Everyday Research Methods

Increasing vaccination rates in evidence-based ways

One of the evidence-based techniques was to "say because," as these ads illustrate.
Images used with permission from the Government of Jersey

Jersey is a self-governing island in the English Channel off the northwest coast of France. Its population is around 108,000. After the COVID-19 vaccine became widely available, the island's government started campaigns to encourage people to get their shots.  Rather than making up communication strategies that just seemed right, they decided to design strategies based on social psychological evidence.  This article describes the studies they used and how they applied them. Let's take a look. 

Strategy: Say because

One strategy mentioned in the article involves the word "because."

The study:

Ellen Langer demonstrated the persuasive power of the word “because” in her famous studies in the 1970s. People waiting in line to use a photocopier were asked “Excuse me, I have five pages. May I use the copier?” 60 percent agreed. But when the request was accompanied with a reason — “May I use the Xerox machine because I’m in a rush?”—94 percent complied.

[...] even when the word because was followed with a completely meaningless reason — such as “May I use the Xerox machine, because I have to make copies?” just as many people said yes. The study wonderfully demonstrates the motivational influence of the word “because” and its continually reinforced association with the good rationales that typically follow it.

The application:

But just because people pay more attention to the word “because” doesn’t mean that what comes after is less important. Hence our two-step “because-then-reason” strategy. First, we prominently included the word “because” in our vaccination outreach campaigns. Then we followed the word “because” with a personalized reason meaningful to that particular messenger (and their audience).

The images in this post show examples of how the posters highlighted the word because. 

Questions about this strategy:

a)  Before basing an intervention on an empirical study, we'd better be sure that study was experimental (rather than correlational). Why?

b) What were the variables in Langer's study, and what were the levels?  Do you think that Langer's study was experimental or correlational? What are the clues? 

 

Strategy: Build ownership

The study:

A 2020 mega-study conducted by several dozen researchers led by Wharton School professor Katy Milkman demonstrated how a simple change to a text message increased uptake of flu shots. Reminder messages that included the words “your vaccine is reserved for you” boosted rates by as much as 11 percent compared to standard messages such as “protect yourself by getting a flu shot.”

The application:

Milkman’s study provided the inspiration for us to adopt a similar approach in the emails and texts we sent on behalf of the Government of Jersey to citizens whose turn it was to receive their Covid-19 vaccine. The words “your vaccine” were designed to give readers that sense of personal ownership.

c) What were the variables in Milkman's study? What were the levels of each variable?

d) Do you think Milkman's study was experimental or correlational? Explain your answer

e) Why do you think the journalist described this as a "mega-study"? Follow the link to test your hunch. 

f)  The journalist seems to mention only two levels of the independent variable in Milkman's study (the words “your vaccine is reserved for you” versus “protect yourself by getting a flu shot.”). But in the Milkman et al.'s actual study, there were many more levels. Check the link to the study to find out how many other conditions the researchers tested. 

 

Note to Instructors: Figure 1 of Milkman's study shows the effect sizes and CIs for all of the different interventions they tried. It's a really nice way to introduce these statistical concepts. 

 

Evidence-based tips for procrastination

What do the studies suggest about why people procrastinate? Photo credit: Timothy Hodgkinson/Shutterstock

The BBC magazine, Science Focus, collected a set of studies on procrastination that might help students at the beginning of a new school year.  The journalist opens with a couple of simple frequency claims. 

[If you procrastinate,] you’re not alone: an estimated 20 per cent of adults (and above 50 per cent of students) regularly procrastinate.

Then the article addresses three topics: 1. Is procrastination a sign of poor time management? 2. Is procrastination unhealthy? 3. How can we stop? Let's take each topic and apply some research methods content as we go.

1. Is procrastination a sign of poor time management?

The journalist quoted psychologist Fuschia Sirois, who explained that contrary to what many people believe, procrastinators aren't more likely to be bad at time management. Instead, she explains, it's about emotion regulation:

“At its core, procrastination is about not being able to manage your moods and emotions. Although many think impulsivity and self-control are the problems – and they do play a factor – underneath is a poor emotional response.”

As Sirois explains, every person faces stressful situations, demanding tasks that trigger brain activity that involves a brain region known amygdala. And it’s the amygdala that processes emotions and signals threats, capable of prompting a ‘fight or flight’ response linked to procrastination.

