Saturday, May 06, 2017

Maintaining the Context of Variables

Just as it is necessary for a quantitative report to clearly communicate the measures being analyzed (Huck, 2012) it is equally important that the researcher be cognizant of the connection between the numbers and what they represent. Fixation on the numbers rather than their meaning can result in inaccurate quantitative studies (Nielsen, 2004).
There are several strategies from personal and professional experience that can help keep focus on the meaning of the numbers.  It is always a good idea to perform a sanity check.  For example, if the data being studied is teacher salaries it is unlikely that a value of 10000000.00 is a reasonable value.   This applies to operations upon those values.  For example, if the mean salary was 5000, it is an indication that the numbers might not be representing what is expected.
Another personal strategy is to consider the units of measure.  Units of measure are relevant when operations such as multiplication are performed, along with more complex statistical operations.  During work with an Internet of Things sensor, the units of measure were important when dealing with light, pressure, and temperature.  Units of measure give guidance in how that measure was initially created.   For example, barometric pressure can be measured in pounds per square inch (PSI).  Converting from PSI to some other measure should remove the pounds and the inches from the units.
Lastly, look at all numbers with a degree of skepticism.  There are many instances where numbers are simply incorrect.  We should always question the correctness, accuracy, and validity of the data.  Sensors report incorrect values, people hit the wrong key, disks become corrupted, data files have errors, data transfers can contain noise, and people sometimes make mistakes.  Professional experience has shown that it is always a good idea to question the validity of the values presented.  Because someone ran a statistical operation on a set of data does not mean that data was without flaw.  We should always question the accuracy of what we analyze.




References
Huck, S. W. (2012). Reading Statistics and Research (6th ed.): Pearson.

Nielsen, J. (2004). Risks of Quantitative Studies.   Retrieved from https://www.nngroup.com/articles/risks-of-quantitative-studies/



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