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Content: In this module, students will build on their knowledge of research design and statistical analysis that they developed during their first year Introduction to Research Methods module. More advanced statistical methods such as logistic regression, multiple regression, ANOVAs, mixed effects models, factor analysis, and power analysis will be introduced. Students will learn how to conduct these analyses in R, interpret the results, and produce reproducible reports using Rmarkdown.

The module consists of fourteen one-hour weekly lectures, followed by 1.5h practicals using RStudio. The lectures will introduce different analysis methods, focusing on understanding how each one is applied to answer different questions and fostering understand of the conclusions that could be drawn from results. The practicals will give students experience of running each analysis using real datasets and writing scripts to produce reproducible analysis pipelines that include data wrangling, data visualization, descriptive statistics, inferential statistics, and communication of results. Students will complete formative assessments between each class to consolidate their learning.

Teaching Delivery: This module is taught in 14 weekly lectures and 14 weekly PGTA-led practicals.

Indicative Topics: Indicative lecture topics – based on module content in 2023/24 and subject to possible changes:

• ANOVAs
• ANCOVA
• Multiple regression
• Model fitting and assumption checks
• Linear mixed models
• Conditional probability and odds ratios
• Logistic and Poisson regression
• Statistical power and significance testing
• Screening and test sensitivity
• Reliability and measurement error
• Factor analysis

Module Aims: The module aims:

1. To teach the principles of research across a range of methodologies, so that students can understand the literature related to their other courses.
2. To introduce students to commonly used inferential statistical tests and to the packages/functions in R to perform these tests. Special attention will be paid to different effect size measures and their interpretation.
3. To enable students to understand and evaluate the research methodology and statistical procedures used by researchers when reading research papers in their studies.
4. To provide students with the necessary skills needed to design, execute, analyse and communicate the results of their research projects in the third year.
5. To encourage students to apply the statistical knowledge and expertise learned in this course to data generated in other units.
6. To teach students fundamental research methodology and coding skills so that they can consider undertaking relevant research in their future careers.