Everyday Research Methods

Ethics, justice, and vaccine trials

The journalist writes that Black Americans have been proportionally less willing to participate in vaccine trials, perhaps because researchers in the past have violated their trust. Photo: Andrey_Popo/Shutterstock

At time of writing (late 2020), researchers around the world are racing to test vaccines for the covid-19 virus. To test the efficacy and safety fo these vaccines, studies must recruit thousands of participants and track them over time. They need to know, Do people who get the vaccine have any negative side effects? Do people who get the real vaccine have lower rates of COVID infection compared to people who get a placebo vaccine? 

In this story, New York Times journalist Jan Hoffman explores one legacy of the Tuskegee study: It has led to pervasive distrust of research participation on behalf of African-Americans. This means that Black Americans may not be eager to participate in vaccine trials for covid-19. 

We need to include people of all ethnicities in these clinical trials. Hoffman writes:

It is essential, public health experts say, that research reflect diverse participation not only as a matter of social justice and sound practice but, when the vaccine becomes available, to help persuade Black, Latino and Native American people to actually get it. (The participation of Asian people [in vaccine trials] is close to their share of the population.)

“Historically, we test everything in white men,” said Dr. Michael, a member of the vaccine development team at Operation Warp Speed, the public-private partnership set up by the White House. “But the disease is coming after people of color, and we need to encourage them to volunteer because they have the highest burden of disease.”

Early on in vaccine trials, African-Americans were making up only 3% of the study participants (this group is 15% of the U.S. population). Therefore, recruiters like Carla Arnold were going door to door and meeting with small community groups, trying to recruit African Americans to participate in the trials. Hoffman's article includes this exchange:

...an older woman turned the question back on Carla Arnold, ... who is well-known to people in the Heights:

“Miss Carla, would you feel comfortable allowing them to inject you?”

Ms. Arnold, 62, adjusted her seat to face them down, her eyes no-nonsense above a medical mask.

“They already did,” she replied. 

[...] Ms. Arnold carefully explained to the tenants her decision to participate in the trial for the vaccine being developed by Moderna, a company that has received pledges of $1.5 billion from the Trump administration for its effort.

“I am a proud African-American woman,” she said. “As African-Americans, we always seem to get less out of things that go on. I want us at the forefront of this. I want to make sure that Black people are represented. I’m going by faith that these people won’t do to African-Americans what they did to us in Tuskegee. I’m holding them accountable.”

Questions

a) In Chapter 4 (Ethics) you read about Tuskegee Syphilis study, which violated all three ethical principles of the Belmont Report. One of these is respect for persons. How did the Tuskegee study violate this principle?

b) The second Belmont principle is beneficence. How did the Tuskegee study violate this principle?

c) The third Belmont principle is justice. How did the Tuskegee study violate this principle?

d) Which Belmont principle is most relevant to the composition of the participant pool for a vaccine trial? 

The Tuskegee study isn't the only famous historical example of research that unfairly targeted Black Americans. Hoffman reminds readers of three infamous studies here: 

[examples] include surgeries by Dr. J. Marion Sims, a 19th-century gynecologist, on enslaved Black women, the 40-year-long Tuskegee study, in which doctors deliberately allowed syphilis to progress in Black Alabama sharecroppers, and researchers’ taking of cells without permission from Henrietta Lacks, an African-American cancer patient, in 1951.

e) As Hoffman's article explains, African-Americans' reluctance to participate in vaccine trials probably reflects, in part, an understandable mistrust of researchers, based on these past examples (“'It’s not the science we distrust; it’s the scientists,' said Jamil Bey"). Reflect on this outcome of past unethical practice: Why 

In one sense, it might seem fair that in the current trial, African-Americans are not disproportionately "bearing the burden" of the vaccine trials and its potential risks (that's the justice principle). Nevertheless, by not recruiting African-Americans in the trials, this group risks being left out of its potential benefits, too. It's an issue of ethical fairness and good practice: Access to to the vaccine (even in its trial form) needs to be equal in all communities, both now and in the future. And this is especially true because Black Americans are much more likely to be affected by the virus.

Hoffman's article ends on a positive note:

By the following week, there were signs that the outreach efforts were helping. The portion of people of color in the Pittsburgh area in the vaccine registry had risen to 8 percent, up from 3 percent. Because trial leaders can choose whom they finally enroll, they have been increasing the percentage of nonwhite subjects. Moderna reported that nationwide, as of Sept. 28, 26 percent of those enrolled were Black.

Thank you to Adam Putnam of Furman University for sharing this example with me. 

State scatterplot: Mask mandates and COVID cases

Wearing masks is associated with lower rates of COVID-19 symptoms.
Photo: Syda Productions/Shutterstock

Here is an article that amplifies Chapter 8 concepts. Using U.S. states as the unit of analysis, it depicts the correlation between mask-wearing and rates of knowing a person with COVID. Please scroll to the middle of this article from the Washington Post to see the scatterplot (we don't have permissions to show it here). 

