1 Introduction to Quantitative Methods. 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!). Back to EDPAS_IOE: Education, Practice and Society. The introductory course has two primary aims. The course will cover the following topics: • Introduction to quantitative research. Justify your choice. The final coursework will also cover both theory and practical questions, and will require students to address specific research or policy questions using real-world datasets. Every quantitative social scientist needs to know how to operate at least one piece of statistical software. Each week, you will complete a problem set which involves writing code in the R programming language (see below for more details) and interpreting the results. This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. In the lecture this week, we discuss descriptive statistics and visualisation of data, starting with single variables and then looking at examples with multiple variables.. As part of the UCL academic community, all staff, speakers, and students share these responsibilities: Everyone must respect freedom of thought and freedom of expression. 1.1 Overview; 1.2 Seminar; 1.3 Homework; 2 Causality. The course has two marked components, a midterm coursework (worth 25% of the course mark) and a final coursework (worth 75% of the course mark). 2.1 Overview. Seminar 2.2. The assessment for this course is a 24 hour prior disclosure exam which will be completed in the last week of the term. 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. By agreeing to take this module, you agree to abide by these terms. Lists linked to PUBL0055: Introduction to Quantitative Methods. In seminar this week, we will cover the following topics:. 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. Linear systems – solving systems of linear equations and methods from numerical linear algebra. This course is designed to introduce you to and help you become familiar with tools of quantitative data analysis for the social sciences. This introductory statistics and research methods course is concerned with all areas of statistics, from data collection through to interpretation. The final coursework will be set on Friday 18th December at 6pm and will be due on Monday 11th January at 2pm. It is important to note that we do not expect any student to have prior programming experience. 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. Seminar 1.2. We will teach you R during the course. They will not censor any topics, and they will expose you to controversial issues, questions, facts, views, and debates. We will introduce the “potential outcomes” framework for thinking about causal inference, and describe the “fundamental problem of causal inference”. • Research question development. Solutions 2. Working from data that we provide, you will be asked conduct various statistical analyses using R, and also to produce substantive responses to the questions posed. Solutions 3. If you ask us a substantive question via e-mail, we will simply ask you to post it on Moodle. Second, the course will equip students to use one or more of the discussed techniques in their MSc dissertation. Given this year’s unusual circumstances, teaching delivery will be split into two different modes of delivery. Browse Hierarchy PUBL0055: PUBL0055: Introduction to Quantitative Methods. We will use Moodle as the primary platform for managing communications for this course, particularly through the Discussion Forum that is listed on the Moodle page for this course. We will also be using the RStudio user-interface, which makes operating R somewhat easier. First, students will be introduced to statistical models that researchers and policymakers use in answering social, political and economic questions. This is a much more efficient mode of communication than e-mail because it allows you to answer each other’s questions, which will be much faster than waiting for a response from us, and for the entire class to see our responses, ensuring that we do not answer the same question multiple times over e-mail. North York, Ont: University of Toronto Press; 2010. Introduction to Quantitative Analysis 1.1. This course is designed to introduce you to and help you become familiar with tools of quantitative data analysis for the social sciences.

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