Syllabus
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
Annotation
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.