Quantitative Methods - JMMZ034

• Title: Quantitative Methods
• Guaranteed by: Department of Russian and East European Studies (23-KRVS)
• Faculty: Faculty of Social Sciences
• Actual: from 2014 to 2018
• Semester: winter
• E-Credits: 6
• Examination process: winter s.:written
• Hours per week, examination: winter s.:2/2, Ex [HS]
• Capacity: unlimited / unlimited (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Explanation: The course is taught at UCL!!!
• Additional information: http://www.imess.eu/_downloads/_pdfs/Quantitative%20Methods.pdf
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: Allan Sikk, PhD.
• Teacher(s): Allan Sikk, PhD.
• Class: External course, not for registration

Annotation

Last update: VYKOUKAL (25.09.2008)

This graduate course assumes no prior knowledge of statistics or knowledge of mathematics beyond
GCSE (or equivalent)-level. It provides a basic introduction to statistics essential for multi-disciplinary
study. The emphasis is on elements of statistical thinking and insight is drawn from simple data and
concepts rather than complex derivations and formulae. The course presents quantitative methods as
an essential intellectual method appropriate for study alongside other approaches to social sciences.
The course is oriented towards making practical use of simple statistical methods and is focused
particularly on interpretation of the results. The second half of the course, introduces students to
regression analysis and so prepares them for more advanced courses in quantitative methods and
econometrics. By the end of the course students all students will be able to produce and interpret
empirical results using real world data. The course uses the STATA software package.

Aim of the course

Last update: VYKOUKAL (25.09.2008)

Aims:

1. To understand statistical thinking as a fundamental intellectual method.

2. To introduce statistical ideas and statistical reasoning that is relevant to students of social

sciences and humanities.

3. To provide a foundation in basic statistical techniques and principles.

4. To prepare students for the spring term course in Advanced Quantitative Methods.

5. To introduce students to the STATA software package.

Objectives:

By the end of the course, students will:

1. Be aware of different types of data and understand issues relating to methods and errors of

sampling, and other biases in data.

2. Have gained practical skills of presenting and interpreting quantitative data such as descriptive

statistics, measures of central tendency, statistical inference, and measures of association.

3. Have a basic understanding of the principles and limitations of linear regression.

4. Be able to access a greater range of literature utilising quantitative approaches.

5. Be prepared to use Stata for basic data analysis, and for creating tables and graphs.

Literature

Last update: VYKOUKAL (25.09.2008)

Core Reading

Compulsory

? Wright, Daniel B. (2002). First Steps in Statistics. Sage.

Recommended

? Hamilton, Lawrence C. (2006). Statistics with Stata.

? Moore, David S. (2001). Statistics: Concepts and Controversies. W. H. Freeman and Company

? Stark, Philip B. SticiGui: Statistics Tools for Internet and Classroom Instruction with a Graphical

User Interface, Department of Statistics University of California, Berkeley

(http://www.stat.berkeley.edu/users/stark/SticiGui/Text/index.htm).

? Taagepera, Rein (2007). 'Predictive versus postdictive models', European Political Science 6: 114-

23.

Optional

? Agresti, Alan & Finlay, Barbara (1997). Statistical methods for the social sciences. 3rd ed. Upper

Saddle River, N.J. : Prentice Hall.

It is strongly recommended that students read the assigned chapters before attending

the lecture!

Teaching methods

Last update: VYKOUKAL (25.09.2008)

Teaching & Learning Methods (Number of Hours): 200 hours total

Lectures/Classes 10 hours

Lab sessions: 13.5 hours

Private reading, coursework, exam preparation, exam: 176.5 hours

Requirements to the exam

Last update: VYKOUKAL (25.09.2008)

Assessment:

50% two project assignments (one due after reading week, one due start of second term)

50% two-hour written exam in the final week of term

Syllabus

Last update: VYKOUKAL (25.09.2008)

Topics Reading

(Wright)

1 Data sources, collection and visualisation

Data sources, sampling, selection bias.

Qualitative and quantitative data.

Bar charts, line charts and pie charts.

Avoiding the misuse of statistics.

Ch 2, 4

2 Simple descriptive statistics

Contingency tables, Frequency table and histogram.

Central tendency: mean, median, mode.

The spread of data: range, quartiles, variance and standard deviation.

Ch 1-3

3 Distribution and inference

Beyond central tendency and spread: skewness, kurtosis, the normal curve.

Normal distribution. Visualizing distributions.

Ch 5

4 Associating two variables

Ordinal and categorical data: contingency tables, chi-square.

Continuous data: scatterplots, correlation.

Ch 8, 10

5 Statistical significance

Confidence interval of mean.

Statistical significance, hypothesis testing.

Ch 6

6 Comparing two groups

Within group T test

Between groups T-tests

Ch 6

7 Comparing more than two groups

Analysis of variance

Ch 7

8 Linear regression

Linear equation, slope and intercept.

Bivariate regression.

Ch 8

9 Linear regression

OLS and R2.

Data considerations.

Multivariate regression, model specification.

Variants of regression analysis.

10 Written examination

Review session

A 2-hour written examination