Everyday Research Methods

Claim: Using smartphones on the toilet increases hemorrhoid risk

The study found that people who use their smartphone on the toilet are more likely to have hemorrhoids. How strong is their evidence?   Photo: Svitlana Hulko/Shutterstock

Is there any relationship between using your smartphone on the toilet and your hemorrhoid risk? Let's get into it. 

 

A study on this topic did find an association. A summary was first  presented by Medscape.com (a science-news outlet) with the headline, "Using smartphones on the toilet increases hemorrhoid risk".  Importantly, the original research has not yet been peer reviewed or published in a journal.  It was presented at a scientific conference called "Digestive Diseases Week 2025". 

 

One news outlet summarized the study and amped up the effect size with this language: "Using your phone on the toilet may dramatically increase risk of hemorrhoids."

 

And a substack post by a physician stuck on some causal language: "smartphone use while on toilet triggers hemorrhoids"

 

Here are some details from the Medscape summary:

• Researchers conducted a cross-sectional survey in adult patients undergoing screening colonoscopy.
• Survey questions were designed to assess smartphone habits while using the toilet; responses to Rome IV questionnaires for functional gastrointestinal disorders in adults; and other behaviors such as straining, fiber intake, and physical activity levels.
• The presence of hemorrhoids was assessed through direct visualization as documented in endoscopic reports.

a) Based on the description above, name at least four variables included in the study. Speculate on what the levels of each variable might be. 

b) What does it mean when they say it was a "cross-sectional survey"?

 

 

Next, here are some results as described by Medscape.com. The first results are frequency claims: 

• Among the 125 participants, 43% had hemorrhoids visualized on colonoscopy.
• Overall, 66% of respondents used smartphones while on the toilet; 93% of those used a smartphone on the toilet at least one to two times per week or more, and more than half (55.4%) used it most of the time.

c) Check the sample in the study. Does it allow us to make any inferences about the smartphone toilet habits of the population of American adults? (That is, can we generalize?) Why or why not? 

Next, here's the key association: 

• Smartphone use on the toilet was associated with a 46% increased risk for hemorrhoids (P = .044) after adjustment for age, sex, body mass index, exercise activity, and fiber intake.

d) If you've worked with Chapter 9, you'll recognize this as a multiple regression analysis. It's a little different from the examples in the book because it is predicting a binary outcome (having hemorrhoids or not). But you should still be able to identify the DV (or criterion variable) and the IVs (the predictor variables. What are they?

 

e) Does this association support the causal claim by author who wrote, ""smartphone use while on toilet triggers hemorrhoids"?   The study's result establishes covariance (why?), but does the study's method establish temporal precedence? Does it rule out all possible internal validity threats? 

 

Finally, Medscape.com reported that for the key association (the relationship between phone use and hemorrhoids), the p value was .044. A p value that is so close to the usual cutoff of 0.05 can be a sign that the results might have been (unintentionally) p-hacked. Here's the p-hacking definition from the textbook :

 

"Researchers can analyze their study’s results in a wide variety of ways. They might remove different outliers from the data, compute scores several different ways, or run a few different types of statistics. This exploratory practice has been dubbed p-hacking (Simmons et al., 2011), in part because the goal is to find a p value of just under .05, the traditional value for significance testing (corresponding to a 95% CI that does not include zero)"(Ch 14, p. 442)

 

Because this study's p-value is .044, it raises suspicions of p-hacking. Here's why. When researchers are analyzing data, they may try various statistical steps--maybe they add new covariates one at a time to their regression analysis, or omit certain outliers here and there, checking the p-value after each step. They might stop their explorations when the p-value for their key association finally falls below .05. Because the study's p-value is so close to .05, it's possible that's what happened here--though we can't be sure, just based on this journalist's summary. 

 

f) p-hacking isn't usually intentional. What steps can researchers take to avoid it? 

  

Thanks to Stephen Chew for sending this example my way (guess where I read it!)

 

Suggested Answers

a) Variables in the study:

• presence of hemorrhoids (assessed during colonoscopy--levels were absent or present)
• smartphone habits while using the toilet (levels might have been never, sometimes, always).
• level of gastrointestinal disorders (levels might have been none, one, more than one).
• degree of straining (levels might have been never, sometimes, always).
• fiber intake (levels might have been number of high-fiber foods per day, from 0 to 10).
• physical activity (levels might have been 0 to more than 100 minutes per week)

    All variables were measured.

b) This means that they measured all variables at the same time. They didn't, for example, measure smartphone toilet habits first, and then diagnose hemorrhoids weeks or months later. 

