Learning Objectives
By the end of this chapter, you should be able to:
- Understand the elements and ethics of different types of data collection in the social sciences
- Understand data quality issues and overcome collecting bad data
- Know the general rules regarding how much data is required for each paradigm
- Know where to find data for your methods and how to decide what is relevant
- Collect quality data for primary and secondary research, including for systematic reviews and theoretical theses
Suggested Timeline: Complete by Mid-January
Now that your research question is formulated and the method is selected, it is time to collect those elements of the “blooming, buzzing, confusion” of fact (James, 1890, p. 250) called data to make a bloomed, picturesque and lasting flower of an argument. Data collection refers to the processes and procedures used to gather, measure and analyze data. In this chapter we are concerned about data collection because how you collect your data will impact the rest of your thesis. Gathering data ethically and reliably is important if you are to answer your research question effectively. As the saying goes “garbage in, garbage out”, so if there are problems with your data collection, your entire project could be undermined. Bad data are “those acquired through erroneous or sufficiently low-quality collection methods, study designs, or sampling techniques, such that their use to address a particular scientific question is scientifically unjustifiable” (Brown, Kaiser & Allison, 2018, p. 2564). Quality data collection techniques overcome the likelihood and the degree to which bad data gets into your project. Gathering quality data rests on several considerations: how you collect data, how much data is collected and determining what data is the most relevant and reliable for your research purposes. Our discussion on data collection also implicates research paradigm — “the set of common beliefs and agreements shared between scientist about how problems should be understood and addressed” (Kuhn, 1970, p. 43). For example, interpretive research will have methods but no simple routinized ‘procedure’; quantitative research will warrant a strict procedure (a set of rules which determines how you gather and interpret data); and qualitative research can fall in between.
Given the foregoing, this chapter begins with a discussion on bad data and some general strategies for ensuring data quality. Next, we highlight sampling concerns and data quality before discussing qualitative and quantitative methods for sampling primary and secondary data. Following that, we outline some of the most common undergraduate social science data collection techniques and procedures to ensure data quality. This is followed by recommendations on the amount of data required for each method and where data that you might be interested in can be found, taking into account different research paradigms. Finally, we present the data collection of two common interpretive methods given its unique requirement that data collection and analysis proceed simultaneously.
References
Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses: Errors, underlying themes, and potential solutions. Proceedings of the National Academy of Sciences, 115(11), 2563-2570.
James, W. (1890). Principles of Psychology. Henry and Holt Company.
Kuhn, T. S. (1970). The Structure of Scientific Revolutions (2nd Edition) University of Chicago Press.