The journalist's writing makes the concepts extremely accessible to students and laypeople. Here's an example: 

For all 50 states plus D.C., this chart plots the percentage of state residents who say they wear a mask in public all or most of the time (on the horizontal axis) and the percentage who say they know someone in their community with virus symptoms (on the vertical axis).

The journalist's orienting remarks are really helpful for a person viewing a scatterplot for the first time:

Take Wyoming and South Dakota, for instance, in the upper left-hand corner of the chart. Roughly 60 to 70 percent of state residents report frequent mask use, as shown on the bottom axis, which puts them at the bottom for mask rates. They also have some of the highest levels of observed covid-19 symptoms, approaching 40 and 50 percent.

Questions for students:

a) Looking at the scatterplot, what is one of the states with the highest rates of mask-wearing?
What is a state with one of the highest rates of "knowing someone with covid-19 symptoms"?

b) Is this correlation positive or negative? Is the association strong or weak? (btw, the r is about .85)

c) Can this association, on its own, support the claim that "wearing a mask prevents covid-19?" Apply the three causal criteria.

d) Here is a quote from the article that is relevant to the causation issue. Is Dr. Reinhart's comment referring to temporal precedence or internal validity?  (and why?)

“There could be other explanations for the correlation,” [Reinhart] said. “For example, states that had worse outbreaks earlier in the pandemic both have higher mask usage now and more immunity.”

e) Let's talk about how they measured the two variables in this correlation--the construct validity. This is a special case in which construct validity overlaps with external validity. Can you explain why? 

[to measure these two variables]...the CovidCast team partnered with Facebook, which is used by 70 percent of U.S. adults and has the ability to survey tens of thousands of them every day at relatively low cost. While the resulting state-level samples aren’t perfect representations of the general population, the researchers weight the responses using Census Bureau demographic data to ensure they’re a good approximation.

e) Challenge question: How do you think this scatterplot would have looked if the data came from June (when infection rates were mostly higher in the Northeast), instead of October? 

Instructors:

The article's data comes from a Carnegie Mellon website that depicts data for several pandemic-related variables for the U.S. For example, you can see a map of the frequency of mask-wearing here. The data from both x and y axis appear to come from here. You can also search how often people in different states do Google searches for the term, "covid" and the percentages of physician appointments that are COVID-related. The site includes R code for their figures, so students might be able to download the raw data and run their own analyses, too.

 

 

 

Podcast: WOOP, WOOP!

 

Wish, Outcome, Obstacle, Plan: An evidence-based app. Photo courtesy of Dr. Oettingen/Woopmylife.

Here's another podcast-based post. NPR's Hidden Brain podcast frequently uses psychological research to illuminate a variety of psychological topics. Today's post is based on a Hidden Brain episode called You 2.0: WOOP, WOOP!

 

The episode asks,"is there a methodical way to achieve your dreams?"

The episode starts off with the suggestion that maybe positive thinking alone isn't the best way to achieve your dreams. Of course, in the United States, we are encouraged to "dream big" and shoot for the stars. But as host Shankar Vedantam interviews researcher Dr. Gabriele Oettingen, we learn that positive thinking--by itself--may not be very helpful. 

At minute 1:40,  you hear how Dr. Oettingen first started getting interested in positive thinking--it stems from a cultural difference she observed between Germany (her home country) and the U.S.  Then Dr. Oettingen lays out the theory. She explains that positive thinking feels good, but...

(2:45)...these positive fantasies--they seduce us to feel already accomplished, and they take our energy away.

Dr. Oettingen describes a series of correlational findings:

(3:00) We found that, for example, the more positively women enrolled in a weight reduction program fantasized about their success in the program, the fewer pounds they lost--three months later, one year later, two years later.

Or the more positively university graduates fantasized about an easy transition into work life, the fewer dollars they earned two years later, the fewer job offers they had gotten, and the fewer job applications they had sent out.

a) Consider the dieting study above. Sketch a well-labeled scatterplot of the correlation she describes (one axis should have "level of fantasizing" on it, and pick one of the weight loss variables for the other axis).

b) Does this study, by itself, show that fantasizing caused these women to lose less weight? (Apply the three causal criteria).

c) Now consider the work life study above. Sketch a well-labeled scatterplot of the correlation she describes (one axis should have "level of fantasizing" on it, and you should pick only one of the job-related outcomes for the other axis).

d) Does this study, by itself, show that fantasizing caused these graduates to do worse on the job market? (Apply the three causal criteria).

In the podcast, Dr. Oettingen shared two other examples: One study on surgery patients and another study on people who had a crush on another person. [Instructors, you could consider having four different groups of students graph each of the four studies.]

e) What are the advantages of doing this same correlational study in four samples (dieters, graduates on the job market, surgery patients, and people with a crush?) Use the term "external validity" in your answer. 

