Deciding course content

Although I view 2117 as a statistics class, its title, Introduction to Statistics for the Health Sciences, reminds me that it is statistics situated within a specific context. 3117’s title, Principles of Biostatistics, makes this even clearer.

As such, I want the specific statistical skills students walk out of the course with to be well-aligned with the tasks they might be asked to do in their careers. How should I decide on content for the course and how do I anticipate what students will need?

I have professional experience as a statistician in the health sciences, and largely drew upon that experience in the past to decide on relevant content for this course. However, I wanted a stronger evidence-base for my decisions. Luckily, a recent study by Hayat, et. al (2017) sampled published papers in public health journals. I could use this to decide the typical basket of tools my students might need to be familiar with.

Hayat, et. al. (2017) found the following epidemiological terms common:

  • Prevalance
  • Relative Risk
  • Odds Ratio
  • Incidence
  • Mortality
  • Hazard Ratio

Hayat, et. al. (2017) found the following statistics tests common (including p-values and confidence intervals):

  • T-test
  • Chi-squared Test / Exact Test (presumably for contingency tables)
  • Correlation tests
  • Non-parametric tests

Hayat, et. al. (2017) found the following statistics models common:

  • ANOVA
  • Linear Regression
  • Logistic Regression
  • Poisson Regression
  • Cox Proportional Hazards Regression
  • Generalized Linear Mixed Models

These results gave me a frame from which I could choose content for 2117 and 3117. I will introduce prevalence, relative risk, odds ratios, and incidence in 2117, and introduce mortality and hazard ratios in 3117. I will introduce the p-values, confidence intervals, T-test, Chi-squared test, and correlation tests in 2117, and introduce non-parametric tests in 3117. I will introduce ANOVA and linear regression in 2117, and generalized linear models in 3117.

While I certainly don’t want to automatically perpetuate the status quo, if I wish my students to be statistically literate, especially in the field of the health sciences, then they must be familiar with these methods. However, I struggle between balancing this traditional base and exposing my students to more modern methods. It seems unlikely that many will take more statistics courses in their lives. Is it my responsibility to seize this opportunity to expose them now, at the risk of leaving them without common skills prevalent in their field?

Nevertheless, with these content learning objectives in place, I could now move on to the specific statistical reasoning learning objectives and content sequencing.

References and further reading:

Hayat, M. J., Powell, A., Johnson, T., & Cadwell, B. L. (2017). Statistical methods used in the public health literature and implications for training of public health professionals. PloS one, 12(6), e0179032.

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