Quantitative Methods

Sylabus

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

Anotace

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.