Everyday Research Methods

Dog people live longer

The study found an association between dog ownership and longevity. Which third variables were controlled for? Which third variables might still be responsible for the relationship? Photo: Shutterstock

I can't resist blogging about research on dogs. Here's a story on NPR that summarized several studies that have tested the correlation between longevity and dog ownership. The journalist introduces the topic this way: 

Dogs and humans that interact with one another get a jolt of oxytocin, the so-called "cuddle hormone." And, if you get to look at dogs and hug them every day, you just might live longer than people who don't have to clean animal hair off their clothes, according to a pair of studies out this month.

Here is a summary of one of the studies:

[researchers] looked over a 12-year period at dog owners in Sweden who have cardiovascular disease. They adjusted for the kinds of things we know affect cardiovascular health — age, demographics, socioeconomic status, marital status, number of children at home — and even after adjusting for all of that, they found a benefit of dog ownership [on longevity].

....they followed over 100,000 people and found a 21% reduction in deaths from any cause.

The study above used multiple regression. 

a) What was the dependent (criterion) variable in this study?  What were the predictor variables? 

b) Sketch a little regression table with results that would be consistent with what is mentioned above. Put the criterion variable at the top, and list the predictor variables. Which betas would be significant here? 

c) The researchers controlled for age, demographics, SES, marital status, and children--but regression doesn't control for all third variables. What else could be associated with both dog ownership and longevity? 

d) They mention a "21% reduction in deaths from any cause."  Which of the four big validities is this concerning? 

One of the experts who was interviewed for this story said:

In particular, I found it very convincing and striking that the benefits seems to be larger among individuals who live alone compared to multiperson households. That suggests the companionship of a dog is possibly very important to their heart health.

e) This is a moderator! Identify the variables in this relationship and complete this sentence: ____ moderates the relationship between ____ and _____.

 

In part of the article, the journalist presents quotes from an expert, who seems to equivocate on the causality issue:

"When you look at the big picture and look at all the evidence around dog ownership and cardiovascular health, it's pretty clear the signal is real and likely causal," he says.

In other words, he's convinced getting a dog improves your health — especially for those with heart disease.

The research is not definitive, though, Kazi adds. "They're not randomized trials, the gold standard for what we would do to evaluate a new drug," he says.

f) It is frustrating to read that the expert believes the "signal is real and likely causal" and then admits that "it's not definitive." You can set this expert straight. Why can't this correlational study support causation?  Apply covariance, temporal precedence, and internal validity. When you address internal validity, check out answer c) above!

 

 

Suggested answers

a) The criterion variable is longevity (being alive at the end of the study). The predictor variables are dog ownership, age, demographics, SES, marital status, and children living at home. 

b) The table might look like this:

DV: Longevity (or being alive after 12 years)

IV                            Beta (sig)
Dog ownership         .12*
Age                            -.11
SES                              .13*
Married                        .08*
Children at home        .07*

d) Current health and mobility might be associated with both dog ownership and longevity. After all, you  may need to have a certain level of health and mobility to own a dog, and healthier folks are more likely to be alive at the end of the study. 

e)  Living status (being alone or married) moderates the relationship between dog ownership and longevity.  

f) There is covariance--there is a link between owning a dog and living longer. 

There is temporal precedence-- the study took place over several years and dog ownership was measured first, and death rates later. 

There is not full internal validity. They controlled for age, demographics, SES, marital status, and children living at home, so they can rule those out. But they did not control for everything. One possibility is that current health and mobility might be associated with both--after all, you  may need to be at a certain level of health and mobility to own a dog, and those folks are more likely to be alive at the end of the study. 

 

 

 

Racial stereotypes behind how we explain school shootings

Participants and newspapers were more likely to blame video games when a school shooter was white than when he was black. Photo: Richard Levine / Alamy Stock Photo

What causes mass shootings? Of course, we have no experimental evidence on this question. Correlational evidence (both within the U.S. and evidence that compares the U.S. to other countries) is consistent with the argument that gun availability is a factor. 

