Today, we will be looking at data that I collected from the RateMyProfessor  (RMP) website on professors at William Paterson University.  I placed the data in an MS Access database.  You can download the data from the links page. 

The database includes two tables:

  1. population.  This data comes from the list for William Paterson.  Each row is a different professor.  I removed names to protect the innocent (and the guilty).  Variables include department, average quality (the average of all helpfulness and clarity ratings for a professor), and n (the number of people rating the professor).  
  2. sample.  This data comes from individual faculty pages on RMP.  Because this was a time intensive process, I did not include all professors here.  Instead, I took a random sample of approximately 90 professors from the list of all professors in the population table.  For each professor, I extracted data from up to 20 students on helpfulness, clarity, easiness, and interest.  The definitions of these concepts are provided here.

What sorts of questions can you address with this data?  We are going to start today by considering what questions might be addressed by analyzing this data. 

One thing we are going to look at is Bayesian averages.

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    Jacob Felson received his doctorate in sociology from Penn State University in December, 2009. He received a master's in sociology from Penn State in 2004, and a bachelor's degree, also in sociology, from the University of Chicago in 2002


    June 2010
    May 2010



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