“Interestingly, people who say they are chronic procrastinators tend to have larger grey matter volume in the amygdala,” says Sirois.

“This means they will also be more sensitive to the potential negative consequences of their actions, leading to more negative emotions and procrastination.”

a) What were the variables in the amygdala study? Was this study correlational or experimental? 

b) Sketch a scatterplot (or bar graph) of the results Dr. Sirois describes here.

 

2. Is procrastination "bad for your health"?

The journalist implies causality with this headline, and with lines like these: 

...procrastination can cause a lot more problems than missed deadlines. Over decades Sirois has examined the impact of chronic procrastinating on a person’s health, her findings worrying at best – and downright terrifying at worst.

Let's see if the evidence can support these causal claims. He quotes Dr. Sirois again, who stated, 

“People who chronically procrastinate – people who make it a habit – have higher levels of stress and a greater number of acute health problems. They are more likely to have headaches or insomnia or digestive issues. And they’re more susceptible to the flu and colds.”

Even more alarming, Sirois has found that procrastination is a factor that can lead to hypertension and cardiovascular disease, with chronic procrastinators more likely to put off healthy behaviour such as exercise.

c) Is Dr. Sirois describing correlational or experimental evidence here? (Explain your answer by specifying the variables she mentions).

d) Why can't the evidence described by Dr. Sirois support the journalist's assertions about "the impact of chronic procrastinating"? Specify the temporal precedence problem and the internal validity problems here (when you discuss internal validity, specify a particular third, or "C" variable that might correlate with both A and B). 

3. How can we stop procrastinating?

In this section, the journalist describes some intervention studies targeting procrastination.

For example, one compelling Psychological Science paper described how downsizing larger metrics of time (think 48 hours instead of 2 days, or 10,950 days instead of 30 years) can make events seem more immediate, prompting people to engage in upcoming tasks.

You can follow the underlined link in the quoted text (above) to see that this paper described several experiments (for example, the abstract states, "we manipulated time metric".

e) Given the journalist's description (and the empirical study's abstract), name the independent variable in this experiment and name its levels.

f) Name the dependent variable in this experiment.

g) Sketch a bar graph of the study's result, as described by the journalist. 

h). Because this study was an experiment, it is more likely to support causation.
Think about something you have to do in the near future. How can you apply the results from the Lewis and Oyserman study in Psychological Science to that task, to prevent yourself from procrastinating on it? 

 

Selected Answers

a) One variable was "saying you are a chronic procrastinator or not" and the other was "volume of grey matter in the amygdala". Both variables must have been measured, so this was a correlational study.  

b) One axis should have "grey matter volume in amygdala" and the other should have "level of chronic procrastination"; the scatterplot should have a positive slope, but you don't have information on how strong the relationship was, so you can't really know how spread out the dots should be. 

c) This is likely correlational evidence. They are measuring (not manipulating) people's procrastination habits. They are measuring (one cannot manipulate) people's acute health problems, susceptibility to the flu, hypertension, and cardiovascular disease. 

d) We can take acute health problems as an example. Even if there is an association between chronic procrastination (A) and acute health problems (B), we still don't know if the procrastination led to the health problems (A to B), or if the health problems lead to more procrastination (B to A). In addition, there could be some outside variable such as life stressors or poverty (C) that is associated with both procrastination (A) and health problems (B). 

e) The independent variable is apparently "time metric" and the levels were hours vs. days, or perhaps days versus years. 

f) The dependent variable seems to be "how immediate events seem to be"

g) The x-axis should have hours and days, and the y-axis should indicate "how immediate events seem to be". The bar for "hours" should be higher than the bar for "days". 
This study seems to suggest that if you have homework due in two days, you might be more likely to get started on it if you say to yourself, "My homework is due in 48 hours." 

 

Frequency claims: Remembering brand logos

This project provided several frequency claims you can analyze.
Photo credit: LukaFunduk/Deposit Photos

Before you read on, take out a pencil and draw the Apple logo (no peeking at your own device!) Now try to draw the Adidas logo, too (no peeking at your feet, either). 

Which one was easier? How confident are you in the accuracy of your drawings?

After you've drawn these two logos, visit this website, where you can compare your own drawings to those of other people. You can also see the official logos for Apple and Adidas. How well did you do?  More importantly, how well did the people in their study do?