 

c) We don't know if we can generalize because we don't know how they got their sample. I would assume that since the sample came from adults who were undergoing colonoscopy, they were not randomly selected. (Note: you should not mention sample size here, because sample size doesn't help you assess external validity.)

 

d) The criterion variable(DV) was presence or absence of hemorrhoids. The predictor variables(IVs) were age, sex, BMI, exercise activity, and fiber intake. 

 

e) The study's result establishes covariance because the results show a relationship between smartphone use and the presence of hemorrhoids. The study can't establish temporal precedence because it was cross-sectional--all variables were measured at the same time, so we don't know for sure that phone use came first and hemorrhoids emerged later. The study can't establish internal validity because it is correlational. While the study can rule out the variables they statistically controlled for (i.e., age, sex, body mass index, exercise activity, and fiber intake), it's possible there is another possible variable, maybe bowel movement frequency, that is associated both with more smartphone use and with more hemorrhoids. 

f) the best way to avoid p-hacking is to preregister the data analysis plan before data are collected or analyzed. If that is not possible, then it's best to be completely transparent, reporting every step in the data analysis process, including all the steps that did not result in a p-value under .05. 

Placebo controls: When are they necessary? When are they ethical?

Many public health officials raise ethical concerns about requiring placebo
controlled tests of slight revisions to existing vaccines.
Photo: PeopleImages.com - Yuri A/Shutterstock

Placebo effects have been in the news lately because the U.S. Secretary of Health and Human Services (HHS), Robert F. Kennedy Jr., announced a new policy requiring placebo controlled studies for vaccines before they can be approved. Several online sources have covered the story. Some writers criticize the new policy (e.g., Paul Offit's Substack and this entry from factcheck.org) and some writers praise the decision. 

I'll pull quotes from  the BBC's coverage, since the BBC is considered a middle-of-the-road news source (according to allsides.org).

Here's the new policy: 

The top US health department plans to require placebo testing for all vaccines in an effort to offer "straightforward" public health information, but experts say such testing could limit availability and raise ethical concerns.

In a statement first given to the Washington Post, the Department of Health and Human Services (HHS) said this week, "All new vaccines will undergo safety testing in placebo-controlled trials prior to licensure — a radical departure from past practices".

On the face of it, it makes sense that any new drug or vaccine should be compared to a placebo--after all, it's the best way to determine if it is effective and safe.

For example, in 1955 when the polio vaccine was first developed, scientists randomly assigned children to receive the true vaccine and others to receive a placebo shot. They found that children who received the real polio vaccine were less likely to become infected with, paralyzed by, or die from polio (it's fun to read the original 1955 press release here). In addition, children who received the real vaccine had no more side effects than kids in the placebo condition. The vaccine was both effective and safe. 

Despite the intuition that placebo controlled studies are necessary, there are good reasons for concern with the HHD's new policy. 

For one, the BBC story pointed out an inaccuracy in the HHS statement: 

The [new HHS] statement said none of the childhood vaccines recommended in the US - except the Covid shot - had undergone "inert placebo" testing, meaning "we know very little about the actual risk profiles of these products".

But public health experts say the statement is misleading, as childhood vaccinations, including ones for Hepatitis A and B, rotavirus, polio and the mumps, were all initially tested against a placebo. In fact, all new immunizations already go through the trials - a type of random testing where one test group receives the immunization, and the other gets a placebo, like a saline shot.

In addition, modern public health policy does not require placebo controls after a vaccine has been shown to be effective. That is, most scientists and public health experts agree that we should approach placebo testing differently when an already-effective vaccine is only slightly changed (a vaccine might be slightly changed to respond to changes in flu or COVID viruses from year to year).

