Consulting Resources

• Reference Collection to push back against "Common Statistical Myths"
  • Post-hoc power is not a thing
  • Avoid analyzing change scores
  • Inappropriately splitting continuous variables into categorical ones
• Language for communicating frequentist results about treatment effects
  • how to communicate results of a null hypothesis test
  • how to explain confidence interval
• Flexible Imputation of Missing Data, The Multiple Imputation book
  • section 6.3.2: "In my experience, the increase in explained variance in linear regression is typically negligible after the best, say, 15 variables have been included. For imputation purposes, it is expedient to select a suitable subset of data that contains no more than 15 to 25 variables"
• GLMM FAQ
• Do not use averages with Likert scale data
  • acting as if Likert or other ordinal scales are continuous level data leads to many problems of interpretation....a great way to understand the conceptual problem is to realize that the mean of Agree and Strongly Agree is not Agree-And-A-Half.
• Statistics Notes in the British Medical Journal
  • Excellent refresher on various statistical concepts
• Data Organization in Spreadsheets
  • The basic principles are: be consistent, write dates like YYYY-MM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.
• Moving to a World Beyond "p < 0.05"
  • Don’t base your conclusions solely on whether an association or effect was found to be “statistically significant” (i.e., the p-value passed some arbitrary threshold such as p < 0.05).
  • Don’t believe that an association or effect exists just because it was statistically significant.
  • Don’t believe that an association or effect is absent just because it was not statistically significant.
  • Don’t believe that your p-value gives the probability that chance alone produced the observed association or effect or the probability that your test hypothesis is true.
  • Don’t conclude anything about scientific or practical importance based on statistical significance (or lack thereof).
  • We summarize our recommendations in two sentences totaling seven words: "Accept uncertainty. Be thoughtful, open, and modest."
• CRAN Task Views
  • Explore curated lists of R packages based on topic