Now let's move on to the rest of the theory. Dr. Oettingen's theory proposes that fantasizing by itself makes people relax--"because mentally, they're already there"--they've achieved the goal. It's better, she argues, to pair your fantasies with thoughts about obstacles. Starting at minute 9:00, Oettingen introduces the term mental contrasting, in which people follow their fantasies with thoughts about the potential obstacles, asking the question: "What is it in you that stands in the way?" 

Listen from minutes 9:00 to 15:30, as Oettingen walks through the WOOP process of activating wishes, fantasizing about the outcome, anticipating obstacles, and making a plan. (WOOP stands for Wish, Outcome, Obstacle, Plan.) 

Oettingen and her team are well aware of the limitations of correlational evidence like that introduced above.  So, although it's not described in the podcast, her team has also conducted experimental studies. In her experimental work, Oettingen and her team have randomly assigned people to engage in WOOP and others to engage in the activities in a different order (perhaps Obstacle, Wish, Outcome). They find that people in the WOOP conditions are more likely to achieve their goals.

f) If you were designing a study for people in a weight loss program, how would you design an experimental study to test if WOOP thinking would lead to more successful weight loss? 

i) What would the IV be (What would its levels be)? What would the DV be?
ii) How would you assign people to the conditions? 
iii) How would you eliminate internal validity threats? 
iv) Sketch a bar graph of your predicted outcome. 

Are you interested in trying WOOP out on your own goals and wishes? The phone app is free, and you can also access it online: www.woopmylife.org.

 

 

 

Too sleepy to choose that major

These students might misattribute their early-morning fatigue to their interest in the subject. Photo credit: Gorodenkoff/Shutterstock

Podcasts have been gaining in popularity in the last few years, and several feature psychological research. I'm going to be devoting my next few posts to some podcasts. We'll start with this very short, 3-minute episode/ interview from the NPR podcast, Hidden Brain. The episode is called "How does the way you feel shape your life?" and it's about how students might be influenced by situational factors (such as how early a class is) when they select their majors. 

The podcast opens with a story:

...behavioral economist Kareem Haggag at Carnegie Mellon University... sometimes teaches early morning classes. And he's worried that when his students are tired or sleepy, they might draw the wrong conclusion about him and about the class.

The researcher himself states (around minute 1)

DR. KAREEM HAGGAG: Our hypothesis was that students who are assigned to an early morning section of the class, or to multiple back-to-back classes before a class, might mix up how tired they are in that class with how much they like the subject, thus leading them to be less likely to choose the subject as their major.

The host, Shankar Vedantam, reports:

This is a psychological phenomenon called misattribution. You know, you go to an amusement park on a sunny day and you think the park is great. You go when it's raining - you think the park is terrible. You're unconsciously confusing your feelings about the weather for the quality of the park.

The researchers used thousands of data points from students at the U.S. Military Academy at West Point to test their hypothesis. As Vedantam points out:

... students at West Point are randomly assigned classes that are scheduled at different times of the day, which means that some students are taking this introductory class first thing in the morning. Some students are taking the very same class later in the day. The researchers then measure the chance that students would later on choose that subject as their college major.

The researchers, led by Dr. Haggag, found that students were more likely to major in the topic of that introductory class if they'd taken it at a later time of the day: 

HAGGAG: We find that students who are randomly assigned to the first period, 7:30 a.m. section, are about 10% less likely to choose the corresponding major compared to a student who takes that class later in the day. 

Questions

a) What are the two main variables of interest in this study (the independent and dependent variables)? What are the levels of each variable?

b) Is this a true experiment or a quasi-experiment? If it's a true experiment, what kind is it (prettest-posttest, posttest-only, repeated measures, concurrent measures)?

If it's a quasi-experiment, which of the four types is it (nonequivalent control groups, nonequivalent control group prettest-posttest, nonequivalent control groups interrupted time series, nonequivalent control groups interrupted time series)?

c) Which is the IV and which is the DV here?

d) Sketch a small bar graph of the results they describe.

e) Can these researchers support the causal claim that "having an early morning introductory class makes you less likely to want to major in that discipline?" Apply the three rules of causation: covariance, temporal precedence, and internal validity in your response.

f) Ask a question about this study's internal, external, statistical, and construct validity.

Bonus content:

In the study above, the main IV was "time of day of the introductory class". As a bonus, here is another independent variable they studied--"number of breaks before the introductory class":

DR. KAREEM HAGGAG: We also compared two students who are sitting in the exact same classroom, but one of whom just had a free period as a break before, and the other came from one or more back-to-back classes. We find that each additional back-to-back class reduces the likelihood that that student enrolls in the major by about 12%.

g) Which is the IV and which is the DV here?

h) Sketch a small bar graph of the results they describe.

i) Can these researchers support the causal claim that "having breaks before your introductory class makes you more likely to want to major in that discipline?" Apply the three rules of causation: covariance, temporal precedence, and internal validity.

