# Quantitative Analysis

MSc in Applied GIS, University of Sheffield, Dept. Urban Studies and Planning, 2022

Fall 2022

### Module Description

This module introduces students to powerful and commonly used statistical methods in the social sciences. It assumes no prior statistical knowledge and focuses on the practical research priorities of selecting, conducting and interpreting the most appropriate test with an eye to, rather than an obsession with, the underpinning statistical foundations. The module uses weekly seminar sessions and practical’s to build practical software skills (using open source coding in R) alongside conceptual understanding.

### Module Aims

• A1. Develop understanding of key concepts in statistical data analysis
• A2. Develop understanding of descriptive statistics and exploratory data analysis
• A3. Develop understanding of issues of sampling and inferential data analysis
• A4. Familiarise students with a range of methods for the statistical analysis of bivariate and multivariate problems
• A5. Develop skills in the quantification, assessment and analysis of bivariate and multivariate relationships

### Learning Outcomes

By the end of the module, a student will be able to demonstrate:

• LO1. An understanding of the underlying concepts of statistical analysis (A1/A2)
• LO2. A capacity to undertake and critically interpret sampling and inferential data analysis, including sample accuracy, sample errors and confidence intervals (A3)
• LO3. The ability to conduct and critically interpret a range of bivariate and multivariate relationship estimation methods (A4)
• LO4. The ability to undertake and interpret the results from various forms of statistical analysis within different statistical packages (A5)

### Module Content

 Week 1 Research design and thinking statistically Week 2 Data: Cleaning, wrangling and visualisation Week 3 Descriptive statistics and distributions Week 4 Comparing means: t-tests and ANOVA Week 5 Categorical data analysis: Frequency tables and Chi-Squared Week 7 Correlations Week 8 Linear regression 1: Foundations and principles Week 9 Linear regression 2: Multivariate regressions and model building Week 10 Linear regression 3: Interaction effects and interpretation Week 11 Logistic regressions