This scale , created by Haidt and Wilson, predicts your political leanings based upon seemingly unrelated questions. Screen grab from time.com You can use this in a classroom to 1) demonstrate interactive, Likert-type scales, 2) face validity (or lack there of). I think this would be 3) useful for a psychometrics class to discuss scale building. Finally, the update at the end of the article mentions 4) both the n-size and the correlation coefficient for their reliability study, allowing you discuss those concepts with students. For more about this research, try yourmorals.org

This story discusses the many levels of analysis required to get to the bottom of the hypothesis stated in the title of this story. For instance, are cigarettes or the patch better for mom? The baby? If the patch isn't great for either but still better than smoking, what sort of advice should a health care provider give to their patient who is struggling to quit smoking? What about animal model data? I think this story also opens up the conversation about how few medical interventions are tested on pregnant women (understandably so), and, as such,  researchers have to opt for more observational research studies when investigating medical interventions for protected populations.

Here is a link to a recent co-authored publication that used Second Life to teach students about virtual data collection as well as the broader trend in psychology to study how virtual environments influence interpersonal interactions. Specifically, students replicated evolutionary psychology findings using Second Life avatars. We also discuss best practices for using Second Life in the class room as well as our partial replication of previously established evolutionary psychology findings (Clark & Hatfield, 1989, Buss, Larson, Weston, & Semmelroth, 1992).

Two prominent psychology journals are changing their standards for publication in order to address several long-standing debates in statistics (p-values v. effect sizes and point estimates of the mean v. confidence intervals). Here are the details for changes that the Association for Psychological Science is creating for their gold-standard publication, Psychological Science, in order to improve the transparency in data reporting. Some of the big changes include mandatory reporting of effect sizes, confidence intervals, and inclusion of any scales or measures that were non-significant. This might be useful in class when describing why p-values and means are imperfect, the old p-value v. effect size debate, and how one can bend the truth with statistics via research methodology (and glossing over/completely neglecting N.S. findings). These examples are also useful in demonstrating to your students that these issues we discuss in class have real world ramifications and aren't be...

I am teaching positive psychology for the first time this semester. One way to quickly teach students that this isn't just Happy Psych. 101 is to show them convincing data collected by an international organization (here, the United Nations) that demonstrates the link between positive psychology and the well-being of nations. This data isn't just for a positive psychology class: You could also use it more broadly to demonstrate how research methods have to be adjusted when data is collected internationally (see item 4) and as examples of different kinds of data analysis (as described under item 1). 1) Report on international happiness data from the United Nations . If you look through the data collected, there is a survival analysis related to longevity and affect on page 66. A graphic on page 21 describes factors that account for global variance in happiness levels across countries. There is also a lot of data about mental health care spending in different nations. 2 ...

A great, foreboding video  (here is a link to the same video at YouTube in case you hit the paywall) about the actual size and implication of Type II errors in scientific research. This video does a great job of illustrating what p < .05 means in the context of thousands of experiments. Here is an article from The Economist on the same topic. From TheEconomist

While it isn't hypothesis driven research  data, this data was collected to see which states are the sweariest. The data collection itself is interesting and a good, teachable example. First, the article describes previous research that looked at swearing by state (typically, using publicly available data via Twitter or Facebook). Then, they describe the data collection used for the current research: " A new map, though, takes a more complicated approach. Instead of using text, it uses data gathered from ... phone calls. You know how, when you call a customer service rep for your ISP or your bank or what have you, you're informed that your call will be recorded?  Marchex Institute , the data and research arm of the ad firm Marchex,  got ahold of the data that resulted from some recordings , examining more than 600,000 phone calls from the past 12 months—calls placed by consumers to businesses across 30 different industries. It then used call mining technology to isola...