 

 

Conspiracy reasoning vs. Scientific reasoning (the case of QAnon)

Conspiracy theories, unlike scientific theories, are unfalsifiable. Credit: buecax/Depositphotos

Early in the research methods course you learn about the theory-data cycle (Chapter 1). You also learn about the benefits of scientific reasoning over informal, everyday reasoning such as intuition, experience, or authority (Chapter 2). The QAnon conspiracy theory is a good example of several principles in these two chapters.

The quotes from this post come from this excellent story in The Atlantic, by journalist Adrienne LaFrance. 

In case you have never heard of the QAnon conspiracy theory, it started from a series of posts by an anonymous author named Q, who posted occasionally on anonymous "image boards" such as 4chan. Here is a quick recap by LaFrance:

In its broadest contours, the QAnon belief system looks something like this: Q is an intelligence or military insider with proof that corrupt world leaders are secretly torturing children all over the world; the malefactors are embedded in the deep state; Donald Trump is working tirelessly to thwart them. (“These people need to ALL be ELIMINATED,” Q wrote in one post.) The eventual destruction of the global cabal is imminent, Q prophesies, but can be accomplished only with the support of patriots who search for meaning in Q’s clues. To believe Q requires rejecting mainstream institutions, ignoring government officials, battling apostates, and despising the press. 

Part I. Falsifiability
Let's practice describing one way that conspiracy thinking is different from scientific thinking. One of the defining features of scientific thinking is that scientific claims are falsifiable. That is, scientists make claims/hypotheses that can be falsified  (disproven) by collecting and analyzing evidence. 

Most conspiracy theories a cannot be falsified, in part because they are stated in squishy, ambiguous terms, and in part because they are so multifaceted. As LaFrance writes,

In the face of inconvenient facts, [QAnon theory] has the ambiguity and adaptability to sustain a movement of this kind over time. For QAnon, every contradiction can be explained away; no form of argument can prevail against it.

Here is an example of some QAnon reasoning: 

At one point, Harger [a believer she interviewed] told me I should look into what happened to John F. Kennedy Jr.—who died in 1999, when his airplane crashed into the Atlantic Ocean off Martha’s Vineyard—suggesting that Hillary Clinton had had him assassinated. (Alternatively, a contingent of QAnon believers say that JFK Jr. faked his death and that he’s a behind-the-scenes Trump supporter, and possibly even Q himself. Some anticipate his dramatic public return so that he can serve as Trump’s running mate in 2020.) When I asked Harger whether there’s any evidence to support the assassination claim, he flipped my question around: “Is there any evidence not to?”

a) Why is Harger's theory (above) about JFK Jr.'s assassination unfalsifiable?

b) And, How does the theory's unfalsifiable nature make it more likely that Harger can believe in QAnon? 

c) Think like a scientist now. We can write a falsifiable statement (i.e., one that could be demonstrated by evidence to be wrong) related to JFK Jr. For example, to falsify the theory that "J.F.K. Jr. will become Trump's running mate in 2020" we could predict this statement:

"J.F.K. Jr.'s name will appear on the ballot with Trump in the 2020 Presidential election." (This is falsifiable because if someone else's name appears on the ballot, then this theory is wrong.)

Now see if you can come up with a falsifiable statement derived from the theory (above) that J.F.K. Jr.'s plane crash death was an assassination. Your statement can take the form, If J.F.K. Jr. was assassinated, then we should see __________. 

Part II. Accepting evidence
One feature of QAnon theory that makes it impossible to falsify is that its believers do not accept any evidence from legitimate sources. That is, whereas scientists use independently verifiable data to support or falsify theories, QAnon believers do not accept the legitimacy of such data. Here's LaFrance again:

Q frequently rails against legitimate sources of information as fake. Shock and Harger [the two believers] rely on information they encounter on Facebook rather than news outlets run by journalists. They don’t read the local paper or watch any of the major television networks. “You can’t watch the news,” Shock said. “Your news channel ain’t gonna tell us [expletive].” Harger says he likes One America News Network. Not so long ago, he used to watch CNN, and couldn’t get enough of Wolf Blitzer. “We were glued to that; we always have been,” he said. “Until this man, Trump, really opened our eyes to what’s happening. And Q. Q is telling us beforehand the stuff that’s going to happen.” I asked Harger and Shock for examples of predictions that had come true. They could not provide specifics and instead encouraged me to do the research myself. When I asked them how they explained the events Q had predicted that never happened, such as Clinton’s arrest, they said that deception is part of Q’s plan. Shock added, “I think there were more things that were predicted that did happen.” Her tone was gentle rather than indignant.

d) Why does QAnon's rejection of legitimate sources of information help its followers maintain their beliefs? 

As an aside, in the passage above, pay attention to the believer's statement that "deception is part of Q's plan." This feature virtually ensures that the theory remains unfalsifiable: Either believers will point to evidence that supports the theory, or, when faced with evidence that does not support the theory, they will claim that "deception is part of Q's plan." The theory is supported no matter what. 