This website presents data from a study of Americans who were asked to draw a variety of brand logos. The website introduces the project:

...we asked over 150 Americans to draw 10 famous logos from memory as accurately as they could. Based on more than 1,500 drawings created over a period of 80 hours, the results reveal that, far from being stamped perfectly in our collective memory, these ubiquitous emblems largely exist as fuzzy visions in our mind's eye. 

The text on the website reports:

Overall, 16 percent of people drew near perfect logos, and 37 percent were good but not perfect. As we would expect, the more complex the logo, the less likely people are to remember it in full.

Across the 10 brands (which included not just Apple and Adidas, but also Starbucks, Foot Locker, Burger King, and Domino's), the percentage of people who sketched near-perfect logos changed. For example, the website reports that 20% drew near-perfect Apple logos, whereas 12% drew near-perfect Adidas logos.  

You can evaluate certain methodological aspects of this project. For example, here's a quote from their discussion of the Apple logo results:

Interestingly, a smaller proportion of people in our experiment put the bite on the wrong side of the apple (22 percent)

a) Wait, was this study really an experiment? why or why not?

The article made some frequency claims. For example, 

One in 5 people thinks the Foot Locker referee wears a hat (he doesn't), and nearly half of people believe the Starbucks mermaid does not wear a crown (she does).

b) Why can each of these two claims be considered frequency claims? 

c)  When people make frequency claims, external validity (also known as generalizability) is pretty crucial. Think about the external validity of this project. Do you think the study they conducted is able to support the claim that "one in 5 people thinks the Foot Locker referee wears a hat?" What information do you need to know in order to evaluate the external validity of this claim?

d) Now consider statistical validity. How precise are the estimates on this website? The study estimated that 20% of the sample drew near-perfect Apple logos. But what's the precision of that estimate? In other words, what's the 95% CI, or margin of error? You can compute it here, if you know the sample size (which, just to remind you, was around 150). 

What was the MoE you found?  What would happen to that MoE if the sample had been larger? (Caveat--MoE computations actually depend on having a random sample, which we don't have, so these estimates are not going to be perfect.)

e) Finally, consider construct validity. The study needed to decide how accurate each person's sketch was. One of their footnotes reads, "accuracy based on independent ratings by a panel of marketing professionals." How do you think they obtained these ratings--what might their procedure have been?

f) Using the large matrices of drawings, make some comments about the construct validity of their operationalization of "accuracy". 

 

Thanks to Marianne Lloyd for sending me this page!

Really? Can "hot baths protect your heart?"

The data came from Japan, where hot baths (whether at home, in local bathhouses, or in hot springs) are a well-loved practice. Photo credit: nitis.s/Shutterstock

Sigh....even the imprimatur of Harvard Medical School doesn't stop writers from tacking causal headlines onto correlational  studies. 

In this example, the journalist writes, "The study is only observational [i.e., correlational] and doesn't prove that daily tub bathing staves off heart problems".  Yet the headline, "Can hot baths protect your heart?" sounds pretty causal to me. 

Here's the journalist's summary of the method:

Researchers analyzed self-reported health and lifestyle information from more than 30,000 middle-aged people in Japan. Participants responded to a questionnaire at the start of the study and were then followed for about 20 years.

And here's the summary of the major results:

Compared with people who didn't take a tub bath more than twice a week, people who took a daily warm or hot bath had a 28% lower risk of cardiovascular disease and a 26% lower risk of stroke. 

This article provides a good opportunity to practice the four big validities. 

a) Name the three major variables in the study, as included in the journalist's summary. 

b) How do you know that the study was correlational? (remember to focus on whether the variables were measured or manipulated)

c) Ask at least one construct validity question about this study. (Hint--start your question with "How well...")

d) Now ask an external validity question about this study. (Reminder--your question should address sampling method, not sample size)

When you consider external validity, it might be relevant to consider that Japan has a well-developed bath culture. Most homes have soaking tubs, and some towns still support a neighborhood sento, or public bathhouse. In addition, many tourist areas and resorts feature beautifully landscaped hot spring baths called onsen. In this study, 2/3 of the participants reported that they bathe in a tub almost daily. What might this bathing culture suggest about the external validity of this study? Would this relationship generalize to a less-bathy culture like the U.S.?  

e) What about statistical validity? The authors report two estimates: One estimate concerns the link between bathing and cardiovascular disease (28%), and another concerns the link between bathing and risk of stroke (26%). The journalist didn't provide 95% CIs--but what does the sample size tell you about the 95% CI's of these estimates? (i.e., will they be less precise or more precise?)

f) Now let's walk through why this study can't support a causal claim. This study does have covariance (baths were associated with lower risk of cardiovascular disease and stroke).

