Everyday Research Methods

Changing minds

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

 

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

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

"The problem with optimism"

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

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

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

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

The article continues: 

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

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

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

The study above was a simple experiment. 

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

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

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

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

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

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

Now read more about the theory:

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

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

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

Childhood physical activity and teen depression

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

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

Here's some detail from the article: 

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

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

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

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

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

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

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

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

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

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

DV: xxx

IVs:           beta

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

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

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

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

 

Replication update: Mueller-Lyer Illusion (Ch 14)

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

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

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

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

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

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

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

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

Some sports psychology behind the Eagles' Super Bowl win

 

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

 

 

Guess what's contagious in chimpanzees?

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

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

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

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

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

[...]

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

[...]

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

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

SHAPIRO: Could she tell why this was happening?

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

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

BARBER: Yeah.

SHAPIRO: Is that what you're telling me?

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

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

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

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

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

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

Questions

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

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

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

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

Prick up your ears! (Oh, I forgot...you can't!)

Most humans have only vestigal muscles that could help them prick up their ears like this cute pooch. Photo: Mary Swift/Shutterstock

The adorable dog in the photo is paying attention.

"Lifting the ears up straight is, in almost every species, a clue that the animal is putting some work into it. They're paying close attention, they're concentrating," says Steven Hackley, a researcher with the University of Missouri.

What about in humans? Except for a rare few, most of us are unable to move our ears in any direction at will. When we’re paying close attention we might lean in or furrow our brow, but we don’t make our ears stand up.

However, humans do seem to have some vestigial muscles around their ears, and these might be employed when we’re trying to pay close attention. Here’s how NPR’s Science section summarized it:

…people do have certain muscles around the ear that never get used, except by those people who are able to deliberately wiggle their ears as a party trick.

Recently, Hackley and some colleagues looked to see if they could record subtle electrical signals in those muscles, by putting electrodes on them.

[…] Twenty volunteers got wired up with electrodes around their ears and sat in a lab surrounded by speakers.

They were told to pay attention to a certain audiobook, which was picked to be engaging. Andreas Schroeer of Saarland University says this audiobook was full of "fun little trivia that people could actually motivate themselves to listen to."

After a little while, though, other sounds faded in, at different volumes. These were picked to be distracting podcasts. "One of them was actually about the history of podcasts," says Schroeer, who notes that people later reported how much difficulty they had in listening at different times.

As the listening task got easier and harder over the course of this experiment, the researchers saw changes in the electrical activity in the ear muscle that, in animals, perks the ear up.

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

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

b) Which type of design was it: posttest only? Pretest-posttest? Repeated measures? Or concurrent measures? Explain your answer.

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

d) Given the results of this study, what would be a good experiment to run next? Why?

Suggested answers:

Variable name	Levels of this variable	Is this manipulated or measured?	Is the variable an IV or DV?	For the IV: was it manipulated as independent groups or within groups?
Level of difficulty in hearing the audiobook	No difficulty (no distraction), some difficulty, (a quiet distraction) most difficulty (a louder distraction)	Manipulated	IV	Within groups
Degree of activity in ear-raising muscles	Low to high	Measured	DV	n/a

b) It is a repeated- measures design because the IV was within-groups and the DV (level of activity in the ear muscles) was measured repeatedly across different IV levels.

c) Possible results pattern:

You can check your sketch (and mine) against the published results, which are open-access in Frontiers in Neuroscience.

d) Your answers may vary, but if you read the full story, you can see what other researchers were saying about the study, and perhaps get some ideas for future research there.

Do Bonobos have theory of mind?

How does the design of the study make it possible to test if bonobos have theory of mind? Photo: Sergey Uryadnikov/Shutterstock

Typically-developing human children point. Pointing can mean that I want to make sure (for example) that you notice the baby rabbit outside, or I want to tell you that you dropped your fluffy scarf on the floor. The simple act of pointing can mean that I have a “theory of mind”—in this case, I believe that you are not experiencing the same perceptions I am. It is a way of establishing joint attention for better communication. 

So far, there’s been little evidence that chimpanzees or gorillas point in a way that reveals theory of mind. But another primate species, bonobos, might have the ability, according to a new study. It’s an example of a repeated-measures design and a small N design.

The study found that bonobos would (helpfully) point at a hidden grape if their human researcher couldn’t see it..

Here’s how the work was summarized by NPR’s Science section:

The study involved three male bonobos, all living at an education nonprofit called the Ape Initiative. During each experimental trial, [researcher] Townrow sat across from one bonobo, who was in an enclosure, but looking through an opening covered by mesh.

