Introduction to Quantitative Research Methods
Year 1, Term II (0.5 credit units)
Overview
This module introduces students to quantitative research methods in the
social sciences by covering the basics of what is required for those
considering a career involving data analytics. It assumes no knowledge of
quantitative methods or statistical software. The course caters for students
from diverse academic disciplines and adopts a practical hands-on approach
to learning, with tutor supported computer tutorials. The course covers
descriptive statistics, data visualisation, data access, probability, sampling,
hypothesis testing, inferential statistics and ends with an introduction linear
regression. Students will be introduced to the R statistical software and work
with data used in current academic research.
Topics
1. Understanding data
• How variables are measured
• Why use R?
2. Examining & visualizing data I
• Averages and measures of dispersion
• Basic tables and graphs in R
3. Examining & visualizing data II
• Comparing variables using tables and graphs
• Data management in R
4. Introduction to geographic data
• What is spatial data?
• Creating maps in R
5. Probability
• Populations and samples
• Basics of probability theory
• The Normal distribution
6. Confidence intervals
• Sampling variation
• Hypothesis testing
• Statistical significance
7. Statistical measures of difference
• Chi-square for categorical data
• T-tests for numerical data
8. Correlation
• Scatterplots
• Measuring relationships between numerical variables
9. Simple linear regression
• Fitting a line of best fit
• Regression model interpretation
• Assumptions of linear regression
10. Multiple regression
• Comparing regression models
• Adding categorical variables