Everyday Research Methods

Replication: "Don't ditch the laptop just yet"

A mini meta-analysis found that the average effect size comparing laptop to handwritten notes was negligible. Credit: insta_photos/Shutterstock

One of the major empirical examples in Chapter 10 (in the 3rd and 4th editions of the textbook) is a study that tested whether laptop or handwritten notes are better for people's learning from a video lecture. As you may recall, the study (Mueller & Oppenheimer, 2014) found that people who took notes by hand generally remembered more--they scored better on conceptual questions (though not better on factual questions) about a TED talk they had watched. 

Last month, a replication study was published in the journal Psychological Science--the same journal that published the original Mueller and Oppenheimer study. The replication was lead by Dr. Heather Urry of Tufts University. The coauthors on the paper are her entire research methods class, who conducted the work as a class project (How cool is that? Shout out to Dr. Urry's students!). 

The work is an excellent example of a replication study. It was covered in a few media outlets . Here is one summary, as reported by the APS website:

But although previous work suggested that students who write notes by hand may retain information better than those who type them out on a laptop, attempts to replicate this research indicate that taking notes longhand may offer no measurable benefit. 

“We found only small, statistically nonsignificant differences in quiz performance as a function of note-taking medium,” whether students used a laptop or pen, wrote APS Fellow Heather L. Urry (Tufts University) and colleagues in Psychological Science. 

Here's some coverage of the same study, in a blog post by Dr. Regan Gurung, on Psychology Today:

There was no difference in how well the students performed on the factual recall questions as a function of how they took notes. This was similar to the 2014 study. The difference, and failure to replicate, came when Urry et al. analyzed performance on the applied questions. Again, there was no difference in scores based on note-taking style.

In the APS summary, it is reported:

Urry and colleagues reached this conclusion through a direct replication of a 2014 study by Pam A. Mueller (Princeton University) and APS Fellow Daniel M. Oppenheimer (University of California, Los Angeles), in addition to a mini meta-analysis of eight related studies. 

a) As a review, what were the IV (and its levels) and DVs in the original study? (hint: There are two DVs)

b) What does the term "direct replication" mean in the previous quote?

c) How does direct replication compare to a conceptual replication? (You'll find these terms in Chapter 14)

 

Now let's talk about the term "mini meta-analysis" in the quote, "...in addition to a mini meta-analysis of eight related studies."   Here's a summary from the Psychology Today blog:

Urry et al. (2021) also found 8 similar studies, that also aimed to replicate the 2014 results.  The studies compared also used similar methodologies with slight variations. The effect of taking notes longhand was not statistically significant.

d) Do you think the eight studies  in the mini meta-analysis were direct replications or conceptual replications? How can you tell? 

e) Now explain, in your own words, how they might have done the mini meta-analysis? What would the authors have done, once they found the eight studies? (Why do you think it was called "mini"?)

f) Dr. Urry's team preregistered their replication study--what does preregistration mean in this context? 

g) While the original Mueller and Oppenheimer study was covered by multiple popular press outlets (such as here, here, and here), the replication study has, so far, not really broken through to the popular press. Why do you think that is? 

You should check out forest plots of the mini meta-analysis!  Here's the link to the journal article. If you're locked out, you can access a preprint of the replication study here. 

People believe claims of sexual harassment more when women are attractive

The coded stereotypicality of the drawing was a dependent variable in one of the studies. Photo: Courtesy of Dr. Jin Goh. Citation: Goh, J. X., Bandt-Law, B., Cheek, N. N., Sinclair, S., & Kaiser, C. R. (2021). Narrow prototypes and neglected victims: Understanding perceptions of sexual harassment. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspi0000260

Sexual harassment (unwelcome and inappropriate sexual remarks or physical contact) harms people in schools and workplaces. One potential problem with sexual harassment is whether people who report it are taken seriously. Unfortunately, when people report sexual harassment, their claims are not always believed, which means perpetrators can go uninvestigated and victims' harassment might continue. 