... newer versions of the shots may not go through the same process, because it is considered unethical to withhold a shot known to be safe from a particular group, and because the shot is only being tweaked in a minor way, vaccine experts said.

a) In Chapter 4 you read about three core ethical principles: Respect for Persons, Beneficence or Concern for Welfare, and Justice. Review these principles and what they mean. Then decide which one of them applies to the concern above (that " it is considered unethical to withhold a shot known to be safe from a particular group")

Here's another, more practical concern about the new policy: 

Requiring companies to conduct placebo tests for simple upgrades of established vaccines would be costly - and the drug makers could ultimately decide to forgo making the newer, more effective versions of the vaccine altogether, said Dr Lurie.

b) When psychology researchers test new psychotherapies for efficacy, they may also use placebo control therapies in their studies, such as a supportive conversation with no therapeutic structure. Do you think this is ethical? Why or why not?  How might your answer change depending on whether the therapy is brand new, or a revision of an existing therapy?

c) Psychology researchers might, instead of using a placebo therapy, use a "wait list design" in which people are randomly assigned to either receive they new psychotherapy right away or assigned to wait, say 6 to 8 weeks, before starting their therapy. Both groups are tested for symptom severity (say, anxiety or depression levels) at the end of the 6 to 8 week period.  What do you think of the ethics of this design? Does it seem better or worse than the placebo design? Explain your thinking. 

 

Touchscreens, buttons, and dials: What's best for drivers?

Survey, observational, and qualitative research can all support carmakers' decisions about how to design a dashboard. Photo: insta_photos/Shutterstock

Here's a story about automobile dashboards! After leaning in to designing cars with touchscreen dashboard controls, automakers seem to be heading back to basic buttons. This summary, from the website PopSci.com, discusses the variety of research that went into the decision--all of which fits into Chapter 6.

I thought it was only me who hated the touchscreen in my new car, but I'm not alone: 

For years, car safety experts and everyday drivers have bemoaned the loss of the humble button. Modern cars have almost unilaterally replaced dashboards full of tactile knobs with sleek, iPad-like digital displays, despite concerns these alluring devices might be making distracted driving worse. But there are signs the tide might be shifting. 

A. Some of the research behind the shift to more dashboard knobs and buttons has been qualitative, as described by this statement about focus groups: 

“When we tested with our focus group, we realized that people get stressed, annoyed and steamed when they want to control something in a pinch but are unable to do so,” Ha [a designer at Hyundai] said.

B. Some of the research came from survey data:

A survey of U.S. car owners by JD Power last year found a consecutive two-year decline in overall consumer satisfaction with their vehicles for the first time in 28 years. The main driver of that dissatisfaction was complicated, difficult to navigate touch-based infotainment systems. [...] Only 56% of drivers surveyed said they preferred to use their vehicle’s built-in infotainment systems to play audio. 

“This year’s study makes it clear that owners find some technologies of little use and/or are continually annoying,” JD Power director of user experience benchmarking and technology  Kathleen Rizk, said in a statement. 

C. And some of the research came from observational data:

A 2017 study conducted by the AAA Foundation claims drivers navigating through in-car screens to program navigation apps and other features were “visually and mentally” distracted for an average of 40 seconds. A car traveling at 50mph could cover half a mile during that time. Buttons and knobs aren’t totally distraction-free, but research shows their tactile response allows drivers to use them more easily without looking down and away from the road.  

Questions:

1. Section A. Reread the short section on focus groups in Chapter 6 (pp. 171-173 in the 5th Edition). Ask one specific question about the Hyundai focus group in Section A, above, that would help you determine the quality of the focus group. (For example, I might ask, "how did the focus group moderators inform their participants about confidentiality?" You should ask another question!)
   
   
2. Section B. Read through Chapter 6's section on survey wording (pp. 150-161 in the 5th Edition). Write two specific questions about the JD Power survey that might help you interrogate the survey's construct validity, and say a bit about why you are asking. Here's one example to get you started:
   
   • Ex: Did JD Power pay attention to the order of questions (p. 155) in the survey? It might matter because if they asked about people's frustrations with their cars first, that might affect their responses to the general question about their overall satisfaction.
     
     
3. Section C. The story links to two observational studies: one conducted by AAA, and one conducted by a car magazine in Sweden. Select one of these studies and read it. As you read, locate the answers to the following questions:
   
   3a. How many different car models were tested? How did the car models vary?
   3b. How many drivers were used to test the car models?
   3c. What specific tasks did the study use in order to test the distraction level of touchscreens?
   3d. Which car or cars were considered the safest, and which car or cars were considered the least safe, in terms of dashboard controls? What data supports this ranking? That is, how did the study researchers operationalize  "good performance"?
   3e. What makes this an observational study? Which observational study examples in Chapter 6 seems the closest to the car study? (see pp. 163-164).
   3f. Do you think the study was susceptible to observer effects or observer bias? Explain your answer.  

