1.10: Connection Between Understanding How Things Vary and What You are Trying to Do
- Page ID
- 57544
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Why is it important to understand that numbers come from our observation of variations of “it” —whatever “it” is you are trying to do?
Because when we design a quantitative research study, we must be aware of what we are studying, how it varies, and what the variations mean as being high, low, or in the middle. When we are trying to understand a quantitative study or statistics, understanding variation is our framework for understanding how to read statistics and the conclusions drawn from statistical analysis.
Quantifying an Issue
There are many ways to start designing a research study. One possibility is to understand the process of quantifying an issue. Where to start? Start with your words and how you want to describe your experience of observing “it.”
· Pain – rate your headache pain. Use words. Then, describe it on a scale from 1 to 10.
· Depression. Describe the depression. Use words. Then, describe it on a scale from 1 to 10.
The ongoing challenge is to “translate” an issue into something quantifiable that communicates how the issue varies and how to organize (i.e. scale) that variation using numbers. How do you quantify these issues: racism, historical trauma, internalized homophobia, and attachment?
An issue varies by type and level.
Let us start with “type.” Are there different types of this issue?
Next, within each type, there is a level. Someone must experience more frequency or intensity (severity) of the issue than another person. What does that look like?
Third, let us consider ensuring that these levels communicate differences, which means we are comparing something to something else. Someone has more of “it” than another person.
The Axioms
So, we use the following axiom: Numbers = variation -> Establish Comparison of the differences in “it.” This is the schematic process of going from observations to data:
Observation \(\to\) variation \(\to\) number \(\to\) collection of numbers \(\to\) data
Further Reading
To know more about this issue, here is an additional reading: Abelson, R. P. (1995). Statistics as a principled argument. Lawrence Erlbaum Associates, Inc.