Research is using the scientific process to ask and answer questions by examining new or existing data for patterns. The data are measurements of variables of interest. The simplest definition of a variable is that it is something that varies, such as height, income, or country of origin. For example, a researcher might be interested in collecting data on triceps skin fold thickness to assess the nutritional status of preschool children. Skin fold thickness will vary.
Research is often categorized in different ways in terms of: data, design, broad aims, and logic.
Experimental research tries to answer “Why” question by examining cause and effect connections. e.g., gum chewing after surgery speeds return of bowel function. Gum chewing is a potential cause or “the why”
Aims. Studies, too, may be either applied research or basic research. Applied research is when the overall purpose of the research is to uncover knowledge that may be immediately used in practice (e.g., whether a scheduled postpartum quiet time facilitates breastfeeding). In contrast, basic research is when the new knowledge has no immediate application (e.g., identifying receptors on a cell wall).
Logic. Study logic may be inductive or deductive. Inductive reasoning is used in qualitative research; it starts with specific bits of information and moves toward generalizations [e.g., This patient’s pain is reduced after listening to music (specific); that means that music listening reduces all patients pain (general)]. Deductive reasoning is typical of quantitative research; it starts with generalizations and moves toward specifics [e.g., If listening to music relaxes people (general), then it may reduce post-operative pain (specific)]. Of course the logical conclusions in each case should be tested with research!
WHAT RESEARCH IS NOT:
Research as a scientific process is not going to the library or searching online to find information. It is also different from processes of applying research and non-research evidence to practice (called Evidence-Based Practice or EBP). And it is not the same as Quality Improvement (QI). See Two Roads Diverged for a flowchart to help differentiate research, QI and EBP.
Below is my adaptation of one of the clearest representations that I have ever seen of when the roads diverge into quality improvement, evidence-based practice, & research. Well done, Dr. E.Schenk PhD MHI, RN-BC!
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.
Musings: For me the most difficult to write sections of a research report are the Intro/Background and Discussion. And yet, those are apparently the easiest to read for many. My students at least tend to read only those sections and skip the rest.
Why? For the author, Intro/Background and Discussion require hard, critical thinking about what is already known about the topic (Intro/Background) and then what one’s findings mean in light of that (Discussion). For research consumers, the language used in these sections is more familiar, ordinary sounding words. On the other hand, writing the technical nature of other sections (Methods, Instruments, Results) is pretty straightforward with scientifically standardized vocabulary and structure. But, for readers, those same sections contain potentially unfamiliar research terminology that is not part of everyday conversation– i.e., scientific vocabulary. Quantitative studies often create more reader difficulty.
My solution for myself as a writer? To spend time making sure that the first sentence of every paragraph in Intro/Background and Discussion makes a step-by-step argument supported by the rest of the paragraph. Follow standardized structure for the rest. Keep language precise yet simple as possible.
Solution for research readers? Read the whole article understanding what you can and keep a research glossary handy (e.g., https://sites.google.com/site/nursingresearchaid/week-1. Even if practice doesn’t make you perfect, it works in learning a new language–whether it is a ‘foreign’ language or a scientific one.
Flaky conferences can taken advantage of your time, money and energy. My own publications in bona fide journals have triggered an onslaught of emails from probably predatory conferences–World Congresses of this and that (global health, nursing, education, etc.). The cartoon below totally resonates! Thanks PHD Comics.
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.
For RNs wanting to pursue a doctorate, it is important to pick a degree that best matches your anticipated career path. The shortest simplest explanation of the difference in these degrees is probably:
PhD – If you want to be a nurse scientist & teach in a university & conduct nursing research.
DNP– If you want to be an advanced practice nurse, who primarily uses research in leadership, QI, patient care, etc. along with measuring project outcomes.
Of course, some DNPs teach in universities, particularly in DNP programs. PhDs may otherwise be better prepared for faculty roles. I encourage you to look carefully at the curriculum at the school where you hope to study and expectations of a university where you hope to teach. Speak with faculty, & choose wisely.
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
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.)