¾ÅÉ«ÊÓƵ

Learning Outcomes (LOs) 

On successful completion of this module, the learner will be able to: 

1. Use statistics to reduce complex data situations to manageable formats to describe, explain and model them, 

2. Derive descriptive statistics for various data types, 

3. Manipulate data in RStudio, 

4. Perform and critique statistical tests (parametric and non-parametric) on two and more sample data, 

5. Communicate effectively research findings in a clear concise manner using correct terminology based on output from RStudio. 

Pre-requisite learning 

The list below is a prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in achieving the learning outcomes of the module. The recommended learning is understanding: 

• Qualitative data v. Quantitative data, 

• Properties of a boxplot, 

• Steps to hypothesis testing, 

• Parametric tests v. non-parametric tests, 

• Meaning of level of significance, 

• Type I error v. Type II error,  

• Interpretation of a p-value, 

• Importance of effect size statistics. 

Introduction to Data Analysis in RStudio 

This part of the training provides an introduction to RStudio. The course addresses LO1, LO2, and LO5 by covering: 

• A review of statistical terminology, 

• Demonstration on how to organise data, create a dataframe, import data, export results, etc., 

• Conclude with compiling descriptive statistics (both numerical and graphical) for various data types. 

Data Manipulation with RStudio 

An important skill to using RStudio is being comfortable with manipulating your data. Often when it comes to statistical tests data needs to be in a certain format. 

This part of the training will focus on common data manipulation techniques encountered in RStudio

i.e. Indexing; Sorting; Ordering a dataframe; Renaming variables; Data types; Grouping a variable; Merging Rows/Columns; Handling missing values; Locating outliers; Filtering. 

This part of the training addresses LO1 and LO3. 

Statistical Inference with RStudio 

The final segment to this part of the training assumes that the delegate will have an understanding of hypothesis testing. The focus of this part of the training is to demonstrate how to perform a proportion of inferential statistics in RStudio, with a focus on how to line up and analyse various data sets properly in both a parametric and non-parametric way. The course focusses on LO3, LO4 and LO5 by covering: 

• Worked examples of testing for normality/differences between two measurements, 

• In the case of non-normal data, the workshop will discuss suitable transformations, 

• One-way Analysis of Variance (ANOVA) with suitable posthoc testing, 

• Within-Subjects Designs (Repeated Measures), 

• Between- and Within-Subjects Designs (Mixed Factorial Experiments), 

• Between-Subjects Designs (Two-way ANOVA). 

Communication during asynchronous training 

• Questions relating to aspects of the training content will be answered through a discussion document on Google Drive. 

• Discussions will create a platform for a shared learning environment. 

• Anne O’Dwyer (anne.odwyer@ul.ie) is the first point of contact, if a question is not for the open discussion document. 

Schedule 

Week 01  

Introduction to the R environment 

Tuesday, 04 November, 09:30 – 11:30 (Live/Zoom). 

Introduction to Data Analysis and Data Manipulation in RStudio (Part I) 

Thursday, 06 November, (Pre-recorded videos on Google Drive). 

Week 02  

Introduction to Data Analysis and Data Manipulation in RStudio (Part II) 

Monday, 10 November, (Pre-recorded videos on Google Drive). 

Statistical Inference with RStudio (Part I) 

Wednesday, 12 November, Part I (Pre-recorded video). 

Q&A session 

Thursday, 13 November, 14:00 – 15:00 (Live/Zoom). 

Week 03  

Monday, 17 November, Part II (Pre-recorded videos on Google Drive), 

Wednesday, 19 November, Part III (Pre-recorded videos on Google Drive). 

Week 04