However, this post isn't about what really causes school shootings. It's about what people think causes them. And it turns out that the race of a school shooter influences what people believe about the shooter. 

After some school shootings (a subset of mass shootings), people have sometimes looked for answers not in the availability of guns, but in video games. Now, there is actually no evidence linking video games to mass shootings. However, this ABC news story talks about when people are more likely to think that video games are responsible: 

There's little evidence showing a relationship between violent video games and school shootings, but video games are often blamed for overall violence in our communities. A recent study published in the Psychology of Popular Media Culture set out to find if we look for an external explanation for violence by whites more often than we do for African Americans.

"We try harder to make excuses for white perpetrators," said investigator Dr. James Ivory, a professor in the Department of Communications at Virginia Tech, in an interview with ABC News.

The first study gave people a mock news story about a school shooting. When asked what caused the shooting, people were more likely to blame video games if the shooter was white than if the shooter was black.

It turns out that in the original study (linked here;  if it's paywalled you may be able to access via your university library), participants read the mock news story and were randomly assigned to one of two photos of the perpetrator--a mugshot showing either a Black or a White man. 

a)  There were two variables in this study. Name each one. For each, was it manipulated or measured?

b) What kind of study is this, correlational or experimental?

c) Sketch a graph of the results of this study. What will you put on the y-axis? 

d) In the study above, what role does random assignment play? Why did the researchers randomly assign people to see either a Black or White perpetrator?  

 

Here is how ABC news described the second study:

The second study looked at thousands of news articles -- video games were mentioned more often in stories of white shooters [compared to black shooters]. 

e) What are the variables in the study above? Name each one; for each, was it manipulated or measured?

f) What kind of study is this, correlational or experimental?

g) Sketch a graph of the results of this study.

h) In the study above, the researchers could not randomly assign news article authors to write about a black vs. a white perpetrator. How does that change the impact of this study? 

i) Which of the two studies do you think provides the strongest evidence that race influences people's willingness to think video games are a cause of the shooting?

 

Suggested answers

a) One variable is race of the perpetrator (levels are Black or White), this was manipulated. The other variable is "likelihood to blame video games". This was measured.

b) This was an experiment because one variable was manipulated. 

d) Random assignment contributes to the study's internal validity. They can be sure that the same kinds of participants were in each group (reading about Black vs. White perpetrators). Therefore, people who play a lot of video games themselves were equally likely to be in the two groups. You might be interested to know that in the original study, the researchers also pretested the Black and White photos they used to be sure that the faces differed on race but not on perceived attractiveness or dangerousness. This pretesting also contributed to the experiment's internal validity.

e) The first variable was race of the perpetrator in the article (Black or White), which was measured. The second variable was whether or not the article mentioned video games (yes or no).

f) This was a correlational study because all variables were measured. 

h) Because this study used naturally-occurring crimes, the researchers may have an internal validity problem. It is possible that the Black and White perpetrated shootings differed not just on race but also on number of victims, age of victims, geography, or some other variable. This introduces an alternative explanation for the result--perhaps people blamed video games for White perpetrated shootings because they had more victims, had older victims, and so on.

i) You might have selected the first study as the strongest because it was an experiment and allows us to make a causal inference. Or you might have selected the second study because it was conducted on "real world" stimuli such as news stories--therefore it appears to have good ecological validity or external validity. 

Thanks to Marianne Lloyd for this idea!

How many have Internet access? Question wording matters

CSP_rasstock/agefotostock

What percentage of Americans would you guess have access to the Internet?  How would you know?

When you consider whether people have computers at home, computers at work, smart phones, ortablets, you might predict that a large majority of Americans are able to go online. In fact, it's probably around 90%.  But, as with all frequency claims, the answer to the question hinges on two key things: Construct validity (How well do you measure "access the Internet?") and external validity, ("how well does your sample represent the population?").

