How do we count the number of poor people in America? How do we operationalize "poor"? That is the psychometric topic of this opinion piece from the New York Times  ( .pdf of same here ). This article outlines several ways of defining poor in America, including: 1)"Jencks’s methodology is simple. He starts with the official 2013 United States poverty rate of 14.5 percent. In 2013, the government determined that 45.3 million people in the United States were living in poverty, or 14.5 percent of the population.Jencks makes three subtractions from the official level to account for expanded food and housing benefits (3 percentage points); the refundable earned-income tax credit and child tax credit (3 points); and the use of the Personal Consumption Expenditures index instead of the Consumer Price Index to measure inflation (3.7 percentage points)." 2)  " Other credible ways to define poverty  paint a different picture. One is to count all those living ...

Scott Janish loves beer, statistics, and blogging (a man after my own heart). His blog discusses home brewing as well as data related to beer. One of his statsy blog posts  looked at the relationship between average alcohol by volume for a beer style (below, on the x-axis) and the average rating (from beeradvocate.com , y-axis). He found, perhaps intuitively, a positive correlation between the average Beer Style review for a type of beer and the moderate alcohol content for that type of beer. Scott was kind enough to provide us with his data set, turning this into a most teachable moment. http://scottjanish.com/relationship-of-abv-to-beer-scores/ How to use it in class: 1) Scott provides his data. The r is .418, which isn't mighty impressive. However, you could teach your students about influential observations/outliers in regression/correlation by asking them to return to the original data, eliminate the 9 data points inconsistent with the larger pattern, and reanalyze th...

51% of the babies born in the US are male. Why? For a long time, people just assumed that the skew started at conception. Then Steven Orzack decided to test this assumption. He (and colleagues) collected sex data from abortions, miscarriages, live births (30 million records!), fertility clinics (140,00 embryos!), and different fetal screening tests (90,000 medical records!) to really get at the root of the sex skew/conception assumption. And the assumption didn't hold up: The sex ratio is pretty close to 50:50 at conception. Further analysis of the data found that female fetuses are more likely to be lost during pregnancy. Original research article here . Richard Harris' (reporting for NPR) radio story and interview with Orzack here . Use this story in class as a discussion piece about long held (but never empirically supported) assumptions in the sciences and why we need to conduct research in order to test such assumptions. For example: 1) Discuss the weaknesses of previo...

This animated short from National Geographic touches on averages, median, mode, sampling, and the need for cross-cultural research. When defining the typical (modal) human, the video provides good examples of when to use mode (when determining which country has the largest population) and when to use median (median age in the world). It also illustrates the need to collect cross-cultural data before making any broad statements about typicality (when describing how "typical" is relative to a population).

This story describes how psychologists used content analysis to provide evidence that Shakespeare indeed authored the play Double Falsehood. The play in question has been the subject of literary dispute for hundreds of years. It was originally published by Lewis Theobold in 1727. Theobold claimed it was based on unpublished works by Shakespeare. And literary scholars have been debating this claim ever since. Enter two psychology professors, Boyd and Pennebaker. They decided to tackle this debate via statistics. They conducted a content analysis Double Falsehood as well as confirmed work by Shakespeare. What they tested for: "Under the supervision of University of Texas psychology professors Ryan L. Boyd and James W. Pennebaker, machines churned through 54 plays -- 33 by Shakespeare, nine by Fletcher and 12 by Theobold -- and tirelessly computed each play's average sentence-length, quantified the complexity and psychological valence of its language, and sussed out the ...

http://www.theonion.com/articles/study-finds-those-with-deceased-family-members-at,38463/ Man, I love the fact that when The Onion does an article about pretend research , they don't skimp on the details. This story includes the journal (NEJM), n -size (85,000), research design (longitudinal), a lead author, covariates (race, nationality, etc.) as well as a replication study. I like to think that the person who writes these articles paid really close attention in a statistics class or a research methods class and remembers just enough to be a smart ass about the research process. From the article: I used this just last week as a review example of correlation =/= causation. The students liked it. Mission accomplished.