This standalone online course is run by the Researcher Academy as part of the Faculty of Engineering Training Programme
Target audience: Postgraduate research students from the Faculty of Engineering. Science Postgraduate research students working collaboratively with Engineering may attend.
This is an introductory course, similar to the general ‘Introduction to Quantitative Research Methods’ but pitched at a higher level. The aim of the course is to understand the statistical concepts covered through the aid of exercises and examples. No particular software package is covered but the methods described can be easily implemented in any stats package or Matlab.
The first part of the course is intended to introduce students to basic statistical concepts, such as random variables, probability and distributions. The second part of the course will involve practical applications of hypothesis testing techniques and interpretation of results from regression analysis.
The course includes the following topics:
1) Random variables and statistical distributions.
2) Discussions of data integrity and factors influencing experiment design.
3) Describing data graphically for a general audience
4) Summarising data numerically. The concepts of bias and efficiency for estimators is introduced.
5) Hypothesis testing of various kinds
6) Estimation of sample size
7) Fitting statistical distributions to data
9) Hypothesis testing for the presence of a trend in data sets
10) Correlation analysis
11) Regression analysis (including multiple regression)
12) Some non-parametric tests
This course is for you if you have had limited exposure to quantitative methods and the associated mathematical analysis.
The aim of the course is to provide a brief introduction to quantitative research methods so that the attendee is able to go away and read more advanced text books and implement the analysis methods on a computer.
By the end of the course you will have a thorough understanding of quantitative methods and how they can be applied to your research. You will be able to
1) Perform hypothesis tests on sample data
2) Summarise data graphically and numerically
3) Understand terms used in more advanced texts
4) Perform further study independently
This is a standalone (self-study) online course available from October 2021. This course is delivered entirely online via Moodle and is self-study. You may access the course any time and as often as you like until the end of the academic year.
Researchers across all 3 campuses can self-enrol on this course in Moodle by following this link:
Introduction to SPSS is an online course designed to teach you the basics of IBM® SPSS® Statistics.
'Virtual Statistical tutor', an online learning tool for those who are familiar with the basics of data collection and analysis and require more information about statistical methods.
Additionally, help is available from the Methods and Data Institute (MDI): PGRs engaged in empirical research for their PhD thesis can call on the MDI for advice on their choice and use of methods and data. A drop-in session especially for PGRs is available every Tuesday from 2-5pm - appointments must be made for this by emailing email@example.com
The following table shows a summary of what is needed to participate in the course.
If you feel you will experience any difficulties participating, please let us know via the ‘special requirements’ tab, providing as much information as possible. The special requirements tab can be completed when you book your place. Alternatively, you can contact us directly at firstname.lastname@example.org.
|Course open throughout the year|
|Engage with pre-recorded course content to attend optional Q&A|
|Access MS Teams|
|Attend the Q&A session at a specified date and time|
|Use MS Teams chat box function|
|Engage with online materials|
|Watch and listen to the course tutor(s) and/or other attendees|
|Attend the live Q&A session (to be marked as attended)|
|Location||Start Date||All Dates||Times||Places Available||Book|