What about temporal precedence--what is this, and why does this study achieve it?

And what about internal validity? Come up with a third variable ("C") that is reasonably associated both with taking more baths ("A"), and with having lower rates of disease ("B"). 

Bonus: The empirical article is open-access and you can read it here. Instructors--it's likely that students would understand this article well enough to read it and critique it.

 

Evidence in favor of U.S. stimulus checks

Which type of quasi-experimental design does this study seem to fit best? The dark line on top represents respondents with children. The red line represents all respondents. The lighter blue line at the bottom represents respondents without children. Credit: Courtesy Patrick Cooney and H. Luke Shaefer. From Poverty Solutions, May 2021, University of Michigan,

Since the COVID-19 pandemic began, several governments, including the U.S. federal government, have distributed cash payments to citizens in an attempt to alleviate the  financial hardship caused by the global shutdown. 

As the New York Times reports here, policymakers wonder if such payments are useful. Now we have data to help address that research question. 

In offering most Americans two more rounds of stimulus checks in the past six months, totaling $2,000 a person, the federal government effectively conducted a huge experiment in safety net policy. Supporters said a quick, broad outpouring of cash would ease the economic hardships caused by the coronavirus pandemic. Skeptics called the policy wasteful and expensive.

[...]

A new analysis of Census Bureau surveys argues that the two latest rounds of aid significantly improved Americans’ ability to buy food and pay household bills and reduced anxiety and depression, with the largest benefits going to the poorest households and those with children. The analysis offers the fullest look at hardship reduction under the stimulus aid.

The full report is available here, and provides some excellent graphed examples of quasi-experimental data. We'll focus on Figure 1 from the report, reprinted above with permission from the authors.

The y-axis on the figure represents the percentage of Americans who "sometimes or often had not enough food to eat in the last seven days." The question is asked by U.S. Census' Household Pulse survey about once per month to a random sample of Americans. The dates of the two U.S. stimulus checks are indicated in the laddered, light-blue vertical lines. Payments occurred in December, 2020 and March, 2021.

Questions

a) The journalist writes that "the federal government effectively conducted a huge experiment in safety net policy".  What is the independent variable in this "experiment?"  What is the dependent variable (i.e., the one depicted in Figure 1)?

b) The independent variable here is not a true IV--it is a quasi-independent variable. What makes this variable quasi-independent?

c) The authors claim that the results from Figure 1 are consistent with the argument that the stimulus checks helped people. The authors wrote in their report: "Based on the speed with which we see hardship fall, we suspect much of this drop was the result of EIP checks, which the federal government was able to quickly deliver to bank accounts for most U.S. households following passage of both bills." Locate the areas on the figure that the authors are referring to. 

d) This quasi-experimental study is probably best described as an interrupted time-series design. What is the "interruption" here? What is the time series?

e) A history threat is one potential internal validity problem in an interrupted time-series design. Explain what a history threat is and also why a history threat might be a problem here. What do you think--to what extent can we be sure it was the stimulus checks, and not some other pair of events, that is responsible for the increased food security depicted here? 

You can see results for other variables in the report here, including figures that tracked people's feelings of anxiety and their feelings of depression. 

 

 

 

 

 

Estimating how many U.S. adults identify as nonbinary

 

Photo credit: nito103/Deposit Photos

Press outlets have been covering a report by the Williams Institute (based at UCLA), a research organization collecting data on demographic information on sexual orientation and gender identity. The quotes in this post come from this summary from PBS.

The new study has estimated the number of LGBTQ adults in the U.S. who identify as nonbinary. (Nonbinary is a term that is often used to describe people whose gender identities are more than the man/woman category; some nonbinary people use they/them pronouns.)  It's important to estimate how many U.S. adults identify as nonbinary (as well as other sexual- and gender-minority identities) because anti-discrimination laws potentially affect them. PBS reports that:

Nonbinary people are part of a “growing subgroup” of the LGBTQ population, Wilson said, adding there’s been greater visibility for nonbinary people. More and more celebrities, like singers Demi Lovato and Sam Smith, and actors Indya Moore and Amandla Stenberg, have talked about identifying themselves as nonbinary or in related terms.

