Students will extend their knowledge of research design, quantitative data analysis and open science practices. The course will cover advanced methods topics such as multivariate simulation, multi-level regression and structural equation modelling. Sessions will highlight the use of real data examples and give opportunities to practice data wrangling, visualisation, descriptive and inferential statistical procedures with a variety of data types and distributions. Students will learn to conduct advanced analyses in R, generate reports using Rmarkdown and use github as a collaborative tool to support reproducible science.

Aims of the module

To introduce students to advanced statistical methods and to the packages/functions in R to perform these analyses. Special attention will be paid to different effect size measures and their interpretation.
	To provide students with advanced analysis skills they can apply to design, execute, analyse and communicate the results of their third-year research projects.
	To encourage students to apply the statistical knowledge and expertise learned in this course to open datasets and/or data generated in other units.
	To teach students data science (organization, wrangling, analysis, visualization) and coding skills valued in academic and non-academic future careers.
	To enable students to develop skills to interact confidently with open data and code
 

Intended learning outcomes of the module

General:

The skills learned in this module build on and expand skills acquired in the introductory (PALS0046) and intermediate (PALS0045) statistical methods modules taught in the 1st and 2nd year. This course provides: 1) advanced research skills that enable students to evaluate and interpret research findings, as well as to apply them in their own research (including 3rd year research projects); 2) data science skills that enable students to interact confidently with open data and code in academic and non-academic settings.

Specific Skills:

Evaluate research reported in research papers.
	Interpret tables and graphs and select the appropriate graphs for displaying different types of data.
	Identify and select the most suitable statistical test taking into account the experimental design, the type of data and the research question(s).
	Report the outcome of statistical tests accurately and clearly.
	Recognize the limitations of statistical tests (e.g. statistical power).
	Write reproducible scripts in R for data simulation, wrangling, visualization, descriptive statistics and inferential tests.
	Use Git to manage and keep track of their code, and the GitHub cloud-based hosting service to manage and share their Git repositories. 
These outcomes will be achieved through:

Teaching of the syllabus outlined below,
	Guidance and formative feedback from tutors on non-assessed exercises in practical computing workshops,
	Computer assisted learning exercises set for study time;