Principles and Practices of Quantitative Data Collection and Analysis

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, although many examples used draw on survey data. The course focuses on key aspects to consider before embarking upon a quantitative analysis, including considerations around data collection, 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.

Topics

• 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

• Descriptive and inferential bivariate analyses: the relationship between two variables

• Evaluating the potential and limits of data & choosing the appropriate type of analysis.

• Introduction to inferential analyses: hypothesis testing and interpreting p-values

• Interpreting a linear regression

• Reporting results

Learning objectives

By the end of the course, participants will be able to:

• Design survey 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 quanitative measures looks like and what it tells us.

• Understand what it means to look at the relationship between two quantitative measures and the different ways of doing that.

• Identify the limits of their data and the analyses it permits.

• Understand the difference between descriptive and inferential statistics

• Interpret p-values, and the results of inferential bivariate analysis

• Interpret the results of a linear regression.

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.

Facilitator

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 specialise 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.