The press recently covered an important series of studies that investigated the role that physical appearance plays in the reporting (and believing) process. One of the summaries appeared in the Seattle Times; another  appeared on the APA website.  The Seattle Times journalist summed up the studies like this: 

The study found people are more apt to believe sexual harassment claims by women who are young, “conventionally attractive,” and appear and act feminine. Women who don’t fit that prototype not only are less likely to be believed, but also are presumed to be unharmed by harassing behavior, the study said. 

The APA website does a better job detailing the methods:

The researchers conducted a series of 11 multimethod experiments, involving more than 4,000 total participants, designed to investigate the effect a victim’s fit to the concept of a typical woman had on participants’ view of sexual harassment and the consequences of that mental association.

In five of the experiments, participants read scenarios in which women either did or did not experience sexual harassment. Participants then assessed the extent to which these women fit with the idealized image of women, either by drawing what they thought the woman might look like or selecting from a series of photos. Across all the experiments, participants perceived the targets of sexual harassment as more stereotypical than those who did not experience harassment.

The details above describe a set of experiments (do you know why they are experiments?)

a) What was the main independent variable (IV) in the five studies above? What were its levels? 

b) What was the main dependent variable (DV) in the five studies? What do you think its levels were? 

c) The image (with the two drawings, above) that goes with this post came from the first study (Study 1), in which participants read a scenario in which " women either did or did not experience sexual harassment" and made a "drawing what they thought the woman might look like". Which drawing do you think came from each condition? 

d) The drawings from Study 1 had to be coded for stereotypical femininity. Since this variable required coding, inter-rater reliability would have been important. How do you think the researchers established inter-rater reliability for their coders? (Hint: you can find relevant information in Chapter 5.)

e) Sketch a small bar graph depicting the results of these studies, based on the journalist's description. 

Now here's some more description from the APA website:

In the next four experiments, participants [read] ambiguous sexual harassment scenarios, such as a boss inquiring about a woman’s dating life. These scenarios were paired with descriptions or photos of women who were either stereotypical or not. The participants then rated the likelihood that the incident constituted sexual harassment.

f) What was the main independent variable (IV) in these four studies? What were its levels? 

g) What was the main dependent variable (DV) in the four studies? What do its levels seem to have been? 

h) Sketch a small bar graph depicting the results based on the journalist's description. 

i) Assess the plausibility of this claim: "Being more stereotypically feminine causes people to think a woman's sexual harassment claim is true"  Apply the three causal criteria (covariance, temporal precedence, and internal validity). 

Instructors: Students are likely to enjoy reading the studies in the original journal article, which appears on my list of journal articles that are student-friendly.

Interrogating Buzzfeed: Does Instagram really have "huge effects" on our psychology?

Do the studies really support cause and effect? And are those effects "huge"? Photo: Julia_Tim/Deposit Photos

This Buzzfeed headline is eyecatching and dramatic: Instagram Has Some Pretty Huge Effects On Our Psychology. Here's Everything We Know So Far.

I see two important issues to interrogate in this headline. First, it's a causal claim ("Instagram has some...effects on our psychology"). Second, it's making an argument about effect size ("pretty huge effects"). Let's see if they pan out. 

The article focuses on body image and mental health. Here's an example:

The research into how Instagram affects our psychology is relatively new, but there is a growing body of evidence to say that it has a profound effect on the way we perceive ourselves and the world around us.

Does the research support such a causal claim? Here's one study that the journalist summarized: 

Earlier this year, the UK's Royal Society for Public Health (RSPH) and the Young Health Movement published a report that looked at the impact of different social media platforms on mental health.

The report was based on a survey of over 1,400 British 14-to-24-year-olds that asked about the positive and negative implications of each social media platform for health and wellbeing.Instagram scored well on its ability for self-expression, but it was linked with some of the worst scores for sleep loss, body image concerns, and FOMO.

It's not completely clear from this summary what questions were included on this survey. However, a survey is unlikely to have manipulated exposure to Instagram, so it can't support the claim that "Instagram has implications for how people perceive themselves and the world around them."