Emoji use predicts narcissism

Were all the variables in this study measured? Were any manipulated?
Photo: Caftor/Shutterstock

Psychology Today columnist Sebastian Ocklenburg summarized a recent study under the headline, The Dark Side of Emojis: A Surprising Link to Narcissism. Let's take a look.

It's a recent correlational study that tested the link between various personality traits and emoji use. (The empirical article can be found here.) 

The columnist provides a straightforward summary of the study's method here:

To clarify this question, a new study, just published in the scientific journal Current Psychology, scientifically investigated the link between gender differences in emoji use and different personality variables (Kennison and co-workers, 2025). In the study, the research team, led by Shelia M. Kennison of Oklahoma State University, gathered data from 285 undergraduate students (135 male students, 145 female students, and 5 students with other genders). First, the scientists gathered information on the volunteers’ emoji use. The volunteers had to rate the frequency at which they used 40 different commonly used emojis when communicating. They were also asked how often they used emojis when writing text messages, social media posts, and replies to social media posts of other people.

Moreover, the scientists assessed different aspects of personality in the volunteers using questionnaires. These included the so-called Big Five (openness, extraversion, agreeableness, conscientiousness, and neuroticism), the Dark Triad (narcissism, Machiavellianism, and psychopathy), and sensation-seeking.

 

a) The headline of the column was "The Dark Side of Emojis: A Surprising Link to Narcissism.". Is this a frequency, association, or causal claim?

b) Based on what you've read so far, classify the variables in this study (hint: I've counted 10 variables). 

Variable name	Potential levels of this variable (if you don't know the numbers, you can say "low to high"	Is this manipulated or measured?

c) Was this study correlational or experimental? 

Now let's read about the study's results. I'll present three findings: 

...First, the results indicated that women used both positive and negative emojis more often than men did based on their responses about their usage of the 40 common emojis. Women also indicated that they used emojis more often than men when writing text messages, social media posts, and replies to social media posts of other people. 

...higher emoji use was linked to higher narcissism in both women and men, with highly narcissistic women showing the most emoji use....

In terms of the Big Five, both men and women with higher extraversion reported more emoji use than introverted people. 

d) Take a look at the three results above. Select two you'd like to sketch a graph of. Then set up the axes for each graph. Remember to label your axes mindfully, and think about whether a bar graph or a scatterplot is the best graphing approach. 

e) Some of the results from this study showed moderators, and the moderator was gender. Here is one example of a moderator patterns in the results:

For men only, higher Machiavellianism was linked to more emoji use. 

Sketch a graph of this moderator. Hint: Make a scatterplot (what will go on the x and y axes?), using one color or shape for men dots and another color/shape for women dots. 

f) Here is another example of a moderator pattern:

For men only, it was also found that people with high neuroticism (indicating mood instability and difficulties dealing with negative emotions) specifically used more negative emojis than other people. 

Sketch a graph of this moderator. Hint: Make a scatterplot (what will go on the x and y axes?), using one color or shape for men dots and another color/shape for women dots. 

 

Changing minds

The article reported three evidence-based techniques for changing people's minds. Photo: Caftor/Shutterstock

In our era of extreme political polarization, people's attitudes seem entrenched. However, a new BBC article proposes some ideas for changing others' minds, under the headline "The Simple Trick to Change Other People's Minds." 

The author's essay focuses on three simple tricks. One is curiosity. He claims: 

By demonstrating our good intentions to learn and understand, we will encourage them to lower their defences so that they are more open to an honest exchange of ideas.

Here's the author's description of a study that tested this idea. As you read, decide if the study was experimental or correlational:

Often it is as simple as asking the right question. In the late 2000s,Frances Chen and colleagues at Stanford University invited students to engage in an online debate about whether the university should introduce a new set of exams. Unsurprisingly, many students were dead set against the idea. Crucially, they thought they were chatting to their peers, but their debating partners were really the experimenters themselves – who followed very rigid scripts that varied depending on whether the participant was in the experimental group or the control group.

In half of the conversations, the experimenters asked the students to elaborate on their views. For example, they might listen to a student's argument and respond: "I was interested in what you're saying. Can you tell me more about how come you think that?" For the other trials, the conversation did not include any request for more information on the participants' beliefs.

