Statistical Research Techniques
- Module Code:
- Unit value:
- Taught in:
- Term 2
(This module is only available to students taking the MSc Political Economy of Development)
Statistical Research Techniques is a module designed specifically to address the distinctive needs of political economy of development. It not only covers standard topics in econometrics and quantitative research skills, but also gives practical training in using survey data, understanding sampling techniques and qualitative statistical methods. This module forms part of requirements of doctoral training in Economics and International Development. The aims of Statistical Research Techniques are threefold:
- to introduce students to statistical inference and a range of statistical tests;
- to encourage clear and coherent expression of statistical results;
- and to promote critical reading of statistics in economics and political economy.
The topics covered will include sampling methods, survey design, comparison of means, analysis of contingency tables, correlation and regression. The software package Stata will be used throughout the course. Open access computers at SOAS are available for students to complete the course work.
The preliminary Mathematics and Statistics course is a pre- requisite to the course. Students may take Quantitative Methods I as an alternative to Statistical Research Techniques, if it is more suitable for their research interests, subject to academic approval.
Objectives and learning outcomes of the module
The learning outcomes are as follows:
- Understand the characteristics of probabilistic and non-probabilistic sampling methods.
- Define and describe the properties of simple random sampling, stratified random sampling and multi-stage sampling.
- Identify the main sources of non-observational (sampling, non-response, frame selection) error in relation to sample surveys.
- Identify the main sources of observational error (questionnaire design, interviewer bias, interviewee bias) in relation to sample surveys.
- Describe a variable, both numerically and graphically, with reference to its level of measurement.
- Distinguish between marginal, joint and conditional probabilities in relation to a contingency table.
- Use a range of measures of association related to a contingency table including conditional distributions, odds ratios, standardised residuals, Pearson’s Chi-Squared test and Cramer’s V.
- Create confidence intervals and hypothesis tests for a number of sample statistics.
- Test for the equality of two sample means.
- Test for the equality of three or more means using one-way ANOVA.
- Test for the equality of variance between two sample variances.
- Be able to interpret a range of correlation coefficients for nominal, ordinal and interval data.
- Understand the main assumptions of the classical linear regression model.
- Generate and interpret the results of simple bivariate and multivariate regression.
- Interpret the standard inferential statistics for simple bivariate and multivariate regression.
- Understand the purpose and consequence of different functional forms in estimating regression equations.
- Use dummy variables as independent variables in a regression.
Method of assessment
Assessment weighting: Exam 100%.
- Agresti, Alan and Finlay, Barbara (2009) Statistical Methods for the Social Sciences, 4th Ed. Prentice-Hall Int. ISBN-13: 9780130272959.
- Gujarati, Damodar N. and D. C. Porter (2009) Basic Econometrics, 5th Ed. McGraw-Hill. ISBN 978-007-127625-2.
- Howell, David C. (2012) Statistical Methods for Psychology, 8th Ed. Duxbury/Thomson Learning. ISBN-13: 978-1111835484.
- Mukherjee, C, White, H., Wuyts, M (1998) Econometrics and Data Analysis for Developing Countries, Routledge. ISBN: 0-415-09400-3
- Thomas, R.L. (2005) Using Statistics in Economics, McGraw-Hill. SOAS Library: A519.5/936748
- Wooldridge, J. M. (2013) Introductory Econometrics: A Modern Approach, 5th Ed. Cengage Learning. ISBN-13: 9781111531041.