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This paper demonstrated times when researchers must make bold claims when operationally defining qualitative variables. For example, they had to make decisions as to how one would asses general happiness pertaining to employment while avoiding confounds. They realized that aging can result in changes to one’s psychological well-being in some but not in others (thus changes across age could be due to factors other than employment), but also had to acknowledge that mental health varies greatly at any given age. When assessing well-being, they used statements pertaining to the person’s “usual” state. In some studies, this could be problematic- given that everyone’s “usual” varies greatly. However, this methodology worked for their experiment because it assessed deviations from their normal state in response to stressors pertaining to employment.

In a more general sense, it was interesting to see the level of detail researchers must consider when attempting to analyze qualitative data (versus it’s more straightforward quantitative counterpart). In a classmate’s post, they referenced the danger of omitted variables being used in order to support a preferred outcome. This reading seems to tackle the other end of the spectrum, in which data must be assessed in a way that best fits the study, but does not manipulate the outcome in a way that would ensure favorable support of the hypotheses.