In this article, Pew Research explains how they get different estimates depending on how they ask. They write, 

While our approach has evolved over time, the current measurement consists of two questions: “Do you use the internet or email, at least occasionally?” and “Do you access the internet on a cellphone, tablet or other mobile handheld device, at least occasionally?” In a January 2018 phone survey, 89% of U.S. adults said “yes” to at least one of these questions.

They compare their own questions to that of the U.S. Census Bureau's American Community Survey (ACS):

The ACS asks, “At this house, apartment, or mobile home – do you or any member of this household have access to the internet?”

Yes, by paying a cell phone company or Internet service provider
Yes, without paying a cell phone company or Internet service provider
No access to the Internet at this house, apartment, or mobile home. 

This question led to a similar estimate:

88% of American adults had either free or paid Internet access at home.

But what about this question, from the Bureau of Labor Statistics CPS data: 

That survey asks, “Does anyone in this household, including you, use the internet at home?” Based on this question alone, 81% of American adults live in households where someone uses the internet at home. The difference between the two figures can be at least partly attributed to the questions’ wording and scope....  And households can have internet access without anyone actually using it – for example, if it is provided for free by their community or included in a bundle with other services.

The CPS separately asks, “Does anyone in this household access the internet using a data plan for a cellphone, smartphone, tablet, mobile hotspot, or other device? This type of internet service is provided by a wireless carrier, and may be part of a package that also includes voice calls from a cellphone or smartphone.” If we include Americans who say yes to this question, the previous 81% figure rises to 83%.

And what about using the Internet somewhere other than home? 

The CPS also asks a battery of questions about whether anyone in the household uses the internet at work; at school; at a coffee shop or other business that offers internet access; while traveling between places; at a library, community center, park or other public place; at someone else’s home; or at some other location. (Each location is asked about separately.) If we once again expand the previous CPS estimate to include people who may not use the internet at home but do so somewhere else, the estimate ticks up to 85%.

Questions

a) What companies or organizations might be most interested in having an accurate answer to the question of how many Americans are online? 

b) Make a table where you have the wording of a question, followed by the estimate of the number of Americans on the Internet. There seem to be at least four different ways of asking. (You can see trends over time by scrolling down to the bottom of the Pew article.)

c) How important, in your view, are the different ways of asking the question? That is, some might say that all the estimates are virtually the same. But others might say that differences of 1 or 2% percentages matters. What do you think--are the differences in these estimates important or not? 

d) What subgroups of Americans would you estimate are least likely to access the Internet? 

 

 

Can Probiotics Actually Treat Obesity?

 

Should you be suspicious of a study conducted by a company that produces probiotics products? Photo: Science History Images / Alamy Stock Photo

Guest post by Dr. Dina Gohar, University of Michigan

 

Hippocrates, considered to be the Father of Medicine, reportedly said, “all disease begins in the gut. ” It took doctors centuries to start studying the connection between our gut microbiota and our health, which is now well-supported by research. We can improve the health and diversity of our gut microbiota with active strategies, such as exercising regularly, eating a healthy diet, and getting enough sleep. We might also be able to improve our gut microbiota by simply consuming live “good” bacteria and microorganisms called probiotics. Probiotics are now sold as capsules, pills, and powders, added to fruit juices, granola bars, and candy, and even put into cosmetics. Fermented foods, such as cultured yogurt, sauerkraut, and kombucha, contain them too. A cloud of hype and hope surrounds probiotics, which have been touted as a treatment (and means of prevention) for everything from intestinal disorders, mood and anxiety disorders, belly fat and obesity-related disorders. 