What about the effect size of the associations? The journalist didn't report the data from the study, so we can't evaluate whether or not the effect was really "huge" or "profound." We'd have to search out the original paper for that. 

How about this other study, summarized later in the article: 

In a study published last year, Cohen and her co-authors found that it wasn’t so much social media use itself that impacted how people perceived their bodies but rather how they were using it.

Following body- and appearance-focused accounts on Instagram — as opposed to neutral accounts dedicated to things such as travel — was associated with idealizing thin bodies, obsessive surveillance of one’s own body, and striving for thinness.

a) Four variables are listed in the study above (Following body-....) What are they?

b) Were these variables measured or manipulated, do you think?  (Check your hunches here, by reading the abstract of the Cohen study.)

c) Why can't this study support a claim such as "following body-focused accounts impacts how people perceive their bodies"? Apply the three criteria for causation. 

d) Does the journalist provide any indication of the effect size of this association? If not, what data might have helped you decide if the link was "huge"?

Let's read a bit further in the journalist's article to see if we can find some experimental evidence. How about this one:

However, Cohen is hopeful that the positive body movements emerging on the platform will have far-reaching benefits for how women reflect on their own physiques.

She has recently submitted a paper for publication demonstrating that among 195 young women exposed to “body-positive” images, mood, body satisfaction, and body appreciation were all improved significantly.

The wording in the example above suggests that there was a manipulated variable, because it reads "among 195 young women exposed to 'body-positive' images'". When I read that, I picture that some people were exposed to these images, and some were not. 

e) Imagine you were going to conduct a conceptual replication of this experiment. What was the independent variable? What would its levels be? 

f) What would the dependent variable(s) be? (Hint: The journalist mentions three).

g Sketch a little bar graph of the results of your experiment--picking one of the DVs to graph. In your view, how far apart would the means and error bars have to be for you to be convinced that the effect is "huge"?

h) Would this experiment have the potential to support a causal claim? What confounds would you need to control for? 

Suggested answers to selected questions

c) Covariance: Yes--apparently this questionnaire study found a link between following more body-focused accounts and idealizing thin bodies more. 

Temporal precedence: Nope--this was a questionnaire study that measured both variables at the same time. We can't know if the IG accounts came first, or if people who idealize thin bodies more are more likely to want to follow body-focused accounts.

Internal validity: Also nope. In this correlational study we can't rule out every possible third variable. For example, maybe people with a history of eating disorders are both more likely to follow body-focused accounts and are more likely to idealize thin bodies.

d) No correlation was reported here--too bad! It would be nice if the journalist had given us some benchmarks indicating how much more likely people were to idealize thin bodies....perhaps a percentage change or a correlation coefficient, and a 95% CI for that change or coefficient. 

 

Thanks to UD student Emily Hogan for suggesting this article for the blog!

 

 

Don't believe it?

Macadamia nuts are in the same evolutionary family as peaches. (or are they?) Credit: gresey/Deposit photos

People's tendency to believe, share, and act upon disinformation (fake news) is a serious threat to civic engagement. Psychologists are studying what makes people more or less likely to believe false claims. Here are some excerpts from a brief article published in Buzzfeed, which summarized a few studies about people's willingness to believe what they read.

"What we find in our research really robustly is that we can push around people's feelings of truth using things like photographs and audio quality, and even the ease of pronouncing someone's name," said [researcher] Newman.

Here's an example of a study on how perceptions of "truthiness" can be influenced:

In 2012 Newman co-authored another paper with studies that found photographs can inflate the sense of "truthiness" that people get from reading a statement.

The sort of photographs that the researchers used for this research were "non-probative", meaning that they didn't actually contain any information about the claims being made.

One study ...found that if participants were given a trivia statement such as "Macadamia nuts are in the same evolutionary family as peaches", they were more likely to believe the statement was true if a photograph of macadamias simply appeared alongside the sentence [compared to no photo].

Bonus: Stephen Colbert (who coined the term "truthiness") also summarizes it in this clip. 

