Core data analysis

This module will provide students with core data analysis skills across platforms. They will develop quantitative skills in Excel, Jamovi and R that will serve as a solid foundation for more advanced statistical analysis training in Term 2. The module aims to provide intensive core training to build fundamental skills in analysis, in students from diverse training backgrounds. They will also become familiar with the underlying principles of the qualitative method and learn some key methods for data collection and analysis. Building knowledge across the qualitative and quantitative approaches will increase student's competence and confidence in applying a mixed methods approach to research questions.</p>

The module supports the development of Abertay Attributes, with professional skills and intellectual understanding supported by practical and assessment activities. Personal skills will be fostered by working with peers on practical assignments, problem solving and reflecting on skill development. Active citizenship will be developed by exploring data uses and open science practices. There will be a focus on digital skills throughout, with practical and assessment activities developing students' understanding of a range of digital data processing and analysis tools, including those that are open source. These digital skills are important for enhancing employability, as are the transferable skills such as communication, problem-solving, perseverance and teamwork fostered in practical sessions.</p>

By the end of this module the student should be able to:

1. Process and visualise data in Excel, produce calculations and apply function commands, and create and interpret pivot tables
2. Apply specialist skills in Jamovi, preparing data and running descriptive analyses and inferential tests with autonomy
3. Apply detailed knowledge and skills to prepare data for R and independently construct R scripts to produce graphs and run descriptive analyses and basic inferential tests
4. Apply an understanding of qualitative analysis and mixed methods approaches
5. Communicate professional level of core knowledge in quantitative and qualitative data analysis
6. Evaluate analytical approaches and techniques

1 Data formatting

Introduction to data processing platforms (Excel, Jamovi, R) and associated data formatting in wide and long formats

2 Excel skills

Data visualisation, using functions and creating pivot tables

3 Basic statistics in Jamovi

Data visualisation, descriptive statistics, ANOVA, correlation, and regression.

4 Familiarisation with the R environment

Basic programming in R to perform calculations, and to manipulate and visualise data within RStudio using R Markdown.

5 Statistical analysis in R

Learn basic coding required to run descriptive and inferential statistics, including t-tests and ANOVAs.

6 Qualitative method

Introduction to the principles underpinning qualitative research methods, and the formation of research questions.

7 Qualitative Skills

Learn about data collection methods, such as focus groups and interviews, and data analysis methods and skills.