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: Mgr. Jiřina Tomečková (21.09.2023)
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.
Course completion requirements
Last update: Mgr. Jiřina Tomečková (21.09.2023)
Grading is based on the Dean's Measure no. 20/2019: https://fsv.cuni.cz/deans-measure-no-20/2019
91% and more => A
81-90% => B
71-80% => C
61-70% => D
51-60% => E
0-50% => F
Literature
Last update: Mgr. Jiřina Tomečková (21.09.2023)
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!
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: Mgr. Jiřina Tomečková (21.09.2023)
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
