Statistical Research Techniques in International Development
- Course Code:
- Unit value:
- Year of study:
- Year 1
- Taught in:
- Term 2
This course is only available to students enrolled on the MSc Research for International Development programme.
The aims of Research Methods I are threefold: to introduce students to basic inference and a range of statistical test; to encourage the clear and coherent expression of statistical results; and to promote the critical reading of statistics within the economics literature.
The topics covered will include sampling methods, the comparison of means, the analysis of contingency tables, correlation and basic regression. The software package Stata 9.0 will be used throughout the course. Open access computers at SOAS are available for students to complete the course work.
On successful completion of the course, students should be able to:
• 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.
• Use a range of standard probability distributions including the Normal distribution, Student's t distribution, Fisher's F distribution and the Chi-Squared distribution.
• 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 in regression analysis.
This course will delivered alongside the parallel course Research Methods I, worth 18 CATS credits. Students will have the opportunity to attend all lectures and tutorial, but the examinable component will be approximately 85% of the 18 CATS credits syllabus. The following topics will not be part of the examinable component of this course: Week 10 Dummy variables.
Objectives and learning outcomes of the course
On successful completion of the course, students will be able to:
- The ability to generate and interpret descriptive statistics for both categorical and continuous data.
- An understanding of the underlying principles and an ability to use confidence intervals and hypothesis tests.
- The ability to generate and interpret measures of association for categorical data using contingency tables, conditional distributions, tests of independence, residuals and odds ratios.
- An understanding of the mechanics of coding a questionnaire
- An understanding of the main principles of survey design, including sources of sampling and non-sampling error.
- The ability to compare means, including one-sample t-tests, paired sample t-tests, independent sample t-tests, and one-way ANOVA.
- A basic understanding of estimation, inference and validation of the Classical Linear Regression model.
Method of assessment
Assessment weighting: Exam 100%.
- Agresti, Alan and Finlay, Barbara (1997) Statistical Methods for the Social Sciences, 3rd Ed. Prentice-Hall Int. ISBN0-13-622515-2.
- Gujarati, Damodar N. and D. C. Porter (2009) Basic Econometrics, 5th Ed. McGraw-Hill. ISBN 978-007-127625-2.
- Howell, David C. (2002) Statistical Methods for Psychology, 5th Ed. Duxbury/Thomson Learning. ISBN 0-534-37770-X.
- 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. (2003) Introductory Econometrics: A Modern Approach, Mason, Thomson. ISBN 0-324-11364-1.