A recent article in the New York Times suggested that a probiotic of heat-killed bacteria can treat metabolic disorders like obesity. This suggestion was based on a study in Nature Medicine.The  New York Times journalist summarized the study as follows:

Researchers tested the bacterium, Akkermansia muciniphila, in 32 men and women who met the criteria for metabolic syndrome by having at least three of five conditions: high fasting blood sugar, high blood pressure, high triglycerides, low HDL (the “good” cholesterol) or excessive waist circumference.

…In a three-month trial, volunteers were randomized to one of three groups: daily tablets containing live bacteria, pasteurized bacteria [A. muciniphila] or a placebo. Compared with the placebo group, those who took pasteurized A. muciniphila had significantly improved insulin sensitivity and total cholesterol, and decreases in several blood markers of inflammation and liver dysfunction. They also had decreased body weight, fat mass and waist circumference, though those differences were not statistically significant. The live bacteria were largely ineffective.

1. This study was an experiment. What is the independent variable (IV) and what are its levels? What are the dependent variables (DV)? What do you think of the outcome measures chosen for this study?
   
   
2. How was the independent variable manipulated:  between or within groups? Which of the four basic experiments is it probably an example of: Pretest/postttest design? Posttest-only? Repeated measures? or Concurrent measures?
   
   
3. Can the results of this study be used to support the causal claim that “Taking pasteurized bacteria can improve people’s insulin sensitivity and total cholesterol?” Why or why not? (Apply the three criteria for causality.)
4. Look at the sample size of this study; do you think it’s a problem? Why or why not? Which of the four big validities does the sample size best fit into? 
5. Do the results above support the claim that probiotics like A. muciniphila can treat obesity? Why or why not? (Is internal validity relevant here? What about construct validity?) Can you conclude that probiotics can help you lose weight? Why or why not?
6. The New York Times article critically failed to mention that the researchers who conducted this study have a patent on the probiotic treatment and even started a company based on it. What do you think about this potential conflict of interest? 
7. Do you think it matters that this study’s somewhat promising findings were only with dead bacteria, whereas probiotics are generally defined as being live bacteria? Why or why not? (Which of the four big validities is being asked about in this question, if any?)
   
   

You might agree that the probiotic study described by this journalist has a few serious problems. For an example of probiotic research done right, check out this NPR news story on a current clinical trial examining if a specific probiotic could eliminate specific GI problems in children on antibiotics (courtesy of Dr. Jess Hartnett’s statistics blog).

Does this punctuation make me sound angry, or what.

Which of these two text messages seems the most sincere? Photo: Travis Carr

If you're an American young adult, you probably already follow certain unwritten rules about about punctuating text messages. FOR EXAMPLE, EMAILS AND TEXTS WRITTEN IN ALL CAPS ARE CONSIDERED A FORM OF SHOUTING. Text messages follow their own set of norms, and the punctuation we use can lead to different interpretations of the same words. Some linguistics researchers have studied the impact of different text-message punctuation. 

This brief article in Quartz summarizes the results of some of this research. The journalist introduced the research with this: 

Though periods can still signal the end of a sentence in a text message, many users will omit them (especially if the message is only one sentence long). This tendency now subtly influences how we interpret them.

First example

This study is an example of a 2x2 factorial design. Here's how the journalist summarized the study: 

In 2016, psychologist Danielle Gunraj tested how people perceived one-sentence text messages that used a period at the end of the sentence. Participants thought these text messages were more insincere than those that didn’t have a period. But when the researchers then tested the same messages in handwritten notes, they found that the use of a period didn’t influence how the messages were perceived.

a) What are the two independent variables (IVs) in this design? What are their levels?

b) What was the dependent variable? 

c) Do you think the IVs were manipulated as independent groups or within groups? 

d) Sketch a graph of the results of the study. Remember to put the DV on the y-axis. Put one IV on the x-axis and represent the other IV with two different lines or bar colors. 

 

Second example

For practice with a simple experiment (not a factorial), compare these two text messages. Which one do you think participants found to be more sincere?