Here's a portion of the PBS summary: 

According to the study, which focused on people ages 18 to 60, nonbinary people make up about 11 percent of the adult LGBTQ population in the country. The study builds on the work of two prior surveys — one on trans adults, [TransPop] the other on cisgender LGBTQ adults [Generations]— to provide more comprehensive data on nonbinary people, a historically underresearched subgroup.

When the researchers extrapolated out form this subsample, they concluded that

There are about 1.2 million LGBTQ adults in the U.S. who are nonbinary.

The data in this report constitute a frequency claim, because they are estimating the level of one variable (that is, nonbinary identity).

First let's talk about the precision of these estimates. The report from the Williams Institute (here) includes 95% CIs for the estimates. For example, the two estimates from the PBS story were presented this way:

Number of nonbinary LGBTQ adults in the U.S., including transgender and cisgender people N [95% CI]	1,219,000 [1,000,000 - 1,481,000]
% [95% CI] of LGBTQ adult population	11.1 [9.3, 13.2]

a) In your own words, describe what the first CI means. Use this definition and template to help you: The 95% CI is meant to capture the true value in the population 95% of the time. The true value in the population might be as low as ____ or as high as ____. 

b) Sometimes the 95% CI misses the true value in the population. What percentage of the time will the CI miss?

Next lets' talk about external validity (generalizability) and sampling. As you consider these estimates keep in mind that there is a distinction between estimating the number of all U.S. adults who identify as non-binary and the number of U.S. LGBTQ adults who identify as non-binary. These values may be different. 

c) Given what you've read so far, what seems to be the population of interest of this study? 

To know if the sample is generalizable, you need to know how they obtained the sample. The PBS summary doesn't say anything about the sampling method used in this study, and neither does the Williams Institute report. To locate information about the sampling, I had to go to the methods sections of the two studies on which the nonbinary study is based: The TransPop study (which studied a sample of transgender U.S. adults) and the Generations study (which studied a sample of LGBTQ U.S. adults). 

In these methods sections, they say that these two studies (TransPop and Generations) worked with the Gallup organization to recruit nationally representative samples of transgender and LGBTQ adults. Gallup performs a Daily Tracking Survey in which they call a national probability sample of 1000 adults, almost every day of the year. These adults are sampled using random-digit dialing, including both landline and cell phone numbers. Gallup asks a variety of questions about well-being, political attitudes, and economic situations. At the end of each  telephone survey, the interviewer asks, “I have one final question we are asking only for statistical purposes. Do you, personally, identify as lesbian, gay, bisexual, or transgender?” Gallup retained the records for people who said yes to this question over one year, and the Generations and TransPop studies contacted these folks later. At this second contact, researchers checked to be sure each person was eligible for the Generations or TransPop study, and if they were, the researchers sent them a survey by mail or email. To be eligible, people had to identify as sexual minority or transgender, be within certain age ranges, and be able to complete surveys in English. 

After people were recruited into the TransPop and Generations studies in this way, they were asked multiple survey questions, including the target question about whether they identified as nonbinary. 

d) In your own words, explain why the procedure used above is likely to lead to a probability (in other words, a generalizable, or externally-valid) sample of LGBTQ adults in the United States. 

e) Reading the description above, which type of random was used here--random sampling or random assignment? 

f) To reiterate, the study concluded that about 11% of LGBTQ people in the United States identify as nonbinary. What method could we use to estimate the percentage of all people in the United States identify as nonbinary? 

g) What do you predict might happen to the estimate of nonbinary people in the U.S. if they redid this study 20 years in the future? 

 

Suggested Answers

a) The 95% CI is meant to capture the true value in the population 95% of the time. The true value in the population might be as low as 1 million or as high as 1.48 million. Importantly, our CI may have missed the true population mean. We don't know if it missed it or not, but we know the probability that our CI missed it--see question b)!

b) the 95% CI will miss the population mean in 5% of cases.  

