This episode is a live recording of the sixth session of #UnblindingResearch held in DREEAM 15th August 2018. The group work has been removed for the sake of brevity.
This session of #UnblindingResearch looks at the fundamental distinction in research between two types of data: qualitative and quantitative. Here are the slides for this session (p cubed of course), you can move between slides by clicking on the side of the picture or using the arrow keys.
Here's our Take Visually for this session:
This session started by discussing the main distinctions between the two once again with a sweet analogy. We also had a think about how the two different types of research collect, analyse and report the data.
Then, through some group work and more discussion this session looked at the strengths, weaknesses, trial design and the role of the investigator when it comes to qualitative vs quantitative.
Qualitative data is any data not in the form of numbers so will include words, images or objects. Qualitative research looks to interpret events in their natural settings and to make sense or interpret phenomena in terms of the meanings people bring to them. In terms of social science there has been a big debate (positivism vs anti-positivism) about the correct approach to take with social phenomena. The groups will be smaller and not randomly selected. Variables are not studied because the study is interested in the whole experience.
The study will be through interviews or focus groups, open ended responses or observations and/or reflections. The researcher will then assess the data or patterns, themes or features. The focus is wide and examines the breadth and depth of the topic in question. Findings are more generalised and due to the nature of the data reliability and validity are difficult to measure. The studies are often time heavy and it may require sub-specialism to correctly analyse the data. The researcher is often closely involved with the subjects and their environment and so can appreciate a fuller view of the issues involved. Qualitative data can suggest possible relationships or cause/effects and reveal subtleties hidden from quantitative research.
Quantitative data is numerical, in units of measurement or in categories or in sequence.
Quantitative research looks to test hypotheses, causality and make predictions. Samples will be bigger and randomly allocated with specific variables studied. Numbers and statistics will be collected through measuring with structured and validated instruments. The goal is to identify statistical relationships in a narrow, specific topic. That means the findings are more projectable across the population base.
Smaller quantitative studies are more likely to be less reliable and so large samples are needed which may be difficult to achieve. The research is remote from the setting and may not have sufficient background to analyse the results or place them in a social context. However, modern software does mean analysis can be performed increasingly easily and the nature of the data and analysis makes it easier for others to appraise your work.