Part III.  Confirmation bias.
In Chapter 2, you learn about quirks of human reasoning that make our intuition inferior to the scientific method. One of these quirks is the confirmation bias, the tendency of people to notice and remember evidence that confirms their beliefs, and to ignore or discount evidence that disconfirms them. 

e) How might confirmation bias be at play in the passage above, about events that Q predicted but that never happened? 

Confirmation bias is probably more prevalent when situations and theories are ambiguous. Ambiguity is clearly at play in QAnon, because Q usually makes general (rather than specific) threats. LaFrance writes,

—this is a very welcoming belief system, warm in its tolerance for contradiction—and are also what makes it possible for a practical man like Hayes [who has a youtube channel "explaining" Q's theories] to play the role that he does. QAnon is complex and confusing. People from all over the internet seek guidance from someone who seems levelheaded. 

f) Explain how confirmation bias, rather than scientific thinking (which strives to objectively evaluate evidence both for and against a theory), can explain the following reasoning about who Q himself is:

Many QAnon adherents see significance in Trump tweets containing words that begin with the letter Q. Recent world events have rewarded them amply. “I am a great friend and admirer of the Queen & the United Kingdom,” Trump began one tweet on March 29. The day before, he had tweeted this: “I am giving consideration to a QUARANTINE.” The Q crowd seized on both tweets, arguing that if you ignore most of the letters in the messages, you’ll find a confession from Trump: “I am … Q.”

Coda.
QAnon might seem like a harmless academic example of conspiracy theories. And, as LaFrance suggests, some people follow it just for fun (it might have even started out as a parody). However, as LaFrance argues, it has actually incited violence in the past. For example, in 2017, one believer walked into a Washington, D.C. pizza shop with a rifle because he had believed QAnon message boards that children were being held hostage in the shop's basement (the building doesn't have one). (He was wrong: "'The intel on this wasn’t 100 percent,' Welch told The New York Times after his arrest.")  LaFrance also reports, "In 2018, Reddit banned QAnon groups from its platform for inciting violence."  In the United States, several congressional candidates have promoted QAnon's theories. Unfalsifiable conspiracy thinking might give our leaders a (false) sense of confidence, but it certainly doesn't belong in government discourse.  

If QAnon supporters call for violence, revolt, or collective action and people do not think critically about what they say, this fake conspiracy theory will become a real threat to our democracy. 

Here's a great story of a person who used to believe in QAnon and has subsequently changed his mind. 

 

 

 

 

 

 

 

Lion attacks on cattle: A field experiment

Researchers painted eyes like these on some of the cattle in each herd. Credit: Cameron Radford

Here's an example of a field experiment that took place in actual fields. The empirical article appeared in Nature Communications, and the authors of the research wrote their own summary for a popular audience in The Conversation. The quotes below come from their popular audience summary. 

In this study, the authors worked with cattle ranchers in Botswana who were trying to reduce the number of lions and leopards who attacked their herds.

The introduction outlines the theory the researchers were testing:

Many big cats – including lions, leopards, and tigers – are ambush predators. This means that they rely on stalking their prey and retaining the element of surprise. In some cases, being seen by their prey can lead them to abandon the hunt. We tested whether we could hack into this response to reduce livestock losses to lions and leopards in Botswana’s Okavango delta region.

Below are some details of the method of their study. It sounds like an experiment: Can you see why?

Working with Botswana Predator Conservation and local herders, we painted cattle from 14 herds that had recently suffered lion attacks. Over four years, a total of 2,061 cattle were involved in the study.

Before release from their overnight enclosure, we painted about one-third of each herd with an artificial eye-spot design on the rump, one-third with simple cross-marks, and left the remaining third of the herd unmarked. We carried out 49 painting sessions and each of these lasted for 24 days.

The cattle were also collared and all foraged in the same area and moved similarly, suggesting they were exposed to similar risk. However the individuals painted with artificial eye-spots were significantly more likely to survive than unpainted or cross-painted control cattle within the same herd.

In fact, none of the 683 painted “eye-cows” were killed by ambush predators during the four-year study, while 15 (of 835) unpainted, and 4 (of 543) cross-painted cattle were killed.

Questions

a) What were the independent and dependent variables in this study? What were the levels of each variable?

b) What kind of experiment was this: Posttest-only? Prettest-posttest? Repeated measures? Or concurrent measures? (Challenge question: How might your answer change if you take the predator's perspective, as compared to the cattle's perspective?)

c) Sketch a bar graph of the results of the study. (Later you can compare your sketch to the figures in the Nature article).

d) The authors don't seem to specify here whether or not they used random assignment to the three painting conditions. Why would random assignment be important?

e) Ask a question about the external validity of this study. (Which term will your answer involve: random assignment or random sampling?) 

At the end of their popular press summary, the authors raise two important limitations:

First, it is important to realise that, in our experimental design, there were always unmarked cattle in the herd. Consequently, it is unclear whether painting would still be effective if these proverbial “sacrificial lambs” were not still on the menu. Further research could uncover this, but in the meantime applying artificial marks to the highest-value individuals within the herd may be most pragmatic.