The bonobo watched as a helper placed a treat, such as a grape, under one of three blue cups that were lined up in a row.

"We established a co-operative context to this task because if I knew where the treat or the food item was hidden, I would reveal it and then the bonobo would be able to receive that as a reward," he explains.

Sometimes, Townrow could see what was happening when the treat got placed under a cup. Other times, his view was obscured by a barrier, so he didn't know where it was.

No matter what he had or had not seen, Townrow would briefly scan the cups, saying, "Hmmm…where's the grape?" and then wait for ten seconds.

It turned out that when Townrow had enjoyed an unobscured view of the treat being hidden, the bonobos usually sat still and waited [during the 10 second gap].

But when his eyes had been behind the barrier, which blocked his view and made it so he couldn't see which cup had been picked as the hiding place, the bonobos tended to point their fingers through the mesh and tap towards the right cup.

Make a mental image of the scene, with a bonobo on one side, a researcher on the other (sometimes hidden by a partition), and a third human handling the three cups, and hiding the grapes, in the middle.

1. This is a small-N experiment, and these types of designs almost always use within-groups independent variables (IVs). What is the IV in this study and what are its levels?
   
   
2. What is the dependent variable (DV) in this study?
   
   
3. What makes this a within-groups design?
   
   
4. Is this study closer to a repeated measures or concurrent measures design, and why?
   
   
5. Why was it important for the researcher, in both occluded and unoccluded conditions, to pause for 10 seconds and say “hmmm…where’s the grape?” Mention one of the four big validities in your answer.

 

 

Selected answers:

1. The IV was what the experimenter could see: There were apparently two levels: either the experimenter behind the mesh could see the three cups on a particular trial, or his view was occluded.
2. The DV was whether the bonobo pointed to the cup where the treat was hidden (or not).
3. It is a within-groups design because the bonobo participants were exposed to both levels of the IV (some trials had vision occluded and some trials had un-occluded vision)
4. It is a repeated- measures design because the IV was within-groups and the DV (pointing or not) was measured repeatedly across several trials.
5. It was important for internal validity—they needed to be sure that the two conditions were the same in all respects except for whether the vision was occluded or not.

The empirical  study  appears in the Proceedings of the National Academy of Sciences.

Stressed? Writing a to-do list might help

To do: Decide whether each study's variables are manipulated or measured. Check! GoodStudio/Shutterstock

I wouldn't be able to get through my week without a to-do list. And now my habits are validated by this new story about research on to-do lists, which comes from the BBC News. 

The journalist summarizes a series of studies for the article. It provides a great opportunity to practice deciding if a study is correlational or experimental.

Here's one quote: 

In a study of employees at a German IT company, those who had unfinished tasks left over at the end of the working week were more likely to think about their work problems over the weekend compared with those who were more or less on top of their workload. ...Those with unfinished tasks were also more likely to find their sleep was disturbed over the weekend.

a) What are the variables in the study above? What are the levels of each variable? Are the variables manipulated or measured?

b) Given your answers to a) above, Decide whether this study is correlational or experimental. 

 

Next the journalist asks,

So, what can you do to stop these whirring worries from keeping you awake?

You could count sheep. You could read a book for a while [...]. Or you could turn the light on and make a list of all those things you need to do. [...]

 You might think this is the last thing that would work, but  a study conducted in the US found it was surprisingly effective.

In fact, when Michael Scullin, director of the Sleep Neuroscience and Cognition Laboratory at Baylor University in the US asked one group of volunteers to write a list just before bed of everything they'd achieved that day, and a second group to write a to-do list, all about the tasks they had to do complete tomorrow and in the next few days, it was this second group who subsequently fell asleep more quickly.

c) What are the variables in this new study? What are the levels of each variable? Are the variables manipulated or measured?

d) Given your answers in c) above, Decide whether this study is correlational or experimental. 

 

The journalist also gives an indication of the effect size in Scullin's study:

... how much quicker? Nine minutes, no less.

And the journalist also addresses the study's construct validity--citing its  physiological variable: 

The scientists didn't rely on the volunteers' own assessment of their sleep (which isn't always accurate), but confirmed when they were awake and asleep through a type of sleep study known as polysomnography. This involves attaching sensors to a person's head and to other parts of the body in order to monitor their brainwaves, breathing and movements.

