WHAT STATISTICS DO PRACTITIONERS NEED?

What Statistics Do Practitioners Need?

Graduate professional programs seldom provide practitioners—from M.D.s to Ed.D.s—what they need to understand quantitative research relevant to their work.  Future practitioners rarely take more than one or two courses in quantitative methods, and given the way these courses are usually taught, this is not sufficient.  But one or two courses, taught in an effective way, with the needs of practitioners in mind, could suffice. 

What would effective statistics courses for practitioners look like?  It’s easy to specify what they would not be. They would not emphasize explanations of the mathematical foundations of statistical theory.  Nor would courses for practitioners devote much time to how to calculate the statistics they might use in their research, should they ever do any. 

What do they need?  Chiefly, they need to be able to comprehend and critically interpret the research findings in their fields.  That means they require a good understanding of the ways findings are reported by researchers using advanced methods.  The instructors of future practitioners have to be able to explain highly sophisticated techniques in lay terms, and in very abridged ways.  This is a form of teaching that has much in common with translating from one language, the statistician’s, to another, the practitioner’s.  In other words, it is a form of translational research.  The teachers and students need to focus on sufficient understanding of a broad range of concepts rather than learning any statistical method in great depth.  

The Traditional Approach

By contrast, instructors who use a more traditional approach, and insist on a firm grasp of theoretical fundamentals and computational know-how, will not get very far in a course or two.  It will be difficult to go beyond a handful of basics, such as the normal curve, sampling distributions, t-tests, ANOVAs, standard scores, p-values, confidence intervals, and rudimentary correlation and regression.  These are all fine subjects, and it is important for future statisticians to probe them deeply, but if these topics constitute the whole of the armamentarium of future practitioners, those practitioners will not be able to read most of the research in their fields.  And that means they won't be able to engage in evidence-based practice.

The Translational Approach

The main instructional method of the translational approach is working with students to help them understand research articles reporting findings in their fields. If you want future practitioners to be able to read research with sufficient comprehension that they can apply it to practice, teach them how to read the articles—don’t revel in the fine points of the probability theory behind sampling distributions.  First, help students to decipher research articles in their fields, and then practice with them how discuss, with critical awareness, the outcomes presented in those articles.  Instructors should supply some of the articles students study, but they should also encourage students to find articles that they would like to learn how to read.

The articles will of course vary considerably by field, and so too will the advanced statistical methods most useful to practitioners.  Regardless of the specifics, the method of instruction will be the same: work with students to enhance their critical understanding of quantitative research in their fields—its limitations as well as its applications to their professional practice—by teaching them how to decipher and evaluate it.

Practitioner-Researchers

What about practitioners who become involved in research?  Some surely will, and they should be encouraged.  But the traditional approach, stressing mathematical foundations of statistics and computational details, is unlikely to be a way to stimulate practitioners’ interest in doing research on topics relevant to their fields of practice. 

Most practitioner-researchers using quantitative methods will probably work with co-authors who have methodological expertise that can supplement the practitioners’ substantive knowledge.  Successful practitioner-researchers will usually pay more attention to research design rather than analysis.  When thinking about analysis they will focus more on selecting the right analysis methods and less on the details of how to crunch the numbers.  Learning how to design research and write analysis plans that can successfully address research questions is an attainable goal for many thoughtful practitioners.  In graduate and professional education, it can be fostered by the careful perusal of successful (and not-so-successful) research in students’ fields. 

Raising Standards

This approach raises standards.  It does not lower them, as many statisticians might fear.  Investigating the theoretical foundations of statistics is interesting to many of us, but it is largely irrelevant to the education of future practitioners.  Does it truly maintain standards to insist on teaching topics that are irrelevant to students?  Doing computations is comparatively easy (with software assistance).   But, understanding and reasoning about evidence so as to try to solve real problems is hard.  And it is very important. 

  

Incentives for Instructors

        Why should statisticians who teach quantitative research methods courses take the approach advocated here?  What’s the incentive to change traditional ways?  One motivation is that every semester you get to read cutting-edge real research with your students, rather than trying to interest them in the same old textbook descriptions of basic statistical concepts.  That makes class preparation much more interesting.  

        Of greater importance, by teaching subjects more relevant to students’ futures, you will be better fulfilling your responsibilities as an instructor.  If you don’t teach students about quantitative methods in ways relevant to them, you make it hard for them to incorporate research into their professional practice, and you contribute to growing problem of the separation of research and practice.  As one physician put it to me, “I wish I had had more time to learn statistical methods, but the press of content courses was too great—so I just read the abstracts and hope that’s good enough.”  That’s a sad commentary on his education.  I changed doctors.

Further reading

The following books deal with quite advanced topics in quantitative data analysis and do so assuming little if any statistical knowledge on the part of the reader. 

For biomedical fields, two good books that exemplify the approach argued for in this blog are: Motulsky’s, Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking (2^nd edition, 2010) and Bailar & Hoaglin’s, Medical Uses of Statistics (3^rd edition, 2009). 

For the social sciences see Spicer, Making Sense of Multivariate Data Analysis (2005), Vogt, Quantitative Research Methods for Professionals (2007), Vogt et al., When to Use What Research Design (2012), and Vogt et al., Selecting the Right Analyses for Your Data (2014, in production).

More elementary discussions are available in the field of “analytics.”  This is a new term for statistics.  It is usually as applied to studying big data (often Web data) in order to make business decisions. A popular example is Keeping Up with the Quants (2013) by Davenport and Kim.