Second, it is important to consider habituation, meaning that predators may get used to and eventually ignore the deterrent. This is a fundamental issue for nearly all non-lethal approaches. Whether the technique remains effective in the longer term is not yet known in this case.

f) How might you design a study to test the first limitation? That is, how could you manipulate the eye-paintings variable in order to test if this intervention still works if there are no "sacrificial lambs" in the herd? What would your different conditions look like?

g) How might the authors test the second limitation? What can they change about, or add to, their study to test whether "the technique remains effective in the longer term"?

 

 

 

Testing a stereotype about only children (null effect)

Are only children more narcissistic? Credit: stockimagesbank/Shutterstock

People hold all kinds of stereotypes about family structures. For example, lots of people believe that the eldest child in a household is the most responsible. People think that younger children are often spoiled or that middle children are ignored or neglected. Most research on birth order doesn't support these beliefs, though (here's one example, which found that oldest children are a bit smarter, but not any different in personality).

What about only children? Both laypeople and psychologists have sometimes claimed that only children are more likely to become narcissistic. Because they tend to be the focus of parental attention and don't have to negotiate with siblings, onlys are considered to have fewer opportunities to develop humility and social skills. But is the stereotype true?

Here's a journalist's summary of a study that tested this assumption. The study found "... no evidence that only children are more narcissistic than those with brothers or sisters": 

Past research on this topic has turned up conflicting findings. Those earlier studies, however, often had methodological limitations — such as small sample sizes — that made their results less than reliable.

The authors of the new study say their research addresses and corrects those problems. “We can now say with rather high confidence that only children are not substantially more narcissistic than people with siblings,” says Michael Dufner, the study’s lead author and a psychologist at the University of Leipzig

The published article included two studies. The first was meant to establish that people actually do hold the stereotype that only children are more narcissistic: 

... Dufner and his colleagues conducted an online survey of a representative sample of 556 German adults. Their average age was 46, and 105 of them (19 percent) were only children.

The survey was designed to determine the prevalence of the stereotype that people who are only children are more likely to be narcissistic than those with siblings.

The survey found that people do, indeed, tend to judge only children as being more narcissistic, both in terms of their need to be the center of attention (a characteristic known as narcissistic admiration) and in terms of their desire to demean or devalue others to build up their own inflated sense of importance (narcissistic rivalry).

The second study tested the stereotype's accuracy:

For the second part of the study, the researchers analyzed data collected from a representative sample of 1,810 German adults, including 233 without siblings, who answered questions designed to measure their level of narcissism.

The analysis debunked the stereotype. “We found no evidence for the claim that only children are more narcissistic than non-only children,” the researchers write.

Questions

a) What kind of claim is it to say, in the first study, that "people do, indeed, tend to judge only children as being more narcissistic" ? Frequency, association, or causal?

b) Was it important to use a representative sample in Study 1? Why or why not? 

c) The second study was a correlational design. Which two variables did they measure?

d) Why would it be important to use a representative sample for Study 2?

e) Sketch a bar graph of the result that would support the Study 2 statement, "We found no evidence for the claim that only children are more narcissistic than non-only children" 

f) Ask a question about the construct validity of Study 2. Ask a question about the external validity of Study 2.

g) To use terminology from the textbook, Study 2 found a null effect, or no difference. Chapter 11 reviews the potential reasons a study might find a null effect. One reason is a potential ceiling effect on the dependent variable, or DV. Do you think that could be a problem here, with the DV of narcissism? 

h) One way to ensure that a study has enough power and precision to accurately detect a true null effect is to be sure the sample size is large. What was the sample size in this study? 

i) Chapter 11's coverage of null effects focuses on how experiments can sometimes result in null effects, but this study is an example of a correlational study finding a null effect. That is, the authors measured both only-child status and narcissism, and found no relationship. Which of the questions about null effects don't really apply to a correlational study? See the table in Chapter 11 (3rd edition: Table 11.2, 4th edition: Table 11.3). 

To see the original empirical article, go here. 

Does "virtue signaling" change minds?

If a lot of us send virtue signals, it can communicate to others that norms are changing. Credit: SEEphotos7171/Shutterstock"

Among people fighting for racial justice, "virtue signaling" may sometimes be derided as empty action. "Virtue signals" are public messages of support for a social justice cause. They could take the form of a yard sign, a t-shirt, or a social media post. Critics sometimes argue that such messages are easy and cheap. It's easy for a company to put a message on their home page that supports Black Lives Matter. It's not hard to wear a t-shirt that supports a social justice cause. But are these expressions of support all talk and no action?

An article in Time magazine argues that virtue signaling can be more powerful than we think. The authors, psychological scientists Jamil Zaki and Mina Cikara, integrate several studies to make their argument. Here's part of their introduction: 

It’s critical to question people whose most radical act is temporarily changing their profile picture. It’s critical they follow their words with action; the time for talk has passed. But focusing solely on virtue signalers obscures their most important role. Like radio waves, the signals they send are received by someone, and those receivers matter, too.

