Monday, July 24, 2017

de Frieze's "‘Replication grants’ will allow researchers to repeat nine influential studies that still raise questions"

In my stats classes, we talk about the replication crisis. When introducing the topic, I use this reading from NOBA. And it is very important for students to understand what this crisis is. I think it is also important for my students to think about how science could create an environment where replication is more valued. And the Dutch Organization for Scientific Research has come up with a solution: It is providing grants to nine groups to either 1) replicate famous findings or 2) reanalyze famous findings. This piece from Science details their efforts.

The Dutch Organization for Scientific Research provides more details on the grant recipients, which include several researchers replicating psychology findings:



How to use in class: Again, talk about the replication crisis. Ask you students to generate ways to make replication more valued. Then, give them a bit of faith in psychology/science by sharing this information on how science is on it. From a broader view, this could introduce the idea of grants to your undergraduates or get your graduate students thinking about new avenues for getting their replications funded.

Monday, July 17, 2017

Harris's "Scientists Are Not So Hot At Predicting Which Cancer Studies Will Succeed"

This NPR story is about reproducibility in science that ISN'T psychology, the limitations of expert intuition, and the story is a summary of a recent research article from PLOS Biology (so open science that isn't psychology, too!).

Thrust of the story: Cancer researchers may be having a similar problem to psychologists in terms of replication. I've blogged this issue before. In particular, concerns with replication in cancer research, possibly due to the variability with which lab rats are housed and fed.

So, this story is about a study in which 200 cancer researchers, post-docs, and graduate students took a look at six pre-registered cancer study replications and guessed which studies would successfully replicate. And the participants systematically overestimated the likelihood of replication. However, researchers with high h-indices, were more accurate that the general sample. I wonder if the high h-indicies uncover super-experts or super-researchers who have been around the block and are a bit more cynical about the ability of any research finding to replicate.

How to use in a stats class: False positives: The original research didn't replicate (this time, maybe) AND that the experts judging replicability were overly optimistic. Also, one might wonder if there are potential cancer treatments that we don't know about because of false negatives.

How to use in a research class: The lack of reproduction may signal evidence of the publication bias. Replication is necessary for good science. Experts aren't perfect.


Monday, July 10, 2017

Domonoske's "50 Years Ago, Sugar Industry Quietly Paid Scientists To Point Blame At Fat"

This NPR story discusses research detective work published JAMA. The JAMA article looked at a very influential NEJM review article that investigated the link between diet and Coronary Heart Disease. Specifically, whether sugar or fat contribute more to CHD. The article, written by Harvard researchers decades ago, pinned CHD on fatty diets. But the researchers took money from Big Sugar (which sounds like...a drag queen or CB handle) and communicated with Big Sugar while writing the review article.

This piece discusses how conflict of interest shaped food research and our beliefs about the causes of CHD for decades. And how conflict of interest and institutional/journal prestige shaped this narrative. It also touches on how industry, namely sugar interests, discounted research that finds a sugar:CHD link while promoting and funding research that finds a fat:CHD link.

How to use in a Research Methods class:
-Conflict of interest. The funding received by the researchers from the sugar lobby was never fully disclosed. Sugar lobby communicated with the authors of the original research while they were writing the review article.
-Article of ill repute was a literature review. Opens up the conversation on how influential review papers are. Especially when the authors are from well-reputed institutions and they are printed in well-reputed journals.
-A good example of cherry picking data. Articles critical of sugar where held to a different standard.
-I am a psychologist. I discuss the replication crisis in psychology, but other fields (here, nutrition and heart diseaseresearch) are susceptible to zeitgeist as well.

Monday, July 3, 2017

Chris Wilson's "The Ultimate Harry Potter Quiz: Find Out Which House You Truly Belong In"

Full disclosure: I have no chill when it comes to Harry Potter.

Despite my great bias, I still think this pscyometrically-created (with help from psychologists and Time Magazine's Chris Wilson!) Hogwart's House Sorter is a great example for scale building, validity, descriptive statistics, electronic consent, etc. for stats and research methods.