JACKIE I AM SO SO SO SORRY! I thought you were behind us in the cab and then I saw you weren’t!!!!! I feel soooooooo bad! Catch another cab and ill pay for it for youuuuu

Jackie, I am so sorry. I thought you were behind us in the cab and then I saw you weren’t. I feel so bad! Catch another cab and I’ll pay for it for you.

e) These two messages were studied by linguist Deborah Tannen. What would have been the independent and dependent variables in the study that compared these two messages? How might you design the study--would a posttest only design be better, or a concurrent-measures design, and why? 

f) How might the results of this study change if they were conducted on an older sample compared to a younger one? 

Observing how people talk to their dogs

What's she saying to her dog? What category would her statement fall into?
Photo Credit: Shift Drive/Shutterstock

This entertaining article quotes several cute examples of how people talk to their dogs while they are out in public. The author, Alexandra Horowitz, is a psychologist who studies dog cognition. She has been recording the snippets of owner-dog conversation she hears as she goes throughout her day. (Most of those doing the talking are the owners.) She shares examples such as "You’re so cute and so smart. And worth money! I could marry you" and "You’ve got a lot to learn! A lot to learn!” and "Don’t even think about it.”

The main body of the article shares the author's categorization system. After jotting down dozens of dog-directed human comments, the author was able to classify them into categories. One category, for example, she titled Cheering Squad, for comments in which the owner is congratulating the dog's behavior. She dubbed another category Mom Commentary, described as, "Eyes on the dog, she sees everything. And she’s gotta talk about it." Another is Perfectly Implausible Instructions, in which owners direct the dog to do impossible things. And there are two other categories in the article: "We've Discussed This" and "Rhetorical Realm".

Read the article to find examples of each of these categories. The author's system provides a clear example of the observational research practices from Chapter 6. The author observed a large set of behaviors. Then she developed a categorization system in order to simplify what she observed.

1. This piece is meant to be entertaining. But if the author were to formally study how people talk to their dogs, the next step would involve coming up with clear operational definitions of each of the three categories. Take a look at the article and try your hand at developing operational definitions of Cheering Squad, Mom Commentary, and Perfectly Implausible Instructions. Write clear instructions that would enable a coder to decide which comments go in only one category (and not in the others).
   
   
2. Test out your operational definitions in the real world. Go out and find some people with dogs, and record some of your own snippets of conversation. Classify each snippet into the categories using your operational definitions, keeping in mind you'll probably need an "other" category. 
   
   
3. A good categorization system requires inter-rater reliability. How would you establish inter-rater reliability for your system? (Hint: You'll need to involve another rater who uses the same categories to categorize the same pieces of conversation.
   
   
4. Any researcher will tell you how difficult it is to develop a reliable coding system. What issues did you have when you tried this out yourself? 

Which Ice Cream Flavor Are You?

Many flavors, many personalities: How could we test whether an ice cream personality test is reliable and valid? Photo credit: Okanmetin/Getty Images

At the height of summer, we all start considering the same, timeless question: Can your personality can be accurately summarized by an ice cream flavor? This Buzzfeed poll asks you to answer a series of summer-activity-related questions and then delivers a verdict. Go take it yourself! I'll wait. 

The first time I took the poll, I answered the questions seriously and was told that I am characterized as "Coffee," along with this description: 

You’re a go-getter, a hard worker who doesn’t pause to reflect on what you have accomplished in the past because you are so focused on what you want to accomplish in the future. You seek to inspire the people around you and help to propel them to greater achievements. At the same time, you are approachable and kind rather than snotty and entitled. It’s basically the best of all worlds.

I sure felt good reading that personality description! The second time I took the quiz, I clicked on questions randomly haphazardly, just to see what other feedback there might be. This time I got "Chocolate", with this description:  

Mmm, you’ve got a truly rich and decadent side that you aren’t afraid to share. You make people feel good — really good. You’ve got a knack for excelling in social situations and know just how to be a crowd-pleaser. Keep doing your thing and living that luscious life.

