Author Archives: pmean

PMean: Peer grading in Introduction to R, SPSS, SAS

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I’ve gotten some helpful feedback that I need to encourage more interactions among students in the on-line classes, Introduction to R, Introduction to SPSS, and Introduction to SAS. No just interactions of the students with the teacher, but interactions between the students.

In many online classes this is done by encouraging online discussion of the material in the class. This is not so easy, however, for these three classes. I can just imagine myself posting the following on Blackboard. “Tell me what you think about the read.csv function in R.”

There are a couple of ways, however, that make sense for technical classes like these. Continue reading →

PMean: Changes to the Introduction to R, SAS, and SPSS classes

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I have helped develop and have taught (along with other faculty in our department) three one credit hour pass/fail classes: Introduction to R, Introduction to SPSS, and Introduction to SAS. These classes were developed back in 2014-2015 and they are in need of some serious updates. I will try to outline some of the updates that I think these classes need in this blog post. Continue reading →

Recommended: Is vaccine effectiveness (VE) different from vaccine efficacy

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This is a non-technical discussion of the difference between effectiveness and efficacy (two easily confused terms) in the context of vaccination. Short answer: efficacy is a measurement under ideal circumstances while effectiveness is a measurement in a “real-world” setting. Continue reading →

Recommended: A sampling of outstanding women in analytics

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This is a list (with single paragraph descriptions) of 186 women who have accomplished great things in the area of Analytics. There is a brief accompanying article at the Forbes magazine website, but it is very brief. The author of this list, Meta S. Brown, defines Analytics quite broadly, so the women have very diverse backgrounds and interests. I only recognized one name off the bat, Grace Wahba, an excellent researcher, but someone, unfortunately, that I haven’t met. If I get a chance, I’ll include in a separate blog post a list of outstanding women in Analytics that I HAVE met. Meta Brown’s list includes links so you can find out more about these talented women. Continue reading →

PMean: Mixed up variable names in SAS

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Some of my students in the Introduction to SAS class were having trouble reading in a tab-delimited text file, and it’s not too surprising, because some of the student in the Introduction to R class were having problems with the same file. Here’s some details about the data set, what problems it caused, and a couple of ways that you could fix it. Continue reading →

Recommended: The history of Hadoop

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If you want to understand big data, you need to understand Hadoop. Hadoop is the technology underlying many big data efforts. But most of the descriptions of Hadoop are jargon laden and impenetrable to newcomers. Well, maybe just impenetrable to this newcomer. But one great revelation to me was a historical note as to WHY there was a need to develop Hadoop. It was all those pages that had to be indexed by search engines at Google and Yahoo. So I went out to try to find more details. This article, with a ton of references throughout, is an excellent introduction to the precursors to Hadoop, the development of Hadoop itself, and the explosion of systems that used Hadoop as their foundation. Continue reading →

Recommended: Adherence to Methodological Standards in Research Using the National Inpatient Sample

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I normally don’t recommend articles that are stuck behind pay walls, but this is an important article. It shows how 85% of a sample of research studies using the National Inpatient Sample database failed to follow at least one of seven well documented practice recommendations of the Agency for Healthcare Research and Quality. Continue reading →

PMean: Sentiment analysis of A Christmas Carol

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I was at an interesting talk about sentiment analysis and decided to try something simple myself. Sentiment analysis is a text analytics method that compares text data with a list of words with positive or negative sentiments. The relative frequency of the positive or negative words is a crude measure of the general sentiment of the text item. I ran a sentiment analysis on the text of the famous Charles Dickens novel, A Christmas Carol. Continue reading →