MY464 Introduction to Quantitative Analysis for Media and Communications
Course information
Course Description
This course is intended for those with little or no past training in quantitative methods. The course is an intensive introduction to some of the principles and methods of statistical analysis in social research. Topics covered in MY464 include descriptive statistics, basic ideas of inference and estimation, contingency tables and an introduction to linear regression models. For those with some quantitative training the slightly more advanced course MY452 (Applied Regression Analysis) would be more appropriate, followed by other Department of Methodology and Department of Statistics courses on quantitative methods, such as MY454 (Applied Statistical Computing), MY455 (Multivariate Analysis and Measurement), MY456 (Survey Methodology), MY457 (Causal Inference for Observational and Experimental Studies), MY459 (Quantitative Text Analysis), ST416 (Multilevel Modelling), and ST442 (Longitudinal Data Analysis).
Course Objectives
This course aims to impart a level of familiarity suitable for a moderately critical understanding of the statistical material in the journals commonly used by students in their work and the ability to use some elementary techniques.
Teaching
Lectures: 2-hour in-person lecture every week.
Applied exercises: Each week there will be an exercise for students to complete in which the ideas covered in the lecture for that week will be applied to a real data set using the software package R/RStudio. Each exercise will have an accompanying explanatory video and a multiple-choice quiz to be completed on Moodle to check your learning.
Seminars: Students will attend a one-hour seminar each week, starting in Week 2. The seminars will go over the material covered in that week’s lecture, the corresponding applied exercise and provide a forum for students to ask questions and discuss the material covered in the course. Seminars will be available to attend in person and online. Please consult the on-line timetables for the times and locations of the class groups. The allocation of students to seminars is done through LSE for You. This will be explained in the first lecture and on the Moodle page. Please contact the course administrator listed on the Moodle page if you have any issues.
Course Materials
Coursepack: This coursepack is the main course text. It is available to be viewed online at https://lse-methodology.github.io/MY464/ You can view the coursepack in HTML form, or download it as a PDF or ePub to view offline.
Lecture slides: Copies of the slides displayed during the lectures can be downloaded from the MY464 Moodle page.
Recommended course texts:
Alan Agresti and Christine Franklin (2013). Statistics: The Art and Science of Learning from Data (Third Ed.). Pearson.
Alan Agresti and Barbara Finlay (2013). Statistical Methods for the Social Sciences (Fourth Ed.). Pearson
Earlier/later editions are also suitable. While neither of these books is absolutely required, you may wish to purchase one if you would like to have additional explanation, examples and exercises to supplement the coursepack. Of these two, Agresti and Finlay is a bit more advanced. It is also the recommended additional course text for MY452 (which also has a coursepack similar to this one), so you may want to purchase it if you are planning to also take that course.
Other text books: There are hundreds of introductory textbooks on statistics and quantitative methods, many of them covering almost identical material. If you have one which you would like to use, and which looks as if it covers the same material at about the same level as this course, then it is probably suitable as additional reading.
- There are also many books and online resources which focus on the R/RStudio software package used in the computer classes. We do not consider them necessary for this course, or for learning statistics.
MY464 on Moodle
The course materials are all available on Moodle. Go to http://moodle.lse.ac.uk/ and login using your username and password (the same as for your LSE e-mail). Then in the select courses dialogue box type in MY464, and in search results click on MY464. The site contains the structure of the course week by week, the readings, weekly applied exercises and the associated data sets, coursepack and other materials, as well as a section on news and announcements.
Notes on studying for the course
To learn the material from this course you must do the work every week since it is cumulative; if you miss a week or two there is a chance that you will struggle to catch up. Also bear in mind that most people cannot learn quantitative techniques passively by just watching the lectures and reading the occasional chapter in a textbook. To learn statistics you have to do it; there are no shortcuts. Thus in addition to a lecture there will be a weekly applied exercise (in which you do some data analysis and interpretation using R/RStudio - instructions will be provided). Doing the exercises and discussing them in the weekly class is the best way to make sure you have understood and can apply what was covered in the lectures. If you are having any trouble this will reveal what the problem is. Thus the course is designed to have multiple, reinforcing ways of helping you learn this material.
Examinations/assessment
There will be a two-hour examination in January in the LENT Term. The exam will be completed online during a three hour window. Examination papers from previous years are available for revision purposes at the LSE library web site. 2018-19 was the first year that MY464 has existed, but the past exams for MY451 provide a good guide to the kinds of questions that we ask. Exams vary from year to year. Some questions closely follow questions that you will have answered in the homeworks or have seen on past exam papers. Other require you to apply the principles you have learned in new ways. Students should understand that past examinations should only be used as rough guides to the types of questions that are likely to appear on the examination.
Computing
Students must know their Username and Password in time for the first applied exercise in week 1. This information can be obtained from IT Help Desk (Library, 1st floor). The software package used for MY464 is R/RStudio, which will be introduced in the first applied exercise in week 1.