More information: If we replicated the study, the new estimate and 95% CI will be a little bit different from the first one. But we'd still expect that the replication estimate of the number of nonbinary adults will be between 1 million and 1.48 million. We'd be surprised if our new estimate was a lot lower or a lot higher than this range. 

c) The population of interest is LGBTQ adults in the United States (who have access to a cell phone or landline)

d) The Gallup sample is generalizable because their Daily Tracking Survey goes out to a random sample U.S. adults. Any LGBTQ folks that are called in that survey are therefore also a random sample, and random samples are generalizable.  

e) They used random sampling. (As a reminder, random assignment is used in experiments, to assign participants to different groups.)  

f) One method could be to ask directly of a random sample of US adults. For example, Gallup could potentially add a nonbinary identity question to their Daily Tracking Survey for one year.

Another method was the one used in the present study. Gallup learned that 3.5% of their Daily Tracking Survey sample answered yes to the LGBTQ question, and then, of that subsample, a certain percentage (about 11%) identified as nonbinary. After adjusting weights, they got a percentage, (maybe around .05%) which they multiplied by the entire U.S. adult population, to produce the estimate that about 1,219,000 adults (or between 1 and 1.48 million) identify as nonbinary. To see some of the statistics I quoted here, visit this page)

g) Answers will vary

Claim: "Drinking any amount of alcohol causes damage to the brain"

Photo credit: Dangubic/Deposit Photos

Here's a link to CNN's coverage of a study on alcohol use and gray matter in the brain. The headline reads, "Drinking any amount of alcohol causes damage to the brain, study finds." Let's consider how well the study supports the causal claim the journalist (or the editor) attached to it.  Here's one of CNN's introductory statements: 

In an observational study, which has not yet been peer-reviewed, researchers from the University of Oxford studied the relationship between the self-reported alcohol intake of some 25,000 people in the UK, and their brain scans.

a) The term, "observational study" in this context is synonymous with "correlational study." What are the key variables in the study? What does the quote above tell you about whether the variables were manipulated or measured? 

Here's the next paragraph in the CNN summary: 

The researchers noted that drinking had an effect on the brain's gray matter -- regions in the brain that make up "important bits where information is processed," according to lead author Anya Topiwala, a senior clinical researcher at Oxford.

"The more people drank, the less the volume of their gray matter," Topiwala said via email.

b) The CNN journalist erroneously wrote, "drinking had an effect on the brain's gray matter".  Let's walk through whether this correlational study here can support the journalist's causal claim. The first criterion is covariance, which is about the study's results. Does the study show covariance?

c) The second criterion is temporal precedence. The journalist doesn't provide information about whether the drinking behavior (variable A) was measured first in time, before brain scans measured people's gray matter (variable B). What are the two potential directions this relationship might go?

d) The third criterion is internal validity. There could be some third variable, C, which is associated with both A and B. See if you can come up with a potential C variable that could reasonably correlate with both amount of drinking and reduction in gray matter. 

You might have noticed that the opening to the CNN summary mentions that the study "has not yet been peer-reviewed". 

e) What happens during the peer review process? Do you think journalists should summarize, for the general public, studies that have not yet been peer-reviewed?

f) The empirical report has not yet been published, but you can read more about the study where it is posted on the preprint site, medarxiv. What are some pros and cons of putting one's research on "arxiv" sites like these? Consider issues such as openness of science, peer review, and public interest. 

 

Thanks again, Stephen Chew, for sharing the example!

Cat-in-the-box

Pancetta the cat sitting in Kanizsa square. Photo and research by Gabriella Smith, M.A.

I've shared several examples of research on pet dogs on this blog, so it's about time I present an example of research on pet cats.

If you own a cat yourself, you've probably noticed that they enjoy climbing into 3-dimensional boxes, baskets, and other containers. You might also know that cats also seem to prefer "climbing" into 2-dimensional boxes--that is, they are likely to sit in a simple square that has been masking-taped onto the floor. A recent study took a closer look at this adorable behavior as part of a test of whether cats are susceptible to a phenomenon known as the Kanisza contour illusion.

As illustrated in the photo of Pancetta that accompanies this post, the Kanisza contour illusion consists of four Pac-Man-like shapes that, when arranged just so, are usually interpreted by humans as a square. Researcher Gabriella Smith wondered if cats, too, would fall for the illusion, so she created an experiment to test it. 