It was a tiny change in the script, but the addition of the single question changed the whole tone of the debate by provoking a considerably more open-minded response from the participants. They were more willing to continue the conversation and to receive further information on the other person's arguments, for instance.

a) Classify the variables in the study above. I've helped you out by putting variable-related text in bold. 

Variable name	Levels of this variable	Is this manipulated or measured?	Is the variable an IV or DV?	For the IV: was it manipulated as independent groups or within groups?

b) Assuming they randomly assigned students to the two groups, which type of design was it: posttest only? Pretest-posttest? Repeated measures? Or concurrent measures? Explain your answer.

c) Sketch a graph of the study’s results. You don’t know the exact values but you should be able to guess the pattern (hint: Be sure to put the DV on the y-axis).

d) Apply the three causal criteria here to decide whether this study supports the causal claim: "showing curiosity in a person's argument causes them to be more willing to hear your own argument." 

 

A second of the author's "simple tricks" is sharing personal experiences. Here is a study that illustrates how personal experience works:

Consider what a recent examination of the 2018 midterm elections demonstrated. The study measured the progress of 230 canvassers, conversing on a range of political issues with 6,869 voters across seven US locations. Some were asked to make their case using purely statistical arguments – concerning, for instance, the common fear that immigration increases crime – while others were asked to exchange personal stories, in addition to presenting factual evidence.

Each of the voters took opinion polls before and after they met the canvassers. The researchers found that the mutually respectful exchange of experiences was more likely to shift opinion than conversations that focused more on impersonal facts and statistics.

Just for fun, here's a note on the effect size of this study:

While the overall effects were small – resulting in a five-percentage-point shift in views on immigration, for example – this should be taken in context. On average, the conversations lasted just 11 minutes in total, yet a significant number of people started to change strongly held views.

e) Classify the variables in the study above. I've helped you out by putting variable-related text in bold. 

Variable name	Levels of this variable	Is this manipulated or measured?	Is the variable an IV or DV?	For the IV: was it manipulated as independent groups or within groups?

f) Assuming they randomly assigned students to the two groups, which type of design was it: posttest only? Pretest-posttest? Repeated measures? Or concurrent measures? Explain your answer.

g) Sketch a graph of the study’s results. You don’t know the exact values but you should be able to guess the pattern (hint: Be sure to put the DV on the y-axis).

h) Apply the three causal criteria here to decide whether this study supports the causal claim: "sharing personal experiences causes people to shift their opinions." 

i) Feel free to read the article on your own to read the evidence behind the third "simple trick": maintaining civility. 

 

Link to the empirical article for the first study, on showing curiosity. 

Link to the empirical article for the second study, on personal experiences.

"The problem with optimism"

Although optimism feels good, it's more effective to also anticipate what might go wrong. Photo: Jacob Lund/Shutterstock

This article, entitled "the problem with optimism," discusses one of my favorite theories of psychological motivation and goal pursuit--Gabrielle Oettingen's theory of "mental contrasting with implementation intentions" (or MCII).  (The popular press article might be paywalled, so try your college library for the full piece.) 

The journalist introduces the topic by talking about how to cope with distress--perhaps distress about a personal health problem, or distress about current events. One strategy might be to just "look on the bright side:"

Many therapists are trained to identify the exaggerated emotional responses and distortions of reality that beset their patients, and to help them understand that things are not as bad as they imagine. But when the situation really is as dire as a patient believes, soothing reassurance that one’s distress is misplaced would be malpractice.

The article continues: 

Optimism relaxes us, robbing us of the drive to take action. But angst, like a smoke detector, is a powerful motivating force—one that can impel people to help bring about the very changes they need to feel better. Worrying about missing an important deadline at work might, for example, rouse you to work faster or cancel other plans that would delay your task.

Here's a description of a study from Oettingen's lab that supported this theory:

Research suggests that you’re less likely to take such action if you insist on pretending that things will be fine. For example, in a pioneering study published in 2011, college students who were instructed to imagine that the following week would be terrific felt significantly less motivated and energetic—and were academically less productive—than their peers who were told to visualize all the problems that might take place during the coming week. In difficult times, inappropriate optimism can disarm and relax us—and substitute for actions that could actually bring about that sunny imagined future.