How to use in a Research Methods class:

1) The article details how the test drew upon the Big Five inventory. And it talks smack about the Myers-Briggs.


2) The article also uses simple language to give a rough sketch of how they used statistics to pair you with your house. The "standard statistical model" is a regression line, the "affinity for each House is measured independently", etc.



While you are taking the quiz itself, there are some RM/statsy lessons:

3) At the end of the quiz, you are asked to contribute some more information. It is a great example of a leading response options as well as implied, electronic consent.


4) The quiz provides descriptive statistics of how well you fit into each House:


5) There is a debriefing:


This isn't the first time I've posted about Chris Wilson's statsy interactive pieces for Time magazine.

Teach Least Squared Error, trends over time, archival data sets via this feature that finds the British equivalent of your first name based on the popularity of your name when you were born versus the same ranked name in England. Bonus: Your students can find out their British name. Mine is Shannon.

Teach percentiles, medians, and I/O's Holland Inventory with this data investigating the relationship between job salary AND Holland personality match for the job. Spoiler alert: This data also provides an example of a non-significant correlation. Bonus: Your students can find out their own Holland Inventory type.

Monday, June 26, 2017

APA's "How to Be A Wise Consumer of Psychological Research"

This is a nice, concise hand out from APA that touches on the main points for evaluating research. In particular, research that has been distilled by science reporters.

It may be a bit light for a traditional research methods class, but I think it would be good for the research methods section of most psychology electives, especially if your students working through source materials.

The article mostly focuses on evaluating for proper sampling techniques. They also have a good list of questions to ask yourself when evaluating research:



This also has an implicit lesson of introducing the APA website to psychology undergraduates and the type of information shared at APA.org. (including, but not limited to, this glossary of psychology terms.)

Monday, June 19, 2017

Winograd's Personality May Change When You Drink, But Less Than You Think

How much do our personalities change when we're drunk? Not as much as we think. We know this due to the self-sacrificing research participants who went to a lab, filled out some scales, got drunk with their friends. For science!

Here is the research, as summarized by the first authorHere is the original study.

This example admittedly panders to undergraduates. But I also think it is an example that will stick in their heads. It provides good examples of:

1) Self-report vs. other-report personality data in research.
-Two weeks prior to the drinking portion, participants completed a Big Five personality scale as if they were drunk. So, there is the self-report of Drunk!Participant. And during the drinking session, participants had their Big Five judged by research assistants coding their interactions with friends, allowing a more object judgment of the Drunk!Participant.

The findings:

https://www.psychologicalscience.org/news/releases/personality-may-change-when-you-drink-but-less-than-you-think.html#.WUP0o-vyvIV


-Why do we need self and other reports? What sort of traits are people most likely to lie about? This could also open up a conversation about Lie scales, especially their use in situations when their is pressure to present well, like during job interviews.

-What other sort of other-reports have your students seen used in research? I've seen research that asks teachers to evaluate students, parents to evaluate children, etc. When might an acquaintance be a better source of data than a stranger?

2) Conceptual examples of repeated measure/within subject t-test and paired-participant/between subjects t-test.

-At Time 1, Ps reported their personality under normal circumstances, and what they think think of their personalities when drunk. Within-subject t-test. Results: Ps believe that their personalities change substantially when drunk.

-At Time 2, while the participants were drunk, they were observed by research assistants. The research assistants made their best guesses at the Ps Big Five. Between-subject, matched t-test. Results: P extroversion seems to increase, but raters didn't find any other increases.

3) Example of using the Big Five in research.


Monday, June 5, 2017

Brenner's "These Hilariously Bad Graphs Are More Confusing Than Helpful"

Brenner, writing for Distractify, has compiled a very healthy list of terrible, terrible graphs and charts. How to use in class:
1) Once you know how NOT to do something, you know how to do it.
2) Bonus points for pointing out the flaws in these charts...double bonus points for creating new charts that correct the incorrect charts.