An interesting aspect of Smith's study was that the cats participated in their own homes, an approach called "citizen science." In other words, the researchers  "let volunteers test their own cats at home in a standardized way and then send in the results".

Here's a description of the research study, as covered by NPR's All Things Considered.(The original empirical study was published in a journal published  by Elsevier, which means it's very difficult to obtain, even through universities.):

Participants received booklets with instructions on how to print out and cut up paper shapes that could be taped to the floor to create three options: the Kanizsa illusion, the actual outline of a square, and a control that had the Pac-Man shapes facing outward. The volunteers were instructed to videotape their cats as they encountered these stimuli in short tests over six days, while the volunteers wore sunglasses to avoid inadvertently giving the cats any signals with their eyes.

Their results? That the cats chose to sit in the illusory square as often as they did the actual outline of a square — and more than the non-square arrangement.

Here are some questions about the study.

a) This was an experiment. What was the independent variable (IV) and what were its levels?

b) What was the dependent variable (DV)?

c) Was the IV manipulated as independent-groups or within-groups?

d) Which of the four basic experiments does this study seem to have used--Posttest only? Prettest-posttest? Repeated measures? or Concurrent measures?

e) Sketch a graph of the results that were described (hint--put the three IV groups on the x-axis).

f) In the empirical article, the researchers mention that the presentation of the different IV levels was fully counterbalanced, which means that cats in the study were exposed to multiple trials. Why was counterbalancing necessary, and what might counterbalancing have looked like? 

g) The story mentions that "the volunteers wore sunglasses to avoid inadvertently giving the cats any signals with their eyes.". Which internal validity threat was this designed to prevent?

Now here's a detail about the citizen science aspect of the study:

While hundreds of people signed up for the experiment, in the end the researchers got complete data on only 30 cats..... [Smith] notes the number of people who dropped out of this study and says future efforts along these lines may need to be even shorter and easier to ensure that volunteers are able to complete all the required tasks....This latest study, [in contrast to past work on cats] has the advantage of testing untrained cats in a.... natural setting, 

h) Given this outcome, summarize the pros and cons of the citizen science approach to animal research, perhaps adding some of your own.

i) You might be alarmed that the sample size for this study dropped from hundreds of people to only 30, and you might be tempted to think this study has an attrition threat to internal validity. But attrition isn't an issue for internal validity here--can you explain why? 

 

 

An empirical reason to say "Thanks"

Senders of gratitude letters benefit from writing them, and recipients "feel great" when receiving them. Photo credit: Rawpixel/Deposit Photos

This example comes from the subfield of positive psychology, and the study of gratitude. One technique studied by positive psychologists is the practice of writing a gratitude letter to a person who has influenced you.  According to this summary, "There are two excellent reasons for writing a gratitude letter: It will make you feel really good, and it will make the recipient feel great"

The concept is simple: Write a letter to a person whom you'd like to thank. Gratitude letters can be handwritten or typed, emailed or snail-mailed. The important thing is to send them. Gratitude letters can help both the writers and the recipients. 

First, here's the journalist's description of a study about the benefits of writing these letters:

Scientists randomly assigned the 293 participants to three groups: Those receiving psychotherapy, those receiving psychotherapy and participating in expressive writing, or those receiving psychotherapy and participating in gratitude-letter writing. Even in the small study, participants in the gratitude group reported significantly better mental health than the other two groups, even three months after the trial ended.

a) What is the independent variable (IV) in the study above?  What are its levels?

b) What is the dependent variable? 

c) Was the IV manipulated as independent groups or within-groups? 

d) What kind of experiment was this? Posttest only? Pretest-posttest? Repeated measures? or Concurrent measures? 

e) What role do you think the "expressive writing" group played in this study? Use the term "design confound" or "control variable" in your response. 

f) Can this study support the claim that "writing gratitude letters (in addition to psychotherapy) improves mental health"? Apply the three causal criteria. 

g) Ask two questions about the construct validity of the study--one about the IV and one about the DV.

h) Ask a question about the statistical validity of the study. To what populations might we be able to generalize? 

 

What about the recipients? It turns out that people aren't really sure if they should send their gratitude letters. They might feel awkward about it, perhaps surmising that the recipient may not really care about such a thing anyway. But here's a quote about research on receiving these letters: 

On the receiving end, opening a gratitude letter feels even better than you might imagine. Amit Kumar, a social scientist at the University of Texas at Austin, studies the reactions of gratitude-letter recipients.