 

For more practice, here's one more study on to-do lists, as described by the journalist:

It is best to list every specific task, rather than to use general headings, even though it will make your list longer. Professor Scullin's study found that busy people who created lists of more than 10 tasks fell asleep an average of 15 minutes faster than people who didn't write out to-do lists. They also fell asleep six minutes faster than those who only compiled short lists. So, make it comprehensive.

e) What are the variables in this study? What are the levels of each variable? (Hint--the IV here has three levels--be sure you name all three!) And, are the variables manipulated or measured?

f) Given your answers in e), Decide whether this study is correlational or experimental. 

g) Sketch a bar graph of this study's results.

h) Can this study support the journalist's claim that "It is best to list every specific task, rather than to use general headings"? Why or why not? 

The most fulfilling jobs in America

The story reported that members of the clergy are most likely to score high on all four questions.
Photo: Diego Cervo/Shutterstock

Before you read on, make a prediction: Which U.S. jobs would you predict would be the most fulfilling?  In this story from the Washington Post column,"Department of Data" you can explore some fascinating data on U.S. job satisfaction.

In a fun twist, the article also talks about how the construct of "fulfilling work" is measured. (It's not everyday that we get to celebrate construct validity: hooray!)

The data come from the CEV--the Civic Engagement and Volunteering Supplement, which is part of the Census Bureau’s Current Population Survey.

Here's how the journalist introduces the data:

In 2021 and in 2023, the researchers behind the CEV asked whether you agree or disagree with these four statements:

• I am proud to be working for my employer.
• My main satisfaction in life comes from work.
• My workplace contributes to the community.
• I contribute to the community through my work.

The two years of surveys give us enough responses to start doing some serious analysis — or, in the grand tradition of this column, some less-than-serious analysis.

The Post column presents data (click here and scroll down) showing that agreement with these four items increases with age and with education. 

In general, the figures and charts of this story are fun to explore--but they're under copyright, so I can't paste them here. Here are some of the journalist's summary points. You'll see that which profession comes out on top depends on the question. 

The workers most likely to say they are proud to be working for their employer and that they gain satisfaction from work are — surprise! — the self-employed.The self-employed who are incorporated — a group that often includes small-business owners — are almost twice as likely as private-sector, for-profit workers to strongly profess pride in their employer.

Government and nonprofit workers fall somewhere in the middle on those [pride and satisfaction] questions. But they rank at the very top on “My workplace contributes to the community” and “I contribute to the community through my work.” Local government workers, who include teachers, take the top spot for strong agreement on both, followed by nonprofit workers. Private-sector, for-profit workers again lag behind.

The jobs that do worse on these measures [of contributing to the community] tend to be in manufacturing or other blue-collar production and extraction jobs, or at the lower-paid end of the service sector. Folks in food services (e.g., bartenders and food prep), janitorial roles and landscaping, and personal services (e.g., barbershops, laundry and hotels) all struggle to find greater meaning in their work. Though some in better-paid service jobs also struggle by some measures — think sales, engineering or software development.

The story indicates that members of the clergy report the most positive answers across all four of the satisfaction items.

a) Do the four items used in this story (included above in this blog post) have face validity as a measure of job satisfaction? Why or why not?

b) Click to the story and scroll to find the line graph that shows agreement with the four items over the lifespan. What do you notice? What do you wonder?

c) One of the concepts in Chapter 5 is internal reliability, or internal consistency, which is relevant when researchers use multiple items to measure the same thing. In this story, the journalist kept the four satisfaction items separate. But based on what you see in the two line graphs, do you think the four items would have internal reliability? That is, would they likely correlate with each other and have a strong Cronbach's alpha?

d) Students often mistrust self-report questions like these. However, in my opinion, the demographic and job category data provide criterion validity evidence that these four questions are construct valid--that is, that people are not just lying or making things up. Specifically, I think the data shows known groups criterion validity for these four items. Can you explain why? Review what known groups criterion validity is, and apply that definition to this new example. 

e) A friendly reminder: Frequency claims (such as "local government workers are most likely to think their workplace contributes to their community" need not just construct validity, but also external validity. How would you ask about that for this data set? Do you think this data set is externally valid--why or why not? 

For even more fun, the column also includes some charts from a different set of data, showing the happiest, most meaningful, and most stressful daily activities (including spiritual pursuits, volunteering, exercising, and working), as well as charts indicating the happiest places (such as places of worship, one's own home, others' homes, and so 0n). Data-loving psychologists (and research methods students) will enjoy exploring these figures.