The study above was a simple experiment. 

a) What were the two groups (the two levels of the independent variable?)

b) What were the dependent variables? (I counted about about three of them).

c) This was an experimental design. Classify its two main variables:

Variable name	Levels of this variable	Is this manipulated or measured?	Is the variable an IV or DV?	For the IV: was it manipulated as independent groups or within groups?

d) Assuming they randomly assigned students to the two groups, which type of design was it: posttest only? Pretest-posttest? Repeated measures? Or concurrent measures? Explain your answer.

e) Using your answers to the table, sketch a graph depicting the results of the study (remember to put the DV on the y-axis)

Now read more about the theory:

.... a technique called mental contrasting, co-developed by the psychologist Gabriele Oettingen, who led the study on college students. The idea is to visualize an attainable goal (such as getting involved in local politics or running a mile), then think about all the obstacles that might get in your way (such as failing to find people who share your political vision, or shin splints). Mental contrasting has been shown to help people improve their relationships and recover from chronic pain, possibly because it undercuts the complacency brought about by unrealistic optimism. 

e) Dig a little deeper into mental contrasting theory by choosing one of these follow-up ideas:

    1. Try the WOOP app, which walks you through a mental contrasting practice.
   2. Click on one of the links in the paragraph above (Even better....) to read one of the two empirical articles linked there. You can read exactly how Oettingen studied mental contrasting in people's close relationships and chronic pain. 

Childhood physical activity and teen depression

The study found that kids who spent more time in physical activity during ages 6-9 were less likely to be depressed at age 15.
Photo: PeopleImages.com - Yuri A/Shutterstock

The online magazine Study Finds has a new summary of a developmental psychology study. The headline focuses on the relationship between childhood screen time and teen depression. But another major result of the study found a relationship between childhood physical activity and teen depression. We'll focus on that here. 

Here's some detail from the article: 

The eight-year study, which tracked children from elementary school into adolescence, found that kids who racked up more screen time—especially on mobile devices—showed higher levels of stress and depressive symptoms as teenagers.

This study has a longitudinal design, apparently ("The eight-year study, which tracked children...") tells you so. 

Now for the method and results. I'll join you on the other side.

Study authors used data from the Physical Activity and Nutrition in Children (PANIC) study, which followed 187 Finnish children over eight years, from ages 6-9 into their mid-teens. Researchers regularly checked in on their physical activity, screen time, sleep patterns, and eating habits. When these children reached adolescence (average age 15.8), the researchers assessed their mental health using standardized measures of stress and depression.

The data painted a clear picture: teenagers who had accumulated more total screen time and mobile device use throughout childhood showed significantly higher levels of stress and depressive symptoms. [...]

Physical activity told the opposite story. Teens who maintained higher activity levels during childhood...showed better mental health outcomes. This protective effect remained significant even after researchers accounted for factors like parental education, body composition, and puberty status. [...] Surprisingly, neither diet quality nor sleep duration showed strong relationships with teen mental health in this study. 

Let's apply what you're learning in Chapter 9 to this summary.

a) This is a study that used a multiple regression design. What was the main dependent (criterion) variable? 

b) Focusing on the paragraph above that starts "Physical activity told the..."  what were the independent (predictor) variables? (Hint: I counted six of them if you include screen time).

c) Mock up a little regression table. putting the criterion variable on the top and the predictor variables below it. Make up a beta for each predictor variable--of course you don't know what the real betas were, but you should be able to guess whether beta is positive or negative, and also to determine whether it is closer to or further from zero. 

DV: xxx

IVs:           beta

xxx            0.xx
xxx            0.xx
xxx            0.xx
(etc)

d) Write a "beta" sentence for two of the betas on your table. Use this template: ("There appears to be a ____ relationship between ___ and ____, such that higher levels of _____ go with higher/lower levels of _____, when controlling for ___, ____, ____, ____, and ____")

The journalist slips into causal language in this article. Here's an example:

Researchers now have more concrete evidence about lifestyle factors that might help prevent psychological distress before it takes root.

 

Replication update: Mueller-Lyer Illusion (Ch 14)

Turns out this illusion is probably universal--not only in humans, but also in other species. Photo: Dr. Dorsa Amir

One of the examples in Chapter 14 is about the Mueller-Lyer Illusion (pictured), specifically, as a prominent example of culture's influence on psychological processes. The original study (conducted by Segall, et al., 1966) showed that people who don't grow up in "carpentered" worlds don't fall for this illusion: They see the two blue lines as the same length. Segall and his colleagues argued that only people in Western, carpentered worlds see the top line as longer than the bottom one. 

