Six tools for research in educational statistics

In this months’ Journal of Educational and Behavioral Statistics, Howard Wainer* identifies six necessary tools that researchers in educational and behavior statistics should master:

  • Bayesian methods. “Bayesian methods allow us to do easily what would be hard otherwise.” Sounds like I am on the right track.
  • Causal inference. It’s not enough to chant “correlation is not causation.” You need to read and understand Rubin.
  • Missing data. That’s not exactly a tool, is it? It’s a problem that afflicts all researchers working with educational and behavioral data. Wainer says, “Dealing with missing data is, quite simply, the most important practical problem facing researchers.”
  • Picturing data. “A graph of data is the best way to find something that you were not looking for.” Resources: Tufte, Flowing Data.
  • Writing clear prose. I think it’s funny he should bring this up, because to make this list more clear, he should have made each bullet point parallel (e.g., “Use Bayesian methods,” “Understand causal inference,” “Handle missing data,” “Explore data visually,” “Write clear prose.”) In other words, I agree with him on the importance of writing clear prose.
  • A deep understanding of Type I (false negative) and Type II (false positive) errors. Specifically, statistical researchers need to pay as much attention to Type II errors as to Type I.

* Wainer, H. (2010). 14 Conversations about three things. Journal of Educational and Behavioral Statistics 35(1).


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