# Statistical Research Techniques

Module Code:
15PECC039
Credits:
15
FHEQ Level:
7
Taught in:
Term 2

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:

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.

#### Prerequisites

The preliminary Mathematics and Statistics course is a pre- requisite to the course. Students may take Econometrics 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%.