Quant: Getting Lost in the Numbers

It is easy to get lost in numbers when you do quantitative research.
These are suggestions that can help keep the focus on people and organizations when you are dealing with numbers representing them.


In quantitative research, data that is collected is numerical in nature. Rarely is every member of the population studied, and instead a sample from that population is randomly taken to represent that population for analysis in quantitative research (Gall, Gall, & Borg 2006). At the end of the day, the insights gained from this type of research should be impersonal, objective, and generalizable.  To generalize the results of the research the insights gained from a sample of data needs to use the correct mathematical procedures for using probabilities and information, statistical inference (Gall et al., 2006).  Gall et al. (2006), stated that statistical inference is what dictates the order of procedures, for instance, a hypothesis and a null hypothesis must be defined before a statistical significance level, which also has to be defined before calculating a z or t statistic value.

Essentially, a statistical inference allows for quantitative researchers to make inferences about a population.  A population, where researchers must remember where that data was generated and collected from during quantitative research process.  However, it is easy to get lost in the numbers during quantitative research, thus here is a list of some of the ways to keep the focus on the people and organizations when research deal with the numbers that represent their population: To design a quantitative research project, researchers must understand the purpose and rationale of their own research designs and their research methods (Creswell, 2014).  Knowing the purpose and rationale can help the development of a research question(s) and hypothesis.  With a clear research question and hypothesis can a researcher to design and review their data collection from people, organizations, or instruments.  It is when focusing on the methods section that researchers can keep their focus on the people and organizations by identifying the population, consideration of a stratified population before sampling, sampling design and procedures, selection process for the individuals, which variables to study (their name, how they relate to the research question, and collection description) (Creswell, 2014).

  • The numerical data used in the quantitative research was generated and collected from people, a social group, an organizational entity, or an instrument. The numerical value alone does not have any meaning nor value to the research. But, when the numerical value is paired with contextual information, then it provides researchers a wealth of information to conduct their statistical analysis on the data (Ahlemeyer-Stubbe, & Coleman, 2014; Miller, n.d.a.).
  • Remember each data point, row or column represents a person, group, or thing with all its features and bugs. It would be wise to create a metadata file that describes the data points variables to help keep the focus on the people and organizations.  In SPSS, the metadata section is called the “Variable View”, and each person is represented as an entity or row of data in the “Data View” (Field, 2013; Miller, n.d.b.).
  • Data sets are never neutral and theory-free data repositories but require researchers to interpret that data through their personal lenses (Crawford, Miltner, & Gray, 2014). One must gather and analyze data ethically to avoid social and legal concerns. Thus, the researcher must be aware of how their analysis of the data can be used to cause harm to others or help facilitate discriminate against disenfranchised groups of people (Robinson, 2015).


  • Ahlemeyer-Stubbe, A., & Coleman S. (2014). A practical guide to data mining for business and industry. UK, Wiley-Blackwell. VitalBook file.
  • Crawford, K., Miltner, K., & Gray, M. L. (2014). Critiquing Big Data : Politics , Ethics , Epistemology Special Section Introduction. International Journal of Communication, 8, 1663–1672.
  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics (4th ed.). UK: Sage Publications Ltd. VitalBook file.
  • Gall, M. D., Gall, J., & Borg W. (2006). Educational research: An introduction (8th ed.). Pearson Learning Solutions. VitalBook file.
  • Miller, R. (n.d.a.). Week 1: Central tendency [Video file]. Retrieved from http://breeze.careeredonline.com/p9fynztexn6/?launcher=false&fcsContent=true&pbMode=normal
  • Miller, R. (n.d.b.). Week 2: All about SPSS. [Video file]. Retrieved from http://breeze.careeredonline.com/p99kywtldbw/?launcher=false&fcsContent=true&pbMode=normal
  • Robinson, S. C. (2015). The good, the bad, and the ugly: Applying Rawlsian ethics in data mining marketing. Journal of Mass Media Ethics, 30(1), 19–30. http://doi.org/10.1080/08900523.2014.985297

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