Of course, nobody is going to mistake a Buzzfeed poll for a legitimate personality measure. But it does provide a fun way to consider issues raised in Chapters 5 and 6. Take the quiz yourself and then prepare these questions:

 

1.     What flavor ice cream did you get? What did the results say about you?

 

2.     Do you think this quiz is reliable? What kind of reliability is relevant for this measure? How would you empirically establish the quiz's reliability? Here's a hint: You'd start with a large sample of people and have them take this ice cream quiz, and then..... (what?)

3.  Now think about the measurement validity of this quiz. I'll narrow down the question for you here: Are people who score in the Coffee category actually more likely to be "hardworking," "inspiring," and "approachable," as the description suggests? How would you establish the criterion validity of this quiz? Here's a hint: You'd start with a large sample of people and have them take the ice cream quiz. You'd separate them into people who got the Coffee category and those who did not get Coffee. How should the Coffees be different from the non-coffees?

 

4.     What might we change about the quiz if we wanted to study ice cream preferences in a scientific way? 

 

5.  Read this description on the Barnum effect. Reflect on how the Barnum effect might be at play for people who take this Buzzfeed quiz. 

 

Many thanks for this idea to Josh Maxwell of the University of New Mexico!

 

Suggested answers to selected questions.

2. Test-retest reliability is the most relevant. To establish it, you'd have a large sample of people take the Buzzfeed quiz at one time point and record everyone's category (e.g., Coffee, Chocolate, etc.) Then you'd track down the same group of people again some time later (maybe 4 weeks later) and have them take the same quiz again. People who were scored Coffee at Time 1 should also score Coffee at Time 2, and people who were scored as Chocolate at Time 1 should also score as Chocolate at Time 2. (You'd compute the correlation between these two category assignments using Cohen's Kappa.)

3. To establish this criterion validity, you'd recruit a large sample of people and have them take the quiz, recording those who got Coffee and those who did not. Then you'd need to measure variables such as "hardworking" "inspiring" and "approachable." I'm not sure how to measure "inspiring" or "hardworking," but you could probably measure "approachable" using the Big Five trait of Agreeableness (here's one measure of that). If the Buzzfeed poll is valid, then a simple bar graph (and an independent groups t-test) should show that people who are assigned Coffee should score higher on agreeableness than people who were assigned some other flavor. 

 

 

Tilt your head down to appear more intimidating

According to the study, Captain Marvel's eyebrows have something to do with her dominant pose. Credit: Collection Christophel/Alamy Stock Photo

Captain Marvel looks tough. Perhaps one reason is the tilt of her head, which enhances the appearance of dominance, according to a series of studies that appears in Psychological Science. The studies were summarized by journalist Alice Walton for Forbes. 

Here are some excerpts from the journalist's description of the study:

...there’s a way to communicate dominance that doesn’t involve moving the facial muscles at all: just tilt your head downward slightly and maintain eye contact.

In one experiment, participants in the University of British Columbia study rated [several] avatars who were tilted upward 10 degrees, tilted downward 10 degrees, or level (neutral). Then they rated statements designed to gauge how dominant the person appeared, including “This person would enjoy having control over others,” and “This person would often try to get his way regardless of what people may want.”

Participants rated the downward-tilting heads significantly more dominant than either of the other two positions. When the experiment was repeated using photos of actual people rather than avatars, the same pattern was found.

a) The journalist labels this an experiment, which it really is. What is the independent variable? What are its levels? Is the IV manipulated as independent groups or within-groups? (By the way, the Forbes article shows photos of the three levels.)

b) What is the dependent variable in this study?

c) What's the experimental design: Posttest only? Pretest-posttest? Repeated measures? Concurrent measures? 

d) Sketch a bar graph of the results described here: "Participants rated the downward-tilting heads significantly more dominant than either of the other two positions."

e) What do you think of the construct validity of the dependent variable? Does this operationalization really get at "dominance?" Why or why not? What questions from Chapter 5 might you ask here? 

f) The journalist mentions that "the experiment was repeated using photos of actual people rather than avatars". It's an example of replication. Which type of replication study would you call it: direct replication? conceptual replication, or replication plus extension? (Chapter 14 defines these terms.)

g) By replicating the avatar study on actual people, which validity (or validities)  are these researchers enhancing? 

