index.Rmd

---
title: "MY451 Introduction to Quantitative Analysis"
author:
    - "Jouni Kuha, Department of Methodology, London School of Economics and Political Science"
site: bookdown::bookdown_site
documentclass: book
link-citations: yes
github-repo: LSE-Methodology/MY451
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 MY451 include descriptive statistics, basic ideas of inference and estimation, contingency tables and an introduction to linear regression models."
---

# Course information {-}

![image](dept_of_methodology){height="2.2cm"}\hfill![image](lse){height="2.2cm"}

#### 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 MY451 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.

\newpage

#### 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.

#### Staff {-}

-   **Teachers responsible**:Jonathan Jackson (MT) & Tom Robinson (LT)

-   **Course Administrator**: Lucia Pedrioli, email `methodology.admin@lse.ac.uk`

-   **Class Teachers**: MT: Aqeel Awan, Michaela Sedovicova, Nora Yitong Qiu,
Pinar Jenkins, Sazid Ahmad, Tamara Shengelia, Vikas Chandra & Zuzana Dančíková

\newpage

#### Teaching {-}

-   **Lectures**: Ten two-hour lectures in weeks 1 - 5 and 7 - 11:

    -   Michaelmas Term (MY451M): Thu 2.00pm - 4.00pm in Clement House CLM.5.02

    -   Lent Term (MY451L): TBC

-   **Classes**: Each student will attend a one-hour class each
    week, **starting in Week 2**. Please see on-line timetables for the
    times and places of the class groups. The allocation of students to
    classes is done through LSE for You or the Moodle system (see
    below), depending on your status. This will be exaplained in the
    first lecture and on the MY451 Moodle page. Please do not change the
    classes allocated to you without our consent.

#### Course Materials {-}

-   **Coursepack**: This coursepack is the main course text.
    Digital copies of this coursepack (i.e., what you're looking at now) are available for download
    for free from the Moodle page.

-   **Lecture slides**: Copies of most of the slides displayed during
    the lectures can be downloaded from the MY451 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 editions are also suitable. While neither of these books is
    absolutely required, we recommend that you 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. If in doubt, please ask one of the lecturers.

    -   There are also many books which focus on the SPSS statistical
        software package used in the computer classes. We do not
        consider them necessary for this course, or for learning
        statistics or SPSS. If, however, you feel otherwise, some such
        books are mentioned in the Appendix.

#### MY451 on Moodle {-}
<!-- {#sss-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 MY451, and in *search results* click on
MY451. The site contains the structure of the course week by week, the
readings, weekly computer class assignments and the associated data
sets, coursepack and other materials, as well as a section on news and
announcements.

#### Advisory Sessions {-}

There will be times each week during the Michaelmas and Lent terms
during which a member of the teaching team will be available at the
Department of Methodology in Columbia House to answer questions about
the course. Information on the times of the advisory hours will be given
at the first lecture. These sessions are not intended to be private
tutorials and you will be expected to have made a genuine attempt to
work through the relevant sections of the coursepack prior to coming to
an advisory session. Also, questions addressing material from that
week’s lecture will be given priority. If you are having difficulty with
a topic, come and see us at that time. If you wait until the end of
term, when the advisory sessions can be very busy, we cannot guarantee
that your questions from earlier sessions will be covered. **There will
be no advisory sessions after the end of Lent Term.**

#### 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 (or sleep through it!)
there is a chance that you will find yourself hopelessly lost. So this
is definitely not a “pick and choose” course! Also bear in mind that
most people cannot learn quantitative techniques passively by just
turning up to 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 two-hour weekly lecture there will be one-hour
computer classes (in which you do some data analysis and interpretation
using SPSS - instructions will be provided) and there will be weekly
homework (which will be corrected but not graded by your class teacher).
Doing the assignments in the computer classes and the homework
demonstrate whether 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 get to grips with this material.

#### Examinations/assessment {-}

There will be a **two-hour examination in the Summer Term**.
You may bring one, two-sided A4 sheet of reference material to consult
during the exam. This can be typed or handwritten, and prepared yourself
or in collaboration with others.You are required to bring your own
calculators for the examination. Examination papers from previous years
are available for revision purposes at the LSE library web site. 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.

For many of you, MY451 is only one part of a package of methods training
with a course code such as MC4M1 or MY4M1. If this is the case, your
result for MY451 will contribute only part of the final mark for the
package, with a weight determined by the regulations.

#### Computing {-}

Students must know their Username and Password in time for the first
class. This information can be obtained from IT Help Desk (Library, 1st
floor). The course uses Microsoft Windows-based software. If you are not
familiar with the program, you might want to attend introductory courses
in Windows during the first two weeks of the term. The statistical
package being used is SPSS, which will be introduced in the first
computing class of the course.

#### Software availability {-}

We use RStudio, which is free.

#### Feedback {-}

We would 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 for next year.

#### 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, and Sally Stares. 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.

\newpage

#### Course Programme {-}

------------------------------- ----------------------------------------------------------------------------------------------------------------------------------
**Week 1**                      \

Lecture                         Course overview and organisation. Introduction to basic concepts

Exercise                        Familiarisation with R/RStudio (no seminar week 1)

Coursepack                      Chapter \@ref(c-intro)

**Week 2**

Lecture                         Descriptive statistics for categorical variables

Exercise/seminar                Loading data into R/RStudio, descriptive statistics

Coursepack                      Sections \@ref(s-descr1-intro)–\@ref(s-descr1-2cat) and \@ref(s-descr1-presentation)

**Week 3**

Lecture                         Descriptive statistics for continuous variables

Exercise/seminar                 Descriptive statistics for categorical variables

Coursepack                      Sections \@ref(s-descr1-1cont)–\@ref(s-descr1-2cont)

**Week 4**

Lecture                         Analysis of two-way contingency tables

Exercise/seminar                Descriptive statistics for continuous variables

Coursepack                      Chapters \@ref(c-samples) and \@ref(c-tables)

**Week 5**

Lecture                         Inference for means in two populations

Exercise/seminar                Analysis of two-way contingency tables

Coursepack                      Chapters \@ref(c-contd) and \@ref(c-means)

**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 \@ref(c-probs)

**Week 8**

Lecture                         Correlation and simple linear regression as descriptive methods

Exercise/seminar                Inference for proportions in one and two populations

Coursepack                      Sections \@ref(s-regression-intro)–\@ref(ss-regression-simple-est)

**Week 9**

Lecture                          Inference for the simple linear regression model, 3-way contingency tables

Exercise/seminar                 Correlation and simple linear regression

Coursepack                      Section \@ref(ss-regression-simple-inf) (Hour 1); Section \@ref(s-regression-causality) and Chapter \@ref(c-3waytables) (Hour 2)

**Week 10**

Lecture                         Multiple linear regression

Exercise/seminar                More on linear regression

Coursepack                      Sections \@ref(s-regression-multiple)–\@ref(s-regression-rest)

**Week 11**

Lecture                         Review and exam preparation

Exercise/seminar                Multiple linear regression

Coursepack                      Chapter \@ref(c-more)
------------------------------- ----------------------------------------------------------------------------------------------------------------------------------

\newpage

#### 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 MY451 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?** MY451 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 MY451 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 MY451 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.

**MY451 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.