Do you need to operationalize your variables?
You need to operationalize your variables by explicitly indicating how you plan to measure your study variables. This is operationalizing is important especially if your variables are not directly measured. Some concepts need to be measured indirectly and therefore you need to clearly indicate how you will measure these variables in the study.
Example,
Let’s say your hypothesis says that people who suffer obesity tend to be more aggressive. Now, you have two variables: obesity and aggression.
How do you measure these two variables?
Obesity can be measured by the total weight of a person. What about aggression? The concept of aggression is not easy to measure so you have to find a way to operationalize this variable. If you look up the literature, you may find an aggression scale. The aggression scale maybe 5 survey questions that ask about a person’s willingness to fight, tease or confront others. These questions would specifically measure your variable and make it operational.
To properly operationalize your variables, you must first conduct a thorough review of existing literature to understand how other researchers have measured similar concepts. This review may reveal established scales that are suitable for your study. In instances where an appropriate scale does not exist, you may need to develop your own. However, it is essential to then assess its validity and reliability through a pilot study.
Pilot Study to assure validity
A pilot study is a crucial step in the research process, serving as a dress rehearsal for your main study.
If you create a new survey that hasn’t been tested, it’s advisable to conduct a pilot study with a small group of participants. This initial trial allows you to conduct a qualitative mini-research, where you can not only gather preliminary responses but also engage with participants to ensure they understood the questions as intended.
During this process, you will likely gain a deeper understanding of your topic and your research instrument. This may lead you to make necessary adjustments, such as rewording ambiguous questions, adding new ones, or removing redundant ones. Ultimately, conducting a pilot study is crucial when a research tool has not been fully validated. This includes newly created surveys, modified versions of existing scales, or tools translated into new languages.
Finally, the pilot study provides a valuable mini-dataset. This initial data enables you to evaluate your planned analytical methods and verify your ability to process and interpret the results from your full-scale research.
Test how you operationalize variables
The pilot study is an important step to make sure you operationalize study variables effectively. For example, if you feel the results of the survey items shows that the person was aggressive, while in fact, he was not. Then maybe the concept of aggression was not properly operationalized.