Zaki and Cikara argue that virtue signaling is an important way of influencing others. If a lot of people change their profile pictures to support BLM, for example, more people will start to believe that this movement is popular and they may adopt it, too:

Opinions can work this way: when a particular viewpoint gets a lot of attention, people assume it’s popular, and shift towards it. We receive signals, and are changed by them.

What studies support their argument? Here's one that the authors summarize: 

As social norms shift, individuals shift with them: adopting popular opinions and behaviors, and dropping ones that fall out of style. Norms are especially powerful as they gain steam. In one set of studies, Gregg Sparkman and Greg Walton presented diners in a cafeteria [either] with evidence that 30% of the U.S. population was vegetarian, or that 30% of the population was newly vegetarian. People who learned about this second, “dynamic social norm” were twice as likely to order a meatless lunch themselves. They saw not just where the crowd was, but where it was going, and didn’t want to be left behind.

Here are some questions about the study summarized above:

a) Was this an experiment or a correlational study? How do you know? (If it's an experiment, what kind of study is it?)

b) What were the key variables in the study? Were they manipulated or measured? 

c) Sketch a small graph of the results being described. Would a bar graph or a scatterplot make more sense?

Here's another study Zaki and Cikara introduce:

Signaling and conformity are not inherently positive. In 2016, the U.S. elected a president whose campaign was jammed with racist talking points. Prejudice subsequently increased, specifically toward groups he targeted (e.g., Muslims, immigrants). 

d) This summary doesn't really help you figure out how the study was designed. But here's a chance to think carefully: What kind of study would we need to conduct in order to test the claim that "prejudice subsequently increased, specifically toward groups he targeted (e.g., Muslims, immigrants)."?  There are three variables you would need to include in your study. What are they? 

 

Suggested answers to selected questions

a) This was probably a posttest-only experiment, because it sounds like they manipulated the message about vegetarianism and measured what foods people took in the cafeteria. Here is a link to the original empirical article. 

d) The key dependent variable in this study is prejudice levels, so that's the first variable. Then, in order to establish that "prejudice increased", you'd need to measure prejudice both before and after the election, so that's another variable. Finally, in order to show that it increased specifically toward groups he targeted, you'd need to measure different types of groups (Muslims, immigrants, as well as other potential groups that Trump did not deride in his campaign). You can review the Crandall article here. Do their results really support what Zaki and Cikara are saying about it? (Pay attention to the difference between people's actual prejudice and the normativity of prejudice.) 

 

 

 

 

Racial bias in psychological science journals

A recent study found that in psychology, the authors, participants, topics, and editors of empirical journal articles are mostly White.
Photo credit: Africa Studio/Shutterstock

Is science fair?

You learned in Chapter 2 that empirical journals are the primary place where psychological scientists make their research public. Each empirical journal has an editorial board-- a group of scientists who have power to select the peer reviewers for each article and make the final decision about publication. 

Each step of the way, the stated goals of the publication process are to be fair, to focus on the quality of the science, and to publish the best science possible. But is the process fair?  In light of the renewed energy around anti-racism, a timely new study sheds light on racial diversity in the psychology publication process. The researchers asked, Is race an important variable in the scientific publication process?

The team coded all empirical journal articles that appeared in the most prestigious journals in psychology (over 26,000 articles). There were two journals each from social, cognitive, and developmental psychology. The authors measured:

• how often race was studied as a subject in these articles
• the race of the participants in the studies
• the proportion of the authors of scientific articles were White vs. Non-white
• the proportion of the editors of the journals themselves were White vs. Non-white

Here are some of the results of this published study of published studies, as summarized by the Association for Psychological Science's trade publication:

The results suggest that there is a hierarchy in psychology research that determines who and what gets published. “We are not saying anyone has bad intentions,” said Roberts [one of the study's authors]. “It’s a systemic problem across many fields, and psychological science is no exception.”

First, across the past five decades, articles in psychological journals that highlight race have been rare, and although developmental and social psychology journals have published a growing number of these articles, they have remained virtually nonexistent in cognitive psychology.

Second, most journals have been edited by White editors, under whom there has been a notable dearth of published articles highlighting race and racism.

Third, many of the publications that highlight race have been written by White authors, who employed significantly fewer participants of color.

Let's parse these claims. 

Questions

a) What kind of a claim is it (frequency, association, or cause) to state that "across the past five decades, articles in psychological journals that highlight race have been rare"

[And what's "rare"? The published report says that "only 5% of publications highlighted race" (p. 4). This 5% figure is an average across cognitive journals (less than 1%) and social (5%) and developmental journals (8%)]

b) Let's take the second claim, "most journals have been edited by White editors, under whom there has been a notable dearth of published articles highlighting race and racism"

The quoted text above provides two claims in one sentence. What is the frequency claim in the quoted text? What is the association claim in the quoted text? What are the two variables in the association claim?

c) Now for the third claim, "many of the publications that highlight race have been written by White authors, who employed significantly fewer participants of color."