More Advanced Questions

After establishing the basic effect, the researchers conducted some additional studies to test the mechanism, or mediator. Read this paragraph (written by the journalist) to decide what the mechanism was, and think about how they tested it. 

In another experiment, the team found that the key to the effect was the eyes and eyebrows: when they showed participants faces with the eyes/eyebrows occluded, the tilted head/dominance effect disappeared. But then they showed participants pictures of just the eyes and brow portion of the face, the effect was present, which suggests that this part of the face is both necessary and sufficient—meaning it's not only required but is all that's necessary to elicit the effect.

The above argument suggests that the researchers' theory goes like this:

    Tilting head downward ---> changes in eyebrow position --> dominant appearance

h) To find evidence for their theory, the authors tested four conditions in a 2x2 factorial design. Downward tilt (vs. neutral), and face only vs. eyes/eyebrows only. You can see examples of the stimuli they used in Figure 4 of the original article in Psychological Science. Sketch the predictions of these two studies. Like this:

If changes in eyebrow position are important for tilted faces to appear dominant, then the results should show [sketch a result].

If changes in eyebrow position are NOT important for tilted faces to appear dominant, then the results should show [sketch a result].

 

Of head horns and cell phones

Did the study collect the right data to conclude that "screen time found to cause 'head horns'?"  Photo Credit: Nature Scientific Reports

Maybe you heard the story last week about how cell phones are making horns grow on people's heads. Take a minute to watch this  short video of the morning crew from ABC news discussing it. The link is captioned, "Screen time found to cause head horns" In this video, you'll notice that the journalist announces, "New research shows that constantly looking down at your screen could give you so-called 'head horns'" .

And here's another video that recycles the same material along with extra commentary under the title, "Tech neck causing skull horns"

There's so much to unpack about this story. If you read beyond the first journalists' coverage, you'll find some great analyses of this study and the journalism around it. Turns out, the study itself had problems, and the journalistic coverage didn't help. 

Take a moment to read one of these two critiques of the "head horns" moment: 

Smartphones aren't making millenials grow horns. Here's how to spot a bad study. or Hold the phone: Are you really going to grow a horn from slouching over your phone? 

Use the information in the two critical stories to research the following questions.

First, there are issues related to the "journal to journalism" cycle (Chapter 1), 

a) The original journalist changed the name of the protuberances to "horns." Every journalist thereafter copied this nickname in their own reporting.  What were they originally called? Why do you think the journalist changed their name?

b) In Chapter 1, you learn that journalists don't always pick the most important studies to summarize. Why do you think journalists picked this particular story? Do you think the study is important? 

Second, there are problems with the study itself, which many journalists did not detect--at least at first.

c) One criticism with the study concerns its sample. What are the criticisms raised about this study's sample of 1200 x-rays?

d) Another criticism has to do with the researcher's potential conflicts of interest (CoIs). What were these, and why are CoIs a potential problem in scientific research and publication? 

e) Another criticism has to do with the proposed link between cell phone use and the "horns."  In order to establish a link between having these protuberances and screen time, the scientists would have had to measure both variables and compute the correlation. Did the researchers do this? 

f) Finally, many of the news headlines used causal verbs ("Screen time found to cause head horns"). Even the scientists overclaimed here when they were interviewed by the press. What kind of study would a researcher have to do in order to establish such a causal link? 

g) Are there any additional problems you discovered?