Questions about the first example:

a) What are the two variables in the macadamia nuts study? Name each variable, its apparent levels, and indicate if it is measured or manipulated.

b) Do you think the "photo appearance" variable was manipulated as between-groups or within-groups? Explain your reasoning.

c) Sketch a small bar graph depicting the results of this study, as described by the journalist. 

d) Ask three questions about the statistical validity of the macadamia nut study--your three questions should address effect size, precision, and replication. (Note--the answers to your questions might be present in the original empirical article.)

 Here's a second example:

A study published in March found that people were less likely to believe an academic researcher on the radio if the audio quality of the recording was poor or glitchy.

The study involved 97 participants, who were instructed to listen to recordings of different pieces of science news and assess the quality of the research and the researcher who was reporting it.

Half of the audio recordings were edited to sound as though they were recorded on a bad phone line. The listeners who heard these recordings rated the credibility of the researcher and the studies they were discussing as significantly less favourable than recordings with higher audio quality.

Questions about the second example:

e) What are the two variables in the audio quality study? Name each variable, and indicate if it is measured or manipulated.

f) Do you think the "audio quality" variable was manipulated as between-groups or within-groups? Explain your reasoning.

g) Sketch a small bar graph depicting the results of this study, as described by the journalist. 

h) Ask three questions about the statistical validity of the audio quality study--your three questions should address effect size, precision, and replication. (Note--the answers to your questions might be present in the original empirical article.)

i) Now consider the internal validity of the audio quality study: Why would random assignment be important in this study? 

Selected answers (use these answers as models to help answer the audio quality questions, if needed)

a) One variable is the appearance of a photo; its levels are "photo or no photo." It is manipulated.
A second variable is "level of belief in the trivia statement" or "rated truthiness." Its levels probably range from "low belief" to "high belief". It is measured. 

b) For a single fact, the presence of a photo would have to be manipulated as between groups--people would have to see the fact either with or without the photo. If they were exposed to both levels of the variable (a within-groups design), people might wonder why they are being asked to rate the same fact two times.  However, in the study itself, participants were exposed to multiple facts, some of which were accompanied by a photo and some of which were not....so that makes the variable within-subjects when you consider all of the facts together. 

c) Sketch a small bar graph depicting the results of this study, as described by the journalist. 

d) Ask three questions about the statistical validity of the macadamia nut study--your three questions should address effect size, precision, and replication. (Note--the answers to your questions might be present in the original empirical article.)

For effect size, you could ask questions such as, "how far apart were the truthiness ratings?" "How big is the difference between the photo and no-photo group?" or "What is the d? (or other effect size)"
For precision you could ask, "what is the 95% CI of the effect size estimate?"
For replication you could ask, "has this effect been replicated in other studies? What do those other studies say?"

I would like to thank my student Emily Hogan for sharing this example and Buzzfeed story with me!

 

Should scientists change out of pajamas?

Does the study's design support the causal claim the journalist attached to it?
Photo credit: R.O.M/Shutterstock

Some journalists simply cannot resist attaching a causal claim to a study where it doesn't belong. Here's an example from the trade magazine, Inside Higher Ed, which reprinted a piece from Times Higher Education. The headline reads, "Scientists urged to change out of pajamas." 

The first paragraph begins:

Academics who are tempted to remain in pajamas during the working day should think again, says an Australian study that has linked the practice to a deterioration in mental health.

 

a) There are actually two claim-like statements in the first paragraph. What are they? (Remember that advice is a form of causal claim). 

b) What seem to be the two variables in the association and causal claims above? 

The journalists summarizes the study this way: 

The study, which drew on 163 responses from staff at five medical institutes in Sydney, also delved into the reality of working at home for researchers. [...]

Some 28 percent of scientists said they wore pajamas at least once a week -- a cohort who were twice as likely to report worsened levels of mental health than those who dressed normally each day, according the study, by David Chapman and Cindy Thamrin [...]