A few of my favorites:




Monday, May 29, 2017

Daniel's "Where Slang Comes From"

I think that language is fascinating. Back when I taught developmental, I always liked to teach how babies learn to talk in sort of the same way all across the world. I like regional difference in American English (for example, swearing and regional colloquialisms). So, I really like this research that investigates the rise and fall of slang in America. And I think it could be used in a statistics class.

How to use in class?

1. Funny list of descriptive statistics.


2. Research methodology for using Google searches to answer a question. A good opening for discussion of archival data, data mining, and creating inclusion criteria for research methodology.


3. Using graphs to illustrate trends across time. This feature is interactive.


4. Further interactive features demonstrating how heat maps can be used to demonstrate state-by-state popularity over time. Here, "dank memes" peaked in April 2016 in Montana.


5. The author eye-balled the data can came up with common origins of slang: Hip-hop music, politics, "the internets" (technology). This reminds me, conceptually, of cluster analysis. Note: NO CLUSTER ANALYSIS was conducted to come up with the three slang origin categories.

Monday, May 22, 2017

Trendacosta's Mathematician Boldly Claims That Redshirts Don't Actually Die the Most on Star Trek


http://gazomg.deviantart.com/art/Star-Trek-Redshirt-6-The-Walking-Dead-483111105


io9 recaps a talk given by mathematician James Grime. He addressed the long running Star Trek joke that the first people to die are the Red Shirts. Using resources that detail the ins and outs of Star Trek, he determined that:



This makes for a good example of absolute vs. relative risk. Sure, more red shirts may die, absolutely, but proportionally? They only make up 10% of the deaths. Also, I think this is a funny example of using archival data in order to understand an actual on-going Star Trek joke.

For more math/Star Trek links, go to space.com's treatment of the speech.

Monday, May 15, 2017

Pew Research Center's Methods 101 Video Series

Pew Research Center is an excellent source for data to use in statistics and research methods classes. I have blogged about them before (look under the Label pew-pew!) and I'm excited to share that Pew is starting up a series of videos dedicated to research methods. The new series will be called Methods 101.

The first describes sampling techniques in which weighing is used to adjust imperfect samples as to better mimic the underlying population. I like that this is a short video that focuses on one specific aspect of polling. I hope that they continue this trend of creating very specific videos covering specific topics.



Looking for more videos? Check out Pew's YouTube Channel. Also, I have a video tag for this blog.

Monday, May 8, 2017

Daniel's "Most timeless songs of all time"

This article, written by Matt Daniels for The Pudding, allows you to play around with a whole bunch of Spotify user data in order to generate visualizations of song popularity over time. You can generate custom visualizations using the very interactive sections on this website. For instance, there is a special visualization that allows you to finally quantify the Biggie/Tupac Rivalry.



So, data and pop culture are my two favorite things. I could play with these different interactive pieces all day long. But there are also some specific ways you could use this in class.

1) Generate unique descriptive data for different musicians and then ask you students to create visualizations using the software of your choosing. Below, I've queried Dixie Chicks play data. Students could enter their own favorite artist. Note: They data only runs through 2005.



2) Sampling errors: Here is a description of the methodology used for this data:


Is this representative of all data? What does he mean by "normalize the data" as a way to correct the data? Where could we collect data as to have a more representative sampling? Would Sirus skew older? What about iTunes?

3) Using data mining/archival data to generate insights into research questions.

Here, the question explored in this article is, "What is the difference between a flash in the pan song versus a song for the ages?".


Here, data from 2013 hits has been tracked. And it founds that the post-hit plateau is a good indicator of music that will have longer staying power. Here, event though Daft Punk's Get Lucky peaked much higher than Onerepublic's Counting Stars, Counting Starts has a higher plateau. Also, note that with this interactive piece, students could select any number of songs to compare.

