# Statistical Research Techniques in International Development

Module Code:
15PECC052
Unit value:
0.33
Year of study:
Year 1
Taught in:
Term 2

Statistical Research Techniques is a module designed specifically to address the distinctive needs of research in international 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 in International Development are threefold:

1. to introduce students to statistical inference and a range of statistical tests;
2. to encourage clear and coherent expression of statistical results;
3. 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.

This course will delivered alongside the parallel course Statistical Research Techniques, 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 module

On successful completion of the course, students will 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.
• 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.

#### Method of assessment

Assessment weighting: Exam 100%.