Given the survey format of this study, we can assume that all the variables in this study were self-report. To learn more about this relatively simple study, you can visit the open-access original journal article here, and you can see the full, original text of the survey here. 

Let's work through the four big validities. 

c) Construct validity: Look through the original survey (here) and find the variables that measured pajama-wearing and mental health assessment. What do you think--how well do these variables seem to be measured? (Guess what? You're assessing face validity.) 

d) Construct validity: When you view the original text of the survey (still here)  you might be a bit surprised by the lighthearted tone of some the survey questions. For example, they ask about the "typical home working environment" they included the option, "hiding in the bathroom." When they asked people what they wear during remote meetings, one of the options was, "none of your business, camera turned off."  How might this casual tone affect the construct validity of the variables being measured? Do you think it will it affect the accuracy of self-reports?

e) External validity: The journalist refers to "scientists", so that seems to be the population of interest. The sample was described this way by the journalist:

The study, which drew on 163 responses from staff at five medical institutes in Sydney...

In the empirical article, there is more detail about the sample: 

An invitation to participate was emailed to all staff, students and affiliates of the Woolcock Institute of Medical Research, Sydney, and later extended to other medical research institutes in Sydney (Garvan Institute, Children’s Medical Research Institute, Centenary Institute, Brain and Mind Centre).

Can this study generalize from this sample to the population of interest? Why or why not? (Don't be tempted to focus on the size of the sample here. Remember that it's not the size that matters for external validity)

f) Statistical validity: Here is the effect size of the relationship between wearing pajamas and mental health: 

[people who wore pajamas at least once per week] were twice as likely to report worsened levels of mental health than those who dressed normally each day

In the empirical article, the rates of poorer mental health were described as 59% (for pajama-wearers) vs. 26% (for non-wearers). What do you think of the strength of this effect? What more information would you like that is relevant to statistical validity?

g)  Internal validity. Let's evaluate whether the study can support the causal claim, "wearing pajamas put the scientists at risk for poorer mental health?" In order to support a causal claim, we need to conduct an experiment. Was this an experiment or a correlational study?  Explain your answer. 

h) Internal validity: Now let's apply the three criteria for causation. 

The study does show covariance, because people who report working in pajamas did have twice the rate of mental health decline, compared to those who did not.

What about temporal precedence? Does the method establish which variable came first in time? 

What about internal validity? Can you think of a third variable (some "C" variable) that might be associated with both wearing pajamas and having worse mental health? 

Now make a conclusion--does the study support the advice to "change out of pajamas?"

i) How might you design a true experiment to study the potential causal effect of pajamas on mental health? What variable would you need to manipulate? What variable would you need to measure? 

 

 

 

Frequency claims: Fitter and better rested during lockdown

One of the positive changes people reported was being able to spend more time with family. Photo: Steve Skjold / Alamy Stock Photo

The first peer-reviewed studies about the COVID-19 pandemic  are starting to appear in journals, and journalists are starting to summarize them in the popular press.

Let's explore this summary of a survey study conducted in Scotland and summarized for a general audience by the scientist herself, in The Conversation. The scientist writes, 

 ... for some people lockdowns have provided an unexpected opportunity to make positive changes to their lives...

Here are some details: 

In May 2020, my colleagues and I surveyed over 3,000 people in Scotland to find out what positive changes people had made in their lives during the lockdown period. 

And some of the key results:

More than half the people we surveyed reported these changes for the better: being more appreciative of things usually taken for granted (reported by 83% of participants), having more time to do enjoyable things (by 67%), spending more time in nature or outdoors (by 65%), paying more attention to personal health (by 62%), doing more physical activity (by 54%) and spending more time with a partner or spouse (by 53%).

Each of the above results constitutes a frequency claim, because it talks about the level, or percentage, associated with one variable. Consider this one, for example: "doing more physical activity (by 54%)". The variable here is "doing more physical activity under the lockdown (or not)."