It turns out that the phenomenon of drastic cultural differences in this illusion is probably not replicable. And the explanation is probably incorrect.

Specifically, Professors Dorsa Amir and Chaz Firestone have published a new paper in Psychological Review, arguing that:

"(1) the illusion is not limited to humans, appearing in non-human animals from diverse ecologies;
(2) the statistics of natural scenes are sufficient to capture the illusion;
(3) the illusion does not require straight lines typical of carpentry (nor even any lines at all);
(4) the illusion arises in sense modalities other than vision; and
(5) the illusion arises even in congenitally blind subjects."

I'm convinced by their argument. You can download a preprint of their paper here (I especially appreciated the figures!).

I'll be updating this example in the 6th Edition of Research Methods in Psychology. 

Big thanks to Dr. Hilary Barth, who made sure I knew about this paper!

Some sports psychology behind the Eagles' Super Bowl win

 

How could you test the hypothesis that the most successful pro athletes are more creative than average?
Photo: Timothy A. Clary/AFP via Getty Images

The Eagles are the most local team to my university (the University of Delaware is less than one hour from Philly), so we were excited when they won the Super Bowl last month! (Notice the BIRGing I just did?)

A Drexel University-based sports psychologist, Eric Zillmer, wrote this column in The Conversation that applies some sports psychology findings to the Eagles' win. It's a great introduction to sports psychology, and how it can help us understand the game at a deeper level.  (Instructors: The questions I am asking here are producer-oriented, but you can adapt the examples according to the goals of your own course.)

The first section of Zillmer's article refers to the personality profiles of successful, high-profile athletes. The most research-valid way to assess personality is with the Big Five, which consists of extroversion, conscientiousness, agreeableness, neuroticism, and openness to experience. Zillmer mentions the following results:

Research suggests that super-elite athletes are tough-minded and not easily rattled.

Their psychological profiles look similar to those of high-performance solo classical guitarists or fighter pilots. On personality tests, athletes typically score at least average in extroversion, openness and agreeableness, and high in conscientiousness. Professional athletes work incredibly hard and are disciplined, well organized, goal-oriented, reliable and generally sociable.

A new focus in personality research in competitive athletes is on creativity and, specifically, being open to experience, which includes being receptive to new ideas and being flexible.

Openness to experience and a related trait, creativity, are important to sports performance:

Creativity is essential in unscripted football plays – when a planned play has not been executed properly, like a fumble or an interception. [...]. it becomes even more critical during improvised offensive plays when everything is unscripted. In a sport where milliseconds matter, being creative and engaging in something your opponent doesn’t expect can be the difference between winning and losing.

a) Think about how you could design a study to test the hypothesis that openness to experience is associated with being a more successful professional athlete. Imagine that you have a large pool of athletes of varying ability who are willing to answer questionnaires for you. What traits would you measure in this sample? What variables do you predict would be associated with that trait, and how would they be associated? (sketch a scatterplot, with the trait on one axis, and the athletic outcome variable on the other axis).

 

The second section of the article has to do with the role of anxiety in sports performance:

Performance anxiety is a leading cause of poor athletic performance. Research suggests an athlete’s competitive anxiety can be cumulative and maybe even be contagious, affecting teammates negatively.

Since emotions and thoughts affect behavior and performance, the concept of emotional self-regulation – or intentionally focusing on the present moment – has been introduced into competitive sports. Mindfulness, meditation, yoga, breathing exercises and grounding techniques are now integral to the toolkit for high-performance sports.

b) Think about how you could design a study to test the hypothesis that mindfulness training can improve the performance of professional athletes. Because this is a causal hypothesis, you'd conduct an experiment--what would the independent variable be? What would the dependent variable be?  Would it make more sense to use an independent-groups design or a within-groups design? 

c) Alternatively, you could read about this study, which tested mindfulness training on the anxiety of competitive shooters. How could you adapt this study to a sample of professional football players? 

The third section of Zillmer's article has to do with fan behavior. He writes:

Fans... [are often] are nervous wrecks, like parents watching their kids compete. One remedy for managing this stress is watching the game with other fans. 

d) The study that Zillmer linked to in his article didn't actually measure fans' anxiety. So, how could you design an experimental study to test the hypothesis that watching a big game with other fans can help reduce the fans' anxiety abut their team? What would the independent variable be? What would the dependent variable be? 

