Megalodon Meets Rejection

Transforming teaching into scholarship.  Turner T, Palazzi D, Ward M, Lorin M. Clin Teach 2012; 9: 363-367. Available online from the Baystate Health Sciences Library or from PubMed at your institution.

I got a paper rejected today. There are two possible reasons for this. A) The enormity of my awesomeness and unique perspective are too immense for this journal, and accepting the article would have been too overwhelming of an experience for the editors to handle, the equivalent of putting Megalodon into an aquarium. Or, B) I am a loathsome mess of failure in academic medicine (and life) whose never had an interesting thought or perspective on anything ever, and the world is collectively sighing now that someone has finally made me aware. 

Of course, it's possible there's a third option. Something in the middle: A 1/2) The feedback I got from the reviewers was good feedback and advice and that, if I take this advice, I can put together a better manuscript that has a solid chance of being published. Not at this journal, mind you, but at a lesser, lower impact journal whose editors are monkeys or people who write professional blogs. 

And this is somewhat comforting. In fact, mentors guide us to meet rejection with some kind of perseverance. But the initial get-up-and-go required for putting together and submitting your clinical teaching as scholarly work is an immense animal (Megalodon, perhaps) to be tamed even before the publication decision. And, if the rejection comes, what are some other considerations? 

This article by Turner and colleagues is a useful read; in a 5-page article (don't worry - there are at least two tables in there, only 9 references, and two photos of clinical teaching. Well, one photo of clinical teaching and one photo of a woman writing near a coffee, which must be clinical scholarship). 

The authors bring up many things that should form the backdrop for a clinical educator seeking to disseminate their good work: Boyer and Glassick and the scholarship of teaching, working collaboratively, peer review, outlets for scholarship, and the educator's portfolio. This article is brief - like being offered one bite of an entire Carnival Cruise buffet - but it still may be enough to figure out how hungry you are. And having this sense of the possibilities for educational scholarship is helpful for keeping your projects going (around A 1/2).   

Bottom Line:

Take ten minutes out of your day to read this article and make sure it's nothing new for you. This snapshot of educational scholarship could help frame your perspective so that the immense project taking up space on your To Do List won't die there.

Instrument Construction, or "If It Was Easy, You Did It Wrong"

Thriving in long-term care facilities: instrument development, correspondence between proxy and residents' self ratings and internal consistency in the Norwegian version. Bergland A, Kirkevold M, Sandman PO, Hofoss D, Vassbo T, Edvardsson D. J Adv Nursing. 2013; Early Online. Available online from the Baystate Health Sciences Library, or from PubMed at your institution.

In educational research, we measure things that Paul Visintainer might call "soft." These things are constructs (a term which might seem familiar to you because Tony Artino gave us some insight to writing surveys not too long ago). Constructs help us describe our learners and our patients. Well, this article demonstrates a comprehensive look at developing instruments to measure constructs (no, no - stay with me!).

There are many ways to develop and validate your instrument (yes, I know, the instrument itself is not valid, it produces valid DATA. I know this. I preach this. But in the essence of shortening the sentence, I said "validate your instrument." As long as we're all on board that an instrument which produces valid scores for one group doesn't automatically produce valid scores for another, you'll please bear with me on the details). 

So, there are many ways to develop an instrument with rigor that set it up to produce valid scores. These ways all have some similarities, which I think are identified well in this article. They boil down to:

First, the "thing" being measured is defined. This is simple, yet critical, and often skipped. (Bad. No skipping.) In this article, the authors measure "thriving" which they define by what it is and what it is not. In fact, they have a nice background on how it has been defined previously. Even if we think that everyone knows what the construct is that we're measuring, we still define it. (Quick example: Try to measure "success." From whose perspective? Based on financial security? Based on happiness? Wait - how do we measure "happiness"? Exactly.)

Second, question items are developed in a meaningful and thoughtful way. Think theoretical or conceptual framework. Perhaps you conducted focus groups or in-depth interviews and then analyzed them for themes which became the backbone of your instrument. Or, like in this article, you used interviews and a structured lit review. Note that "talked it over with your friend" does not appear here.  

Third, you report on some measures of consistency/reliability and validity. Side note here about validity and reliability: my two-year-old sleeps with a stuffed monkey. He needs this monkey to survive. From what I can tell, the monkey does not feel the same way. This is sort of the deal with validity (2-year-old) and reliability (monkey). Validity needs reliability to exist. But reliability can exist just fine without validity. So to demonstrate validity, you must accumulate evidence that it is valid. An accumulation of reliability metrics (see this article) and your theoretically and conceptually sound process for item development (see above) can help here. 

Fourth, and Tony Artino wrote about this, instruments need to be piloted. In the highlighted article, a pilot instrument was designed for three different groups of respondents. Statistical analysis helped to guide item reduction. 

Even with their comprehensive approach to instrument design in this article, the authors still end with a tool that they claim needs "further psychometric evaluation and refinement." In other words, even after all this work, it's still not done! And, of course, that's the rub with developing an instrument from scratch - it's a lot tougher than using what already exists (thwarting the wheel reinvention). And, if it's not tough, check to see if you missed something, or start a blog to explain it to us. 

