So, you’ve decided on your data collection methods – now you need to plan exactly how
you’ll use them to answer your research question.
Your data collection procedures are the specific steps you’ll take to gather data that’s
consistent, accurate, and unbiased.
To plan your procedures, consider these questions:
How will you define and measure your variables?
How will you ensure your measurements are reliable and valid?
How will you select and contact your sample?
This video will focus mainly on quantitative research, where it’s extra important to
have a systematic plan for collecting your data.
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First, precisely define your variables and decide exactly how you’ll measure them.
Some variables, like height or age, are easily measured.
But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence.
Operationalization means turning these fuzzy ideas into measurable indicators.
If you’re using observations, which events or actions will you count?
For example, to measure student participation in an online course, you could record the
number of times students ask and answer questions.
If you’re using surveys, which questions will you ask and what range of responses will
be offered?
For example, to measure teachers’ satisfaction with online learning tools, you could create
a questionnaire with a 5-point rating scale.
You may also choose to use or adapt existing materials designed to measure the same concept.
For example, to measure math skills, you could administer a math test created by educators.
So, are you measuring any abstract concepts that need to be operationalized?
Then you should also consider the validity and reliability of your measurements.
Reliability means your results can be consistently reproduced, while validity means that you’re
actually measuring the concept you’re interested in.
For valid and reliable results, your measurement materials should be thoroughly researched
and carefully designed.
Plan your procedures to make sure you carry out the same steps in the same way for each
participant.
If you’re developing a new questionnaire or other instrument to measure a specific
concept, you might want to run a pilot study to check its validity and reliability in advance.
If you’re testing a cause-and-effect relationship, you also need to think about internal and
external validity.
Internal validity helps you establish causality, while external validity means you can generalize
your results to other settings.
Both can be impacted by your data collection procedures – check out our article to learn
more.
You’ve already defined your population and chosen an appropriate sampling method.
Now you need a concrete plan for how you’ll recruit your sample.
That means making decisions like: How many participants do you need for an adequate
sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample – by mail, online, by phone, or in person?
If you’re using a probability sampling method, it’s important that everyone who is randomly
selected actually participates in the study.
How will you ensure a high response rate?
If you’re using a non-probability method, how will you avoid bias and ensure a representative
sample?
Now, there’s just one more step to finalize your research design!
In the next video, I’ll help you figure out your data analysis strategy.
See you there!