Principles and Practices of Quantitative Data Analysis
Qualitative Data Analysis Services
Dr Sarah L. Bulloch
Entry (no or almost no prior knowledge)
Sarah Bulloch, email@example.com
Get to grips with the principles and activities involved in doing quantitative data analysis in this workshop
Who is this course for?
It is designed for postgraduate students and early career researchers planning to work with quantitative data in any discipline.
Course overview and aims
This course provides a high-level overview to the principles and practices of undertaking quantitative data analysis. It is designed for postgraduate students and early career researchers planning to work with quantitative data in any discipline. The course focuses on key aspects to consider before embarking upon a quantitative analysis, as a precursor to learning statistical methods and the use of software. The course combines discussion, demonstration and hands-on exercises.
Please note: the course does not teach participants how to use analysis software such as SPSS, STATA or R.
- Exploring phenomena of interest using quantitative measures
- Types of numerical data: nominal, ordinal, interval and ratio variables
- High quality sources of existing quantitative data: Overview of the UK Data Service and its offer
- Guidelines for gathering and working with one’s own quantitative data
- Variables and their transformation
- The difference between descriptive and inferential statistics
- Types of univariate analyses: descriptive statistics of individual variables
- Bivariate analyses: describing the relationship between two variables
- Evaluating the potential and limits of data & choosing the appropriate type of analysis.
- Reporting results
- Planning for the use of software for the analysis of quantitative data
By the end of the course, participants will be able to:
- Design questions that capture quantitative data in a way that facilitates analysis.
- Approach quantitative data collection confidently.
- Find high quality, topical quantitative data to be re-analysed.
- Understand what an analysis of individual survey questions looks like and what it tells us.
- Understand what it means to look at the relationship between two questions that the different ways of doing that.
- Identify the limits of their data and the analyses it permits.
- Interpret results of descriptive analyses and report these clearly.
- Make choices about which software to use for their analysis.
Format and documentation
This course is delivered in a series of live online sessions during the day that combine discussion, demonstration and hands-on exercises. Participants have the opportunity to discuss their quantitative projects with each other, and with the facilitators. Participants are provided with slide decks, reading lists and resources to further knowledge about the topics covered during the day.
Sarah L Bulloch, PhD is a senior associate of QDA Services and teaching fellow at the CAQDAS Networking Project at the University of Surrey, UK. Sarah has expertise in both quantitative and qualitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her PhD explored the relationship between social trust and gender using large scale population datasets and applying multivariate linear regression, multivariate logistic regression, multi-level modelling and structural equation modelling. Her current work and research centres around enabling others to apply research methods in a meaningful, robust and flexible way.
About QDA Services
QDA Services provide tailored and flexible training, consultancy, coaching and analysis for qualitative, quantitative and mixed-methods researchers. We specialize in facilitating high-quality analysis through the powerful use of digital tools. Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools.
£130 students / £160 general
Website and registration:
Quantitative Data Handling and Data Analysis
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