This article starts with a nice anecdote about being dismissive about what someone else is saying ends up hurting you. It also provides a nice structure, POWER, for organizing consulting meetings. POWER stands for Prepare, Open, Work, End, and Reflect. This article was a basis for some of the content in an interesting webinar on consulting. Continue reading

# Author Archives: pmean

# PMean: How much missingness can you tolerate?

I got a question about how much missing data could you have in a study and still feel comfortable with your data analysis. It’s a question with no hard and fast answer, but I get the question so often that I have developed some general guidance. Continue reading

# Recommended: Bayesian meta-analysis of two proportions in random control trials

I got a question about Bayesian meta-analysis and found this nice teaching example. I’m not sure if the graphs are from the R package bayesmeta, but it looks like it. Continue reading

# Recommended: Section 508 CoP: PDF Accessibility – Part One

I have been somewhat lax in making my work accessible for people with disabilities. This video covers some of the basic things you can do with a PDF file to insure that it is can be easily read by screen reading software. There are similar videos for Microsoft Word and Microsoft Powerpoint files. Continue reading

# Recommended: Microsoft is creating an oracle for catching biased AI algorithms

Artificial Intelligence (AI) algorithms that are used for crime detection, loan approvals, and employee evaluations are considered by many to be objective, but they can sometimes have many of the same prejudices and biases that human evaluators have. Given the opacity of many black box approaches to AI, this could lead to serious problems with fairness and equity. This article discusses an admittedly imperfect approach by Microsoft to evaluate these AI algorithms using (surprise!) an AI algorithm. It flags situations where an algorithm appears to have problems with unfair differential treatments based on race, gender, or age. Continue reading

# PMean: What goes into a contract for a consultation

Someone asked me about what sort of contract to use with a new client. This person did not need a very detailed contract, but said that a handshake would not suffice. Here’s what I suggested. Continue reading

# PMean: Big data groups at UMKC and UM/Columbia

I was at a meeting where I learned about some recent efforts with big data at the University of Missouri-Kansas City and the University of Missouri/Columbia. Here’s a brief description along with links. Continue reading

# Recommended: Analysis of Pretest-Postest Designs

A very nice resource that talks about difference scores, relative change models, analysis of covariance, and repeated measures models. Continue reading

# Recommended: Applied Survival Analysis

Although the title says “Applied”, this book has a fair amount of mathematics in it, which helps you understand why certain approaches work well. The sample outputs for this book are reproduced in several different software programs at the UCLA Institute for Digital Research and Education. Continue reading

# Recommended: Interval regression | R data analysis examples

This page shows R code to handle the tricky data sets where the response is known to be inside some interval. Continue reading