 

 

Guess what's contagious in chimpanzees?

Given the study's results, predict which of these chimps is most likely to pee after another one does.
Photo: Dirk M. de Boer/Shutterstock

The radio program Short Wave (on NPR) highlights 3 science-related news stories every week. In this episode, one story is about a study showing that peeing is contagious in chimpanzees. The study is an excellent example of observational research, statistical reasoning, and turning an informal observation into a full-fledged empirical study.

You can listen to the story here--the study is explained in the first 3 minutes of an 8-minute broadcast. I'm going to paste sections of the transcript here, which features a conversation between reporters Regina Barber, Jonathan Lambert, and Ari Shapiro:

BARBER: [...] Ena Onishi, a grad student at Kyoto University in Japan, was studying a group of captive chimpanzees when something similar struck her as odd.

ENA ONISHI: I noticed that they seemed to have a tendency to urinate around the same time. And it kind of reminded me of some human behaviors of going to the bathroom together, for example.

[...]

LAMBERT: Yeah. So this observation got Ena wondering if this behavior might be socially contagious, like yawning. And to see if it was, she spent more than 600 hours watching a group of 20 chimpanzees.

[...]

LAMBERT: [...] she said that it was easier to hear them pee than to see them pee. But so she noted, when each individual chimp peed and where they were relative to each other. And looking at the data altogether, an interesting pattern emerged.

BARBER: So basically, chimps were peeing together a bit more often than you'd expect if they were just peeing at random. And she published that conclusion in the journal Current Biology this week.

SHAPIRO: Could she tell why this was happening?

BARBER: So being closer helped, but proximity wasn't, like, the main factor here. Chimps have a hierarchical society, and it turns out that the lower ranking chimps were more likely to catch the urge to pee from more dominant chimps.

SHAPIRO: So when my boss pees, I'm going to pee?

BARBER: Yeah.

SHAPIRO: Is that what you're telling me?

LAMBERT: Maybe. And they don't know why this is. It could just be that lower ranking chimps are paying closer attention to higher ranking ones. But that's just one possibility.

SHAPIRO: OK, so there's clearly more to study here, but is there any evolutionary reason that contagious urination would be an advantage?

LAMBERT: One idea is that doing the same thing together just kind of helps a group sync up, which could help them operate better as a unit. If this happens in the wild, it might help the chimps avoid predators who get attracted by the smell of pee by concentrating it all in one spot.

BARBER: But we can't rule out that there might be, like, a non-adaptive reason. It could be just that the chimps pee when they hear other chimps pee, sort of like humans get the urge when you hear, like, running water.

In the empirical study, Onishi and her co-authors counted up (for a total of 600 hours) how often an instance of one chimpanzee's pee co-occured within 60 seconds of another one's pee. This was an instance of "pee contagion." Then, they compared their observed number of c0-occuring pees to a simulation model. In the simulation model, a computer estimated the total number of co-occuring pees they'd see in 600 hours if the null hypothesis is true--that is, how often would the pees occur within 60 seconds of each other if there's no contagion? They found that the observed data showed more pee contagion than the simulated model, confirming that there was contagious peeing.

Then they correlated "pee contagion" to two predictors. First, they found that contagion was more likely when two chimpanzees were physically closer to each other: There was more contagion when chimps were arms' reach, compared to 1 meter or more than 3 meters away. Second, they correlated pee contagion with each chimp's position in the hierarchy, and found a positive correlation, such that the higher the first chimp was in the hierarchy, the more likely another chimp would contagious pee after they did. 

Questions

a) Reading the transcript (or listening to the story), what was Onishi's informal observation, and how did she turn it into a study? Reflect on how her research started with an informal hunch, and evolved into a full-fledged published article. Do you have any research hunches that you'd like to turn into a published article?

b)  The commentators bring up three very different explanations--mechanisms--for the contagious peeing effect. Identify the three explanations in the story.

c) Pick one of these three explanations. How could you collect data in a new study to test that explanation? What variables would you measure, and what result would support the explanation? 

The Current Biology article is available here. You can see scatterplots of pee-contagion and dominance, and dot plots of pee-contagion by distance away. (may be paywalled)