Monday, May 1, 2017

"Student life summarized using graphs" video

I found this video at the Student Problems Page on Facebook. I don't know who to attribute it to, but it was probably a smart, sarcastic Intro Stats student.

Monday, April 24, 2017

NYT's "You Draw It" series

As I've discussed in this space before, I think that it is just as important to show our students how to use statistics in real life as it is to show our students how to conduct an ANOVA.

The "You Draw It" series from the New York Times provides an interactive, personalized example of using data to prove a point and challenge assumptions. Essentially, this series asks you to predict data trends for various social issues. Then it shows you how the data actually looks. So far, there are three of these features: 1) one that challenges assumptions about Obama's performance as president, 2) one that illustrates the impact of SES on college attendance, and 3) one that illustrates just how bad the opiod crisis has become in our country.

Obama Legacy Data

This "You Draw It" asks you to predict Obama's performance on a number of measures of success. Below, the dotted yellow line represents my estimate of national debt under Obama. The blue line shows true national debt under Obama. Note: With this tool, you trace your trend line on the graph, press a button, and then the actual data pops up, as well as discussion about the actual data.

We can use this data to see how political affiliation influences assumptions about the Obama presidency. This one can be used both ways: Right-leaning users may assume the worse while left-leaning users assume the best.


How Family Income Affects Children's College Chances

This example uses data to touch on a social justice issue: Whether or not a college education is really accessible to everyone. After you enter your estimate and see the real data, the website returns normative data about performance on the task and how you compare to other users. Below, the dotted line represents the actual data, and my guess was the solid line.

I think this would be useful in a class on poverty and as an example of a linear relationship.



Drug Overdose Epidemic

This example would be good for a clinical psychology, addiction, criminal justice, or public health class. It asks the user to guess number of deaths due to car accident deaths, gun deaths, and HIV deaths in the US. Finally, it asks you to estimate deaths due to drug overdoses. Which have sky rocketed in the last 20 years (see below).

Then it contrasts drug overdose deaths with car accidents, guns, and HIV. This example may also be useful for social psychology, as it hints at the availability heuristic.


How to use in class:
1) Non-statisticians using statistics to tell a story.
2) Using clever visualization to tell a story.
3) The interactive piece here really forces you to connect to the data and be proven right or wrong.

Monday, April 17, 2017

Sense about Science USA: Statistics training for journalists

In my Honors Statistics class, we have days devoted to discussing thorny issues surround statistics. One of these days is dedicated to the disconnect between science and science reporting in popular media.

I have blogged about this issue before and use many of these blog posts to guide this discussion: This video by John Oliver is hilarious and touches on p-hacking in addition to more obvious problems in science reporting, this story from NPR demonstrates what happens when a university's PR department does a poor job of interpreting research results. The Chronicle covered this issue, using the example of mis-shared research claiming that smelling farts can cure cancer (a student favorite), and this piece describes a hoax that one "researcher" pulled in order to demonstrate how quickly the media will pick up and disseminate bad-but-pleasing research to the masses.

When my students and I discuss this, we usually try to brain storm about ways to fix this problem. Proposed solutions: Public shaming of bad journalists, better editing of news stories before they are published, a prestigious award system for accurate science writing. And another idea my students usually arrive upon? Better training for journalists.

http://www.senseaboutscienceusa.org/

So, you can imagine how pleased I was to discover that such classes already exist via Sense about Science USA.





Their mission:



They support this mission in a few different ways. They advocate for registering all medical trials conducted on humans. They are training scientists to more effectively communicate their findings to the public. And, apropos of this blog, they are also training journalists to better understand statistics AND offer one-on-one consulting to journalists trying to understand data.

Here is their description of why it is important to better train journalists.

http://www.senseaboutscienceusa.org/stats_workshops/

How to use in class:

1) Instead of just showing students the problems associated with poor science writing, let's show them a possible solution as well.
2) Statistics isn't just for statisticians, statistics are for anyone who wants to better understand policy issues, emerging research, and evidence-based practices in their field.
3) Show your students some examples of poor science writing. Have them develop a brief presentation that would address the most common statistical mistakes made by science writers.