“It’s not like it makes some people feel great and some people feel just OK and some people feel kind of weird,” he said of his research findings. “Almost everybody is saying that they feel really, really great.”

i) Most psychology studies have at least two variables (because they are testing association or causal claims). But, at least according to the journalist's description above, the study by Dr. Kumar had only one variable. What was it? 

j When Dr. Kumar said “Almost everybody is saying that they feel really, really great.”, he was saying something about the mean of this variable as well as its standard error. What can you infer about these two statistics, based on his quote? 

k) This study can support a frequency claim, because it's about the level of one variable. In your own words, write the frequency claim it can support. 

(Dr. Kumar's study on gratitude letter recipients can be previewed here. You'll see that in addition to asking recipients how they felt after receiving a gratitude letter, they also asked senders to predict how the recipients would feel. The main finding was that recipients felt much better than senders predicted they would feel.)

Make a face

Should you smile, grimace, or neither when you get your COVID jab? Photo credit: Gregory Johnston/Shutterstock

Have you gotten a COVID vaccination yet? If not,  grab a chopstick and read this article before you go. Here's a link to a journalist's summary of a study on the effect of making a face (such as a smile or a grimace) on the pain of an injection. 

The background for the research is the "facial feedback hypothesis:" If you've read Chapter 14 already, you'll recognize this line of work. 

The idea that manipulating our facial expressions can affect our emotions has a long and storied history. There are many advocates of [this] hypothesis, and many critics, too. Indeed, one of the classic findings in the field — that people find cartoons funnier if they hold a pen between their teeth, inducing a smile — recently failed to replicate. This mixed research background was well known to Sarah D. Pressman and Amanda M. Acevedo at the University of California, Irvine, who led the new work, published in Emotion.

Here is some detail about the study: 

The team randomised 231 student participants to hold either a neutral expression, a regular smile (which involves the cheek muscles), a Duchenne smile (which also recruits muscles by the eyes, resulting in creasing around the eyes) or a grimace (which recruits both these sets of muscles, plus those which allow us to wrinkle our brows.

In the study they manipulated the facial expressions unobtrusively, by asking participants to hold a chopstick in different ways:

To induce these expressions, the participants (who thought they were taking part in an investigation of multi-tasking) were given chopsticks to hold in one of four positions between their teeth, and asked to imitate an example photo. Each participant then held their assigned facial position while they received an injection of saline solution into the upper arm, designed to mimic a flu jab. Their heart rates and skin conductance were measured throughout [before, during, and after the jab], as objective markers of their stress levels, and they also reported on levels of pain (anticipated and then actual) and other emotions, such as being jittery or relaxed.

This study was an experiment. Before we get to the results, let's identify some of its features.

a) What was the independent variable (IV) here? How many levels in this IV, and what were they? 

b) Was the IV manipulated as independent groups or within groups? 

c) The journalist writes, "The team randomised 231 student participants to hold either....". Is the word "randomized" here referring to random assignment or random sampling? 

d) There are several dependent variables (DVs) reported here. Name three of them. 

e) Which type of experiment was this? posttest only? pretest-posttest? repeated measures? or concurrent measures?

Now for the results. The researchers collected multiple DVs and the patterns were not the same across all DVs. I will summarize the results for only one DV, self-rated pain:

The Duchenne smiling and grimacing groups gave lower pain ratings than the neutral group while waiting for the injection, during the injection, and immediately after it. The regular smiling group fell somewhere in between.

f) Sketch a graph of this result (placing the DV on the y-axis, as usual).

g) Now a big challenge: Apply Chapter 11's twelve internal validity threats to this example. How does the study's design help rule these threats out? I've done a few of these for you. You'll find Table 11.1 helpful here!

Design confound
Selection effect
Order effect
Maturation	This was a pretest-posttest design that took place in a single session. The single session helps rule out maturation. We can also rule out maturation because all four groups had the same opportunity to "mature."
History
Regression to the mean
Attrition
Testing	All groups were tested multiple times, which means this potential threat was held constant in all groups, ruling it out as an internal validity threat.
Instrumentation
Observer bias
Demand characteristic	The independent-groups design and cover story about multitasking can probably help disguise the true nature of the experiment.
Placebo effect