The news cycle around the head horns study illustrates the benefit of cross-reading and triangulating. If you come across a journalist's story that's just too weird to be true, read about the same topic on other sources, or on snopes.com. 

 

Did the show "Thirteen Reasons Why" cause an increase in suicides?

The study used a quasi-experimental design.  Credit: Beth Dubber/Netflix/Kobal/Shutterstock

Update 7/19/2019: Netflix has decided to remove the scene depicting the main character's actual suicide, perhaps in response to data from this and other studies. Here's one story on the change. 

The Netflix series "Thirteen reasons why" tells the story of a teenage girl who commits suicide, leaving behind audio recordings about people who contributed to her decision. When the series was released in April, 2017, mental health and school professionals became concerned about the possibility that young viewers of the show would experience increased risk of suicide themselves. The phenomenon of "suicide contagion" occurs when a publicized suicide leads to other suicide attempts. Young people may be particularly susceptible to its effects. Suicide is the third leading cause of death among American teenagers (ages 12 to 19, according to the CDC). 

This Mashable journalist covers an empirical journal article published in JAMA Psychiatry in 2019.  The Mashable story reports: 

A new study, conducted by different authors, came to a similar conclusion: In the three months after 13 Reasons Why launched, the youth suicide rate for 10- to 19-year-olds rose unexpectedly by 13 percent. Based on historical trends dating back to 1999, 94 more American adolescents and teens died by suicide during the three-month period than otherwise expected. Similarly, the study published last month looked at suicide deaths in the nine months following the show's launch and found an additional 195 fatalities. 

Students and instructors will find it useful to consult the publicly-available JAMA Psychiatry article that contains graphs of this study. Specifically, the solid orange lines in Figure 2 show the suicide rates for 24 months of time: 15 months before the show debuted, and about 9 months after. 

a) This study used a quasi-experimental design. Which of the designs from Chapter 13 was it? (Non-equivalent control group posttest only? Non-equivalent control group pretest-posttest? Interrupted time series design? Non-equivalent control group posttest-only design?)

b) In the quote above, the journalist reported that an 13 percent increase in suicide equals an additional 94 adolescent teen deaths. This figure captures the effect size of the study's result. What do you think of this effect size? Would you call it small or large? 

The empirical journal article also documents the publicity the show received via social media. Here's how the journalist describes the evidence: 

Niederkrotenthaler and his co-authors looked at potential exposure to the show between April 1 to June 30, 2017, when the first season of 13 Reasons Why had just debuted and was most popular on Twitter and Instagram. Using social media data as a proxy for understanding peak interest in the show, the researchers suggest the show's portrayal of suicide created a 'contagion effect' The phenomenon can happen when media coverage of suicide leads to suicide clusters. 

Students and instructors can consult the empirical article's Figure 1 to see the results of the social media analysis. 

b) Why did the authors of the JAMA article include the social media piece? How does this evidence help them make their argument? 

The researchers cannot establish for certain that viewing the show caused the increase in suicide. Here's the Mashable journalist: 

While the researchers couldn't determine whether the children who took their own lives watched the series, an accompanying editorial written by researchers at Harvard and Stanford argues the study provides "strong evidence" that the Netflix show may have led to that increase. 

"One cannot draw definitive causal conclusions from such data, but that no similar increase was seen in suicide rates other than for the age group to which the media portrayal pertained provides some further compelling evidence that the excess suicides may indeed have been owing to the series," the editorial's authors wrote. 

c) The quote above adds an additional detail about the study. The researchers not only tracked the suicide rates of people aged 10-19; they also tracked suicide rates of people in the 20-29 and 30+ age group. The researchers found an increase in suicide after April 2017 only for the teenage group. How does this pattern of results support the argument that the show led to increased teen suicide? 

If you want to talk to someone or are experiencing suicidal thoughts, text the Crisis Text Line at 741-741 or call the National Suicide Prevention Lifeline at 1-800-273-8255. Here is a list of international resources.