Monday, April 10, 2017

Reddit's data_irl subreddit

You guys, there is a new subreddit just for sharing silly stats memes. It is called r/data_irl/.

The origin story is pretty amusing.

I have blogged about the subreddit r/dataisbeautiful previously. The point of this sub is to share useful and interesting data visualizations. The sub has a hard and fast rule about only posting original content or well-cited, serious content. It is a great sub.

But it leaves something to be desired. That something is my deep desire to see stats jokes and memes.

On April Fool's Day this year, they got rid of their strict posting rules for a day and the dataisbeautiful crowd provided lots of hilarious stats jokes, like these two I posted on Twitter:



The response was so strong, because there are so many of people that love stats memes, that a new sub was started, data_irl JUST TO SHARE SILL STATS GRAPHICS. It feels like coming home to my people. 

Monday, April 3, 2017

Day's Edge Production's "The Snow Guardian"

A pretty video featuring Billy Barr, a gentleman that has been recording weather day in his corner of Gothic, Colorado for the last 40 years. 

Billy Barr
This brief video highlights his work. And his data provides evidence of climate change. I like this video because it shows how ANYONE can be a statistician, as long as...

They use consistent data collection tools...

They are fastidious in their data entry techniques...



They are passionate about their research. Who wouldn't be passionate about Colorado?


Monday, March 27, 2017

Shameless Self Promotion: I wrote a chapter in a book about Open Educational Resources!

Let's make the academy better for science and better for our students, and let's make it better for free.

Want to learn how? I recommend a Open: The Philosophy and Practices that are Revolutionizing Education and Science, edited by Rajiv Jhangiani and Robert Biswas-Diener.



In the spirit of open resources, it is totally free.

In the spirit of open pedagogy and quick sharing of teaching ideas, I wrote a chapter for the book about how I've gone about sustaining a blog dedicated to teaching for the last four years. The basic message of my chapter: I blog about teaching, and you can, too!  Here are all the chapters from the book:


Johnson's "The reasons we don’t study gun violence the same way we study infections"

This article from The Washington Post is a summary of an article from the Journal of the American Medical Association. Both are simple, short articles that demonstrates how to use statistics to make an argument. Here, that argument is made via regression in order to demonstrate the paucity of funding and publications for research studying gun related deaths.

What did the researchers do? Regression. A regression line was generated in order to predict how much money is spent studying common causes of death. We see that deaths by fire arms aren't receiving proportional funding relative to the deaths they cause. See the graph below.

https://img.washingtonpost.com/wp-apps/imrs.php?src=https://img.washingtonpost.com/blogs/wonkblog/files/2016/12/Capture7.png&w=1484

How to use in class:

1) How is funding meted out by our government in order to better understand problems that plague our country? Well, it isn't being given to researchers studying gun violence because of the Dickey Amendment. I grew up in a very hunting friendly/gun friendly part of Pennsylvania. I've been to the shooting range. And it upsets me that we can't better understand and study best practices for safe gun ownership.

2) Another issue: We don't talk about suicide enough. Half of the gun deaths were suicides.

3) There seems to be under-funding of possible accidents, as opposed to diseases, that cause death (shooting, motor vehicle, falls, and asphyxia). Why might this be?

4) The above image demonstrates correlation/linear relationships as well as gun violence as an influential observation.

5) Regression, y'all. 

The WP article states, 

"If public health issues were funded based on their death toll, gun violence injuries would have been expected to receive about $1.4 billion in federal research funding over about a decade — compared with the $22 million that it actually got, the study found." 

They predicted Y (research funding) based on X (death toll) and found a discrepancy, and the discrepancy is used to make an argument about the funding short fall. If you go to the JAMA article, they describe the research article publication shortfall as well. According to that regression equation, there should be over 38K articles published about gun deaths. Instead, there are 1,738.


