A pilot study is to research what a trial balloon is to politics.
In politics, a trial balloon is communicating a law or policy idea via media to see how the intended audience reacts to it. A trial balloon does notanswer the question, “Would this policy (or law) work?” Instead a trial balloon answers questions like “Which people hate the idea of the policy/law–even if it would work?” or “What problems might enacting it create?” In other words, a trial balloon answers questions that a politician wants to know BEFORE implementing a policy so that the policy or law can be tweaked to be successfully put in place.
In research, a pilot study is sort of like a trial balloon. It is “a small-scale test of the methods and procedures” of a planned full-scale study (Porta, Dictionary of Epidemiology, 5th edition, 2008). A pilot study answers questions that we want to know BEFORE doing a larger study, so that we can tweak the study plan and have a successful full-scale research project. A pilot study does NOT answer research questions or hypotheses,such as “Does this intervention work?” Insteada pilot study answers the question “Are these research procedures workable?”
A pilot study asks & answers: “Can I recruit my target population? Can the treatments be delivered per protocol? Are study conditions acceptable to participants?” and so on. A pilot study should have specific measurable benchmarks for feasibility testing. For example if the pilot is finding out whether subjects will adhere to the study, then adherence might be defined as “70 percent of participants in each [group] will attend at least 8 of 12 scheduled group sessions.” Sample size is based on practical criteria such as budget, participant flow, and the number needed to answer feasibility questions (ie. questions about whether the study is workable).
A pilot study does NOT: Test hypotheses (even preliminarily); Use inferential statistics; Assess safety of a treatment; Estimate effect size; Demonstrate safety of an intervention.
I’m not a New Year’s resolution person. I used to be and then I realized that I wanted to hit the restart button more often than every 365 days. So…my aim for this blog remains pretty much unchanged: Make research processes and ideas understandable for every RN.
Although “to be simple is difficult,” that’s my goal. Let me know what’s difficult for you in research, because it probably is for others as well. Let’s work on the difficult together so that you can use the BEST Evidence in your practice.
The 2019 journey begins today, and tomorrow, and the tomorrows after that!
FOR MORE: Go to PubMed. Search for a topic of interest. Send me the article & we’ll critique together.
Key points from our efforts: EBP/research learning should be fun. Content, serious!
The related publication that records some of our fun efforts and the full collaborative picture: Highfield, M.E.F., Collier, A., Collins, M., & Crowley, M. (2016). Partnering to promote evidence-based practice in a community hospital: Implications for nursing professional development specialists, Journal of Nursing Staff Development, 32(3):130-6. doi: 10.1097/NND.0000000000000227.
Yes.It is easier to do things the way we’ve always done them (and been seemingly successful).
Yet, most of us want to work more efficiently or improve our own or patients’ health.
So, there you have the problem: a tension between status quo and change. Perhaps taking the easy status quo is why ‘everyday nurses’ don’t read research.
Ralph (2017) writes encountering 3 common mindsets that keep nurses stuck in the rut of refusing to examine new research:
I’m not a researcher.
I don’t value research.
I don’t have time to read research.
But, he argues, you have a choice: you can go with the status quo or challenge it (Ralph). And (admit it), haven’t we all found that the status quo sometimes doesn’t work well so that we end up
choosing a “work around,” or
ignoring/avoiding the problem or
leaving the problem for someone else or
….[well….,you pick an action.]
How to begin solving the problem of not reading research? Think of a super-interesting topic to you and make a quick trip to PubMed.com. Check out a few relevant abstracts and ask your librarian to get the articles for you. Read them in the nurses’ lounge so others can, too.
Let me know how your challenge to the status quo works out.
Bibliography: Fulltext available for download through https://www.researchgate.net/ of Ralph, N. (2017 April). Editorial: Engaging with research & evidence is a nursing priority so why are ‘everyday’ nurses not reading the literature, ACORN 30(3):3-5. doi: 10.26550/303/3.5
Reliability & validity are terms that refer to the consistency and accuracy of a quantitative measurement questionnaire, technical device, ruler, or any other measuring device. It means that the outcome measure can be trusted and is relatively error free.
Reliability– This means that the instrument measures CONSISTENTLY
Validity – This means that the instrument measures ACCURATELY. In other words it measures what it is supposed to measure and not something else.
For example: If your bathroom scale measures weight, then it is a valid measure of weight (e.g. it doesn’t measure BP or stress). You might say it had high validity. If your bathroom scale measures your weight as the same thing when you step on and off of it several times then it is measuring weight reliably or consistently; and you might say it has high reliability.
Practice based in evidence (EBP) means that you must critique/synthesize evidence and then apply it to particular setting and populations using your best judgement. This means that you must discriminate about when (and when NOT) to apply the research. Be sure to use best professional judgment to particularize your actions to the situation!
Actually when it comes to quantitative data, there are 4 levels, but who’s counting? (Besides Goldilocks.)
Nominal (categorical) data are names or categories: (gender, religious affiliation, days of the week, yes or no, and so on)
Ordinal data are like the pain scale. Each number is higher (or lower) than the next but the distances between numbers are not equal. In others words 4 is not necessarily twice as much as 2; and 5 is not half of 10.
Interval data are like degrees on a thermometer. Equal distance between them, but no actual “0”. 0 degrees is just really, really cold.
Ratio data are those with real 0 and equal intervals (e.g., weight, annual salary, mg.)
(Of course if you want to collect QUALitative word data, that’s closest to categorical/nominal, but you don’t count ANYTHING. More on that another time.)