As with b), the quoted test provides two claims in one sentence, a frequency claim and an association claim. What are these? 

d) In Chapter 1 of the 4th Edition (available Fall 2020), you will read about Merton's norms of science--a set of values that the community of scientists usually endorses and strives  to follow. Here they are, with short descriptions (for a full review, see Chapter 1 or this summary). 

Universalism: Anyone can do science as long as they follow the accepted criteria for science, which are established in advance, are objective, and are accessible to all.
Communality: Share! The process of science, including data, methods, and products, should be free and open for all to use. 
Disinterestedness: The quality of the science should be the only thing that matters, not the prestige of the person or place it was conducted. 
Organized Skepticism: Verify everything. Critical peer review matters, every study should be replicated, and criticism of one's evidence is expected and welcome. 

Roberts and his coauthors argued that psychology is too White. Which of Merton's norms does this result seem to violate, and why? 

Suggested answers:

a) This is a frequency claim--a statement about the frequency of the variable, "highlighting race" across 26,000 journal articles.

b) The frequency claim is about the proportion of journal editors who are White vs. a Person of Color (POC). The association claim is about a link between two variables: whether a journal has a White editor or not, and whether that journal published articles highlighting race or not.

c)  The frequency claim is about the proportion of article authors who are White vs. POC. The association claim is about a link between two variables: whether the article was written by a White author or not, and whether that article included POC as participants or not.

d) Open to discussion!

Here is a summary of this work.

Empathy and social justice

The more privilege people have, the less empathy they have for people lower in status. But that can change with increased perspective-taking. 
Photo credit: Clay Banks/Unsplash

The spring of 2020 has seen sustained, worldwide, democratic protests, led by Black Lives Matter and demanding social justice and police reform in the U.S.  After this spring, American support for Black Lives Matter and police reform are suddenly higher than it has ever been.  Psychologist Jamil Zaki asks in this opinion piece, Why now? Why did Americans react more strongly in 2020, after police killed George Floyd, compared to other highly publicized instances when police killed other unarmed Black people, including Breonna Taylor, Tamir Rice, Philando Castille, Michael Brown, and Stephon Clark?  

Dr. Zaki points to empathy as the answer. He writes, 

In the past few weeks, many people have opened their eyes to suffering they had previously ignored. Much credit for this should go to the activists and organizers who have made it harder to look away. 

Dr. Zaki supports his argument with evidence from psychological research on empathy, power, and structural change. For example, teams of psychological researchers have investigated how being in powerful positions is associated with being less able to understand others' suffering:

In one series of studies, the psychologist Michael Kraus and his colleagues measured people’s socio-economic status, as well as their ability to decipher emotions in pictures and in-person interactions. People higher in status were less accurate about other people’s feelings. More recent work has replicated these results and also found that high-status individuals make more errors when trying to take other people’s perspective.

a) What are the variables in Dr. Kraus's research? Were these variables measured or manipulated? (Hint: There are two studies--take one at a time.)

b) Given your answer to question a), were these studies correlational or experimental?

Here are two more brief research descriptions (the links go to the original empirical studies): 

Higher-status individuals display less interest when talking with strangers, and report less concern for the suffering of others. These gaps play out in racial contexts as well. In another study, Kraus found that high-income white Americans overestimate racial economic equality more than black Americans or low-income white Americans.

c) What about the studies above--what are the variables? Are the studies correlational or experimental? (Hint: There are two studies--decide the variables in each study separately.)

Zaki argues that one reason higher status people have less empathy is because the powerful are less motivated to think about others. In contrast, less-powerful people need to pay attention to others in order to function well in society. He writes, "This is one way privilege works its way into our minds. Not only are privileged people exempt from material struggles, they can comfortably ignore everyone else’s."

Here are two more brief study descriptions that Zaki uses in support of his argument:

[Power] expands the change a person could make while narrowing the aperture of who they truly see. But this is not inevitable. When powerful people choose to empathize, they become more cooperative and more invested in justice. In one particularly relevant series of studies, Emile Bruneau and his colleagues asked members of low-power groups to “perspective give,” sharing their stories, and high-power individuals to perspective take, paraphrasing what they’d heard. These dialogues increased connection and positive regard between groups — not by ignoring existing power structures, but by reversing them.

d) In the study by Bruneau and colleagues, what were the variables? Can you tell if the variables were manipulated or measured?  

e) What do you think--was Bruneau's study an experiment or a correlational study? Can it support the causal claim that "these dialogues increased connection and positive regard between groups?"

f) Dr. Zaki's descriptions of the studies are short on detail, likely the result of working under a word-limit and writing for a popular audience. Try following the links to one of the studies linked in Dr. Zaki's article. Are you able to access the full text of the study easily? If not, can you access the journal using your university's library? When you read the full text of the original study, were you correct about its correlational or experimental nature?