Bottom Line:

This article is a good view of the basic processes of instrument design, and presents the basic foundation - from defining the construct to a theoretical framework to pilot testing. Sure, it's a lot to digest. But, the impact of our clinical practice isn't just apparent in physiological measures, and good instruments - new instruments - can help us uncover some of the most important ways that we, as caregivers, have true impact. 

From the Field: Can You Write an Effective Questionnaire? A. Yes, B. Always, C. Read this Post!

Check out this post "From the Field" by Anthony Artino, Jr, PhD, - Associate Professor at Uniformed Services University of the Health Sciences in Bethesda, survey design connoisseur, and, most recently, guest blogger! Apply Dr. Artino's points to research, quality improvement, or your everyday opinion survey. Dig in!

You can't fix by analysis what you've spoiled by design. Rickards G, Magee C, Artino AR. JGME. 2012; 4(4): 407-410. Available online from the Baystate Health Sciences Library and at your institution. 

Tracing the steps of survey design: A graduate medical education research example. Magee C, Rickards G, Byars LA, Artino AR. JGME. 2013; 5(1): 1-5. Available online from the Baystate Health Sciences Library and at your institution. 

What do our respondents think we're asking? Using cognitive interviewing to improve medical education surveys.  Willis GB, Artino AR. JGME. 2013; 5(3): 353-356. Available online from the Baystate Health Sciences Library and at your institution. 

If you’re anything like me, you’ve completed more questionnaires than you care to count. Whether it’s an end-of-course evaluation of a workshop you attended or a satisfaction survey from a recent visit to the clinic, questionnaires are ubiquitous in education and health care. There’s one problem though, and it’s a problem you’ve surely encountered – many questionnaires are poorly designed and often times they fail to capture the very thing they’re attempting to measure. Some common problems with questionnaires include confusing or biased language, baffling visual layout and design, and unclear instructions. Unfortunately, in the age of email, the Internet, and online tools such as SurveyMonkey, the number of survey requests grows exponentially with each passing day. 

Despite the plethora of bad questionnaires that exist in education and health care, there is a wealth of evidence-based knowledge regarding the “best practices” in survey design. Much of this knowledge is detailed in the highlighted articles and briefly summarized below as three principles:

1. You can’t fix by analysis what you’ve spoiled by design.  Even though this principle is true for all types of research and evaluation, it is especially true in questionnaire design for one simple reason – when creating a survey, we’re often trying to assess things that are traditionally hard to measure (so-called “fuzzy” or non-observable constructs). These fuzzy constructs include things like student anxiety, resident confidence, and faculty job satisfaction. As such, it’s critically important that survey designers take the time to carefully design and pretest their questionnaires prior to implementation.

One way to pretest a questionnaire is to have a group of experts review the items and then have a group of potential respondents complete the survey while you observe. Having experts review your draft ensures, among other things, that the content of your survey is relevant and clear; whereas having potential respondents complete your survey verifies that the way they interpret your items aligns with what you had in mind when you designed the questionnaire.

2. The questions guide the answers. People often underestimate the degree to which the precise wording of a question plays a critical role in determining the answers provided by respondents. Take, for example, the following two questions about health insurance: “Are you fairly treated by your health insurance company?” versus “Does your health insurance company resort to deception in order to cheat you of covered benefits?” These two questions would likely elicit very different responses, and it probably wouldn’t surprise you to find that an advocate for health insurance reform asked the second question. Clearly, words like “deception” and “cheat” are strong indications that the author of the question doesn’t have a high opinion of the health insurance industry. Thus, as this principle implies, the wording of a question largely determines the answers people provide.

And while this principle is true in everyday life, when it comes to questionnaires, the effect is even more pronounced; most surveys don’t give respondents the chance to provide feedback about misunderstandings and ambiguities. Therefore, when it comes to questionnaire design, small wording changes can often make big differences, which is another reason to pretest your survey before sending it out to 3,000 respondents.

3. Think of it as a conversation. At the end of the day, a questionnaire is really just a conversation between you (the skillful survey designer) and your respondents. As such, you should consider the implicit assumptions that underlie the conduct of conversations in everyday life. These conversational “rules” include the idea that speakers should try to be informative, truthful, relevant, and clear. If you break these rules as a survey designer, you shouldn’t be surprised (or upset) if your respondents, in turn, provide you with poor-quality answers.

An important implication of this principle is that you should ask questions when you want to learn something from your respondents, as opposed to asking them to agree or disagree with a list of statements. Asking people to rate a bunch of statements is not very conversational – when’s the last time you went up to a friend in the hallway and asked her to “rate the following statements on a scale of 1 to 10”? At the end of the day, people are more familiar and adept at answering questions – not rating statements – so, as an informed survey designer, you should ask well-thought-out questions and pretest those questions on experts and potential respondents prior to implementing your survey.
  

Notwithstanding the temptation to think of survey design as “more art than science,” there’s actually quite a bit of scientific evidence to guide you through the survey design process. Following these evidence-based best practices will not only save you time and effort during data analysis and interpretation, but they will also improve the chances that your survey will actually measure what you intend it to measure.

Bottom Line:

A. Put some effort and thought into the development of your questionnaire and pretest your survey items before implementation. 

B. Use these articles as a way to develop good practice. 
C. All of the above!