- Module: Research Skills
- Skill: Collecting, Analyzing, and Interpreting Quantitative Data
- Publisher: SAGE Publications, Inc.
- Publication year: 2022
- Online pub date:
- Discipline: Quantitative Data Collection
- Keywords: dependent variables, distribution, inferential statistics, measures of central tendency, p value, parametric statistics, publications, quantitative data collection, research design, research skills
Online ISBN: 9781071881514Copyright: © SAGE Publications, Inc. 2022
The Designing Your Research Project Skill presented a variety of different designs: some that generate data in word form (qualitative data), others that generate data in numeric form (quantitative data). This Skill focuses on the latter, exploring how best to collect, analyse, interpret, and present quantitative data. The aim of this Skill is to give you a core understanding of fundamental principles of collecting, analysing, and interpreting quantitative data, so that you can ask key questions, know when you need to seek additional advice, and then make good decisions. Overall, you should gain an increased confidence in understanding a wide range of issues surrounding quantitative data.
There are some myths about quantitative data that would be best to clear up right away. The first myth is that the term ‘quantitative’ denotes a research design; in actuality, there is no such thing as ‘quantitative research’. As discussed in the Designing Your Research Project Skill, there are a wide range of designs that you can use to collect quantitative data. Often, the use of quantitative data is associated with a positivist or post-positivist paradigm, although this is not always the case.
Another myth to bust is the primacy of numerical data. However, just because something is numeric does not make it any more or any less important in a research sense than data collected in word form. Different types of data answer different types of research questions, and to understand most phenomena more fully, it is helpful to have as broad an understanding of the different types of research designs used to explore them and to learn to interpret the full range of types of data collected from these.
Importantly, interpretation of quantitative data is always linked to the design. How the data were collected and how they were analysed is critical to one’s ability to generalise the results to then make any predictions based on the data. Unless you understand the research design, data collection, and analysis, the numbers alone are meaningless. Therefore, this Skill focuses on how decisions about design, data collection, and analysis influence understanding and interpretation of quantitative data so that you are able to make the best choices in your own research as well as to better interpret studies of others involving the use of quantitative data.
There’s a glossary of terms and concepts available for this entire Module in the Beginning Your Research Skill.