Feedback
We welcome any comments you have on the course. If there are any problems that we can deal with, we will attempt to do so as quickly as possible. Speak to any member of the course team, or to your departmental supervisor if you feel that would be easier for you. Also please let us know if you find any errors or omissions in the coursepack, so that we can correct them.
Acknowledgements
This coursepack bears many traces of previous materials and all of their authors, Colm O’Muircheartaigh, Colin Mills, Matt Mulford, Fiona Steele, Paul Mitchell, Sally Stares, Jouni Kuha, and Ben Lauderdale. Many thanks to Farimah Daftary, Sue Howard, Jon Jackson, Paul Mitchell, Indraneel Sircar, and many students of previous years for comments and suggestions which are incorporated in the current revision.
Course Programme
| Week 1 | |
| Lecture | Course overview and organisation. Introduction to basic concepts |
| Exercise | Familiarisation with R/RStudio (no seminar week 1) |
| Coursepack | Chapter 1 |
| Week 2 | |
| Lecture | Descriptive statistics for categorical variables |
| Exercise/seminar | Loading data into R/RStudio, descriptive statistics |
| Coursepack | Sections 2.1–2.4 and 2.8 |
| Week 3 | |
| Lecture | Descriptive statistics for continuous variables |
| Exercise/seminar | Descriptive statistics for categorical variables |
| Coursepack | Sections 2.5–2.7 |
| Week 4 | |
| Lecture | Analysis of two-way contingency tables |
| Exercise/seminar | Descriptive statistics for continuous variables |
| Coursepack | Chapters 3 and 4 |
| Week 5 | |
| Lecture | Inference for means in two populations |
| Exercise/seminar | Analysis of two-way contingency tables |
| Coursepack | Chapters 6 and 7 |
| Week 6 | |
| Reading Week | No lecture, no exercise/seminar |
| Week 7 | |
| Lecture | Inference for proportions in one and two populations |
| Exercise/seminar | Inference for means in two populations |
| Coursepack | Chapter 5 |
| Week 8 | |
| Lecture | Correlation and simple linear regression as descriptive methods |
| Exercise/seminar | Inference for proportions in one and two populations |
| Coursepack | Sections 8.1–8.3.4 |
| Week 9 | |
| Lecture | Inference for the simple linear regression model, 3-way contingency tables |
| Exercise/seminar | Correlation and simple linear regression |
| Coursepack | Section 8.3.5 (Hour 1); Section 8.4 and Chapter 9 (Hour 2) |
| Week 10 | |
| Lecture | Multiple linear regression |
| Exercise/seminar | More on linear regression |
| Coursepack | Sections 8.5–8.7 |
| Week 11 | |
| Lecture | Review and exam preparation |
| Exercise/seminar | Multiple linear regression |
| Coursepack | Chapter 10 |
FAQ: Frequently Asked Questions
Why do we use R/RStudio? I’ve heard that SAS/STATA/MINITAB/SPSS/LIMDEP is better. At this level it does not matter which program you use since we are learning standard procedures that are common to all programs. In favour of R/RStudio is that it is free, flexible and extremely powerful.
Can I get a copy of the R/RStudio software to use on my home computer? Yes, this will be explained in weeks 1 and 2 applied exercises and classes.
I’m taking MY464 because I want to learn how to use R/RStudio but we don’t seem to learn very much about the program. Why is that? MY464 is not a course about learning to use R/RStudio. We use the program merely to facilitate data analysis and interpretation. Some options for learning more about R/RStudio will be mentioned in the first lecture.
I’m taking MY464 to help me analyse data for my dissertation. Can I discuss my data and my specific problems with the lecturers? Yes, but not during the course. Staff of the Department of Methodology will be happy to talk to you about problems specific to your dissertation during the weekly sessions of the Methodology Surgery (see the website of the department for more information).
Does the coursepack contain everything I need to know for the exam? Yes. However, you will stand by far the best chance in the exam if you also attend the lectures, where the lecturers emphasise and explain the key parts of the material.
The lecturer introduced some material that was not in the coursepack. Do I need to know that material? This is almost certainly an illusion. The lectures will not introduce any genuinely new material not included in the course pack. However, sometimes the lecturer may of course use different words or a different example to further explain some topic. Copies of the most relevant notes displayed at the lectures will be posted in the MY464 Moodle site. All of the material required for the exam is contained in the coursepack, with the posted lecture notes as additional clarification.
Can I work together on the applied exercises with my friends? Yes, we positively encourage you to discuss the exercises with your colleagues. If you do this, please make sure you complete the multiple-choice quiz yourself.
I’m not registered at the LSE but at another University of London college. Can I attend this course? Normally yes, but you will have to complete an intercollegiate enrolment form.
I would like to audit the course without taking the exam. Is that OK? Yes, you are welcome to attend the lectures providing you are an LSE/University of London student and there is room for you.
MY464 is not challenging enough for me. Is there a more difficult course? Yes, MY452 and numerous other courses offered by the Department of Methodology and the Statistics department.