Research is not all white lab coats and test tubes. Simply put,research is a systematic way to ask and answer your questions by looking for patterns in new or existing data.Typical steps are clockwise are in Figure 1 below.
In the Figure 1 below, I’ve included the step of IRB review. Remember that an IRB (institutional review board/ AKA human subjects review board) must review all research procedures for your compliance with federal ethical and legal rulesbeforeyou begin any data collection or subject contact.
Why does every little kid, who doesn’t get the answer they want from Mom, then go and ask Dad or Grandma or Auntie hoping to get a different response? Because kids know that the answer you get to your question depends on who you ask!!
In research it’s the same. Sometimes the answer you get depends on who you ask. Thus, it’s not only important to write out a well-formulated research question, but you need to ask the right persons! Unlike little children, however, you need to ask a representative sample that will give you an accurate scientific answer–not just an answer that matches your preconceived notions of the answer you want. The answer you need is not necessarily the one you want…or expect.
What research sample will give you a more accurate, scientific answer? It’s a sample that represents the population of interest. NO sampling method guarantees a representative sample from the population, but some sort of random selection of participants from the population is likely to be best. Sometimes, however, it’s not possible or affordable to do random sampling and so researchers pick their sample using a different sampling method.
Remember that the BEST samples are REPRESENTATIVE. That may or may not mean they are randomly selected.
Whatever sampling method is used, be clear yourself about the strengths and weaknesses of that sampling method. Report your sampling method in the procedures section of your final report. That way your readers can tell whether you asked subjects who were the equivalent of “Mom,” “Dad,” or both.
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.
It is original history that holds lessons for nurses today.What is it that we learn from the women, whose stories are within this book?
I think it’s mainly this: “Do not let your personal limitations stop you from doing the good that you see to do in the world.”
This book contains the previously untold story of how a remarkable set of lay and professional nurses shaped Church of Christ (COC) missions in southeastern Nigeria. No archive of their work existed, and I enjoyed the privilege of compiling the story from the memories, bags, basements, and boxes of the women who lived this story and those who knew them. The book was 10 years in the making.
These women’s decisions and actions occurred within a broader shift of COC perspectives away from missionary healthcare as incidental volunteer women’s work, and toward healthcare missions as a Christian duty. For each being a missionary nurse meant delivering healthcare as part of Christian evangelism. To that end they executed multiple roles: healer, educator, revolutionary, advocate, good-will ambassador, protector, administrator, evangelist, role model, fund-raiser, friend, and colleague. We did “everything that needed to be done that there was nobody to do,” reflected missionary Nancy Petty RN.
Enjoy the read, and pursue the good that you can do in this world.
[Featured image: August 23, 1965. The Nigerian Christian Hospital Outpatient Clinic opens. Photographer JR Morgan. Used with permission from JRM private collection]
“History provides current nurses with the same intellectual and political tools that determined nursing pioneers applied to shape nursing values and beliefs to the social context of their times. Nursing history is not an ornament to be displayed on anniversary days, nor does it consist of only happy stories to be recalled and retold on special occasions. Nursing history is a vivid testimony, meant to incite, instruct, and inspire today’s nurses as they bravely tread the winding path of a reinvented health care system.” (American Association for History of Nursing)
What’s the difference between statistical and clinical significance? Here’s a quick, non-exhaustive intro.
In short, statistical significance is when the difference in outcomes between an experimental and a control group is greater than would happen by chance alone. For example, in a trial of whether gum chewing promoted return of bowel activity among post-op patients, one post-op group would chew gum and the other group would not. Then researchers would statistically compare timing of return of bowel activity between the two groups to see if the difference was greater than would occur by chance (p<.05 or p<.01). If the probability (p) level of the statistical test is less than .05 then we have very strong evidence that gum chewing made the difference. [See example of gum chewing trial in free full text Ledari, Barat, & Delavar (2012).]
All well and good.
However, the effect of an intervention may be statistically significant, but not clinically meaningful to practitioners. Or the intervention’s effects may not be statistically significant, and yet still be clinically important enough to be worth the time, cost, and effort it takes to implement.
What is clinical significance, and how can we tell if something is clinically significant? Two overlapping views:
“Clinical relevance (also known as clinical significance) indicates whether the results of a study are meaningful or not for several stakeholders.7 A clinically relevant intervention is the one whose effects are large enough to make the associated costs, inconveniences, and harms worthwhile.8” (Armiji-Oliva, 2018).
Clinical significance is “the practical importance of research results in terms of whether they have genuine, palpable effects on the daily lives of patients or on the health care decisions made on their behalf” (p. 449, Polit & Beck 2012).
Let me illustrate. Researchers recently examined the effects of a 1300-1500 quiet time on a post-partum unit. Outcome measures showed that women’s exclusive breastfeeding rates increased 14%. However, this change was not statistically significant (p = .39)—a probability value well above p < .05. Nonetheless, researchers concluded that the findings were clinically significant because a higher percent of women exclusively breastfed their infants after quiet time, and arguably for those couplets the difference was “genuine” and “palpable” (p. 449, Polit & Beck). The time, cost, and effort of implementing a low risk quiet time was reasonably associated with producing valuable outcomes for some.
Always remember that the higher the risk of the intervention, the more cautious should be your translation of findings into a particular practice setting. Don’t overestimate, but don’t overlook, clinical significance in your search to improve patient care.
Critical thinking: How might issues of statistical versus clinical significance inform the dialogue on mask wearing during the pandemic?