Monday, March 20, 2017

Retracton Watch's "Study linking vaccines to autism pulled following heavy criticism"

This example from Retraction Watch illustrates how NOT to do research. It is a study that was accepted and retracted from Frontiers in Public Health. It purported to find a link between childhood vaccination and a variety of childhood illnesses. This would be a good case study for Research Methods. In particular, this example illustrates:

1) Retraction of scientific studies
2) The problems with self-report surveys
3) Sampling and trying to generalized from a biased samples
4) What constitutes a small sample size depending on the research you are conducting
5) Conflict of interest

This study, since retracted, studied unvaccinated, partially vaccinated, and fully vaccinated children.

And the study found "Vaccinated children were significantly less likely than the unvaccinated to have been diagnosed with chickenpox and pertussis, but significantly more likely to have been diagnosed with pneumonia, otitis media, allergies and NDDs (defined as Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and/or a learning disability)."


But the study surveyed moms who home school their children. A group that is historically, but not exclusively, anti-vaccination. From the study:


"Homeschool organizations in four states (Florida, Louisiana, Mississippi, and Oregon) were asked to forward an email to their members, requesting mothers to complete an anonymous online questionnaire on the vaccination status and health outcomes of their biological children ages 6 to 12."


And money to conduct this study was crowd sourced via a pro Autism:Vaccination website. There are other problems with this study, as noticed by Retraction Watch as well as the summary piece below. The sample sized used was relatively small for this kind of research, no one verified the various diagnoses via medical records, etc.


So, there is plenty for your students to consider with this study. Maybe you could create a methodology for this study that would fix the current, flawed methodology. Or you could just give your student the summary of the study and ask them to find the problems.

Further treatment (and deconstruction) of the study can be found here.

Monday, March 13, 2017

I've tracked all my son's first words since birth [OC]

Reddit user jonjiv conducted a case study in human language development. He carefully monitored his son's speaking ability, and here is what he found:
https://imgur.com/gallery/KwZ6C#qLwsn9S...go to this link for a clearer picture of the chart!



How to use in class:
1) Good for Developmental Psychology. Look at that naming explosion!
2) Good to demonstrate how nerdy data collection can happen in our own lives.
3) Within versus between subject design. Instead of sampling separate 10, 11, 12, etc. month old children, we have real-time data collected from one child. AND this isn't retrospective data, either.
4) Jonjiv even briefly describes his "research methodology" in the original post. The word had to be used in a contextually appropriate manner AND observed by both him and his wife (inter-rater reliability!). He also stored his data in a Google sheet because of convenience/ease of tracking via cell phone.

Monday, March 6, 2017

Annenberg Learner's "Against All Odds"

Holy smokes. How am I just learning about this amazing resource (thanks, Amy Hogan, for the lead) now?

The folks over at Annenberg, famous for Zimbardo's Discovering Psychology series, also have an amazing video collection about statistics, called "Against All Odds".

Each video couches a statistical lesson in a story.

1) In addition to the videos, there are student and faculty guides to go along with every video/chapter. I think that using these guides, and instructor could go textbook free.
2) The topics listed approximate an Introduction to Statistics course.

https://www.learner.org/courses/againstallodds/guides/faculty.html

Monday, February 27, 2017

rStats Institute's "Guinness, Gossett, Student, and t Tests"

This is a nice video for introducing t-tests AND finally getting the story straight regarding William Gossett, Guinness Brewery, and why Gossett published under the famous Student pseudonym. What did I learn? Apparently, Gossett DID have Guinness' blessings to publish. Also, this story demonstrates statisticians working in Quality Assurance as the original t-tests were designed to determine the consistency in the hops used in the brewing process. Those jobs are still available in industry today.


Credit goes to the RStats Institute at Missouri State University. This group has created a bunch of other tutorial videos for statistics as well.

Monday, February 20, 2017

Raff's "How to read and understand a scientific paper: a guide for non-scientists"

Jennifer Raff is a geneticist, professor, and enthusiastic blogger. She created a useful guide for how non-scietists (like our students) can best approach and make sense of research articles.

The original aritcle is very detailed and explains how to go about making sense of experts. Personally, I appreciate that this guide is born out of trying to debate non-scientists about research. She wants everyone to benefit from science and be able to make informed decisions based upon research. I think that is great.

In the classroom, I think this would be a good way to introduce your undergraduates to research articles.

I especially appreciated this summary of her steps (see below). This could be turned into a worksheet with ease. Note: I still think your students should chew on the full article before they would be ready to answer these eleven questions.

http://blogs.lse.ac.uk/impactofsocialsciences/2016/05/09/how-to-read-and-understand-a-scientific-paper-a-guide-for-non-scientists/#author


If you are looking for a more psychology-specific guide for learning how to read research, I also love this perennially popular piece by Jordan and Zanna. It may be entitled "How to read an article in social psychology", but it is a good guide to reading research in any psychology discipline. I teach two research-reading heavy psychology electives (Positive and Motivation and Emotion) and I assign this article, and a quiz about this article, during the first week of both classes.

Anyone else have any other suggestions for guides to reading reserach? Lemme know and I'll add them to this post.

Monday, February 13, 2017

NY Magazine's "Finally, Here’s the Truth About Double Dipping"


Yes, it includes the Seinfeld clip about George double dipping.


The video provides a brief example of how to go about testing a research hypothesis by operationalizing a hypothesis, collecting, and analyzing data. Here, the abstract question is about how dirty it is to double dip. And they operationalized this question:


Research design: The researchers used a design that, conceptually, demonstrates ANOVA logic (the original article contains an ANOVA, the video itself makes no mention of ANOVA). The factor is "Dips" and there are three levels of the factor:




Before they double dipped, they took a base-line bacterial reading of each dip. Good science, that.
They display the findings in table form (again, no actual ANOVA). 

I am totally horrified by this salsa data.



However...the acidity of the salsa seems to help out in terms of killing bacteria after two hours. So, dig into that bowl of salsa two hours after your last guests go home? Still ew.

Because of the re-testing, using 1) baseline, 2) Time 1, and 3) Time 2, this now becomes a good example of a repeated measures ANOVA.


How to use in class:

1) How do we go from a research question to research?
2) ANOVA
3) Repeated measure design

Monday, February 6, 2017

Refutations to Anti-Vaccine Memes' Vaccination rates vs. infection rates


Refutation to Anti-vaccination Memes came up with this nice illustration to explain why anti-vaxxers shouldn't claim a "win" just because more vaccinated people than un-vaccinated people get sick during an outbreak.

I feel that this example has a bit more credence if paired with actual immunization rate/infection rate data. For instance, a case when an outbreak has occurred and the majority of infected are immunized, but there were still some un-immunized individuals.

To further this case, yes, most people in America are immunized. Here is an example of a an outbreak that has been linked to un-vaccinated folks.

How to use in class:
-Base rate fallacy (which DOES matter when making an argument with descriptive stats!)
-Relative v. absolute risk.
-Making sense of and contextualizing descriptive statistics.

Thursday, February 2, 2017

Southern Poverty Law Center's Hate Map

The Southern Poverty Law Center has used mapping software in order to illustrate the location of different hate groups in the US.


How to use in class:

I think this demonstrates how good old descriptive data collection plays a valuable roll in law enforcement, social justice, etc.

I think this demonstrates why well-visualized data may be a more compelling way of sharing information than data in tables.

Another way to use this is for your students to create a methods section based upon the data collection information provided on the website:



You can make the data more personalized for your class by digging down to state-wide data.



In addition to the maps, the website includes various other descriptive data quantifying different hate groups in the US.

I used this in class along with  other examples of how data can be mixed with maps in order to provide information on regions/states.

This could also be used in a Social Psychology class in order to illustrate the presence of organized, deliberate prejudice in our society.