Now let's use the four big validities to systematically interrogate this frequency claim, "54% of people in this Scottish survey reported doing more physical activity during the lockdown." 

a) Construct validity: How do you think they measured the variable "doing more physical activity"? (Self-report? Observational? Physiological?) . What are your initial impressions of, or questions about, how well this variable was measured? 

b) Construct validity: Let's say you wanted to establish the criterion validity (See Ch 5) of the (presumably self-report) measure of "doing more physical activity".  What behavior should self-reports of this variable correlate with, if the self-report measure is valid? 

c) Statistical validity: The point estimate, 54%, is one element of statistical validity. What about the precision of this estimate? That is, what is the 95% CI, or margin of error of this estimate? It's not provided in the article, but you can visit this calculator to estimate the CI. Do that now. 

d) Statistical validity:  Replication is another aspect of statistical validity. The scientist-author cites at least one more, similar study that came to a similar conclusion: 

An Australian study (still in preprint, meaning its findings are yet to be reviewed by other scientists) also sought to find out similar information. In a survey of over 1,000 people, it found that 70% of participants reported having experienced at least one positive effect of the pandemic. 

e) External validity:  The sample for this survey was N =3000. Don't get distracted by the size of the sample (remember--it's not the sample size that helps establish external validity). Instead, what do you need to ask in order to decide whether the study's sample can generalize to their population of interest (Consult Ch 7)?

f) External validity: I visited the peer-reviewed article (here--open access) and found this information about their sampling technique.

The target population of the survey was adults, aged 18 years or older, currently residing in Scotland, who were interested in sharing their experience of positive change. Participants were primarily recruited through social media advertisements on Facebook and Twitter which directed participants to the online survey on Qualtrics. 

What kind of sampling did they apparently use in this study? (Name a sampling technique that is discussed in Chapter 7). 

What is their population of interest?

Does the sampling technique they used allow them to generalize from their sample to their population of interest? Why or why not? 

g) Internal validity: In a frequency claim, nobody is making a causal statement, so we don't actually need to interrogate internal validity.

h) Now that you've looked at all four validities, what do you think about the quality of this study? Make a nuanced comment about its strengths and weaknesses. 

 

P.S. Thanks to Marianne Lloyd for the reference!

Musical skill: Why does an early start seem to help?

People might start musical training early because they are living in musical families or show early evidence of musical talent. 
Photo credit: Artem Furman, Alamy stock photo

What accounts for musicality? Some people are highly skilled and accomplished musicians, while others express no aptitude. A recent correlational study tried to understand why some people are more musically accomplished. A summary of this research appears on the APS website, and the original empirical article appears in the journal Psychological Science. 

The researchers used a sample of 310 professional musicians as well as a large sample of twins (which they called "amateur musicians"). The twin sample came "from an existing research project, the Study of Twin Adults: Genes and Environment (STAGE)." The twin sample enabled the researchers to estimate genetic and environmental similarities on musicality.

The participants completed measures of musical aptitude (e.g., tests of their accuracy at pitch and rhythm) and musical achievement (e.g., having performed publicly or won prizes). Researchers also asked each person what age they had started learning music. They asked how many hours per week they'd practiced at different points in their lives. The journalist summarized one of the core results:

By comparing the results from these two independent studies, the researchers were able to show that an earlier start age is associated with musical aptitude, both in amateurs and professional musicians...

Questions for students appear in gray throughout this post:
a) Sketch two scatterplots of this result described in the green quoted text above--one scatterplot for professional musicians and one for amateurs. The x and y axes will be the same on the two scatterplots--be sure to label these carefully. Will your scatterplots show positive or negative relationships? 

 

Now, it might make sense that starting musical training at an earlier age is associated with better musical aptitude simply because of total practice time. That is, people who start training earlier have practiced more hours, and practicing more hours is usually associated with more musical skill. In other words, we might sketch this, third-variable proposal:

According to the study, was total practice time a third variable, explaining why starting age was associated with greater aptitude? Consider this full quote from the journalist's summary: 

By comparing the results from these two independent studies, the researchers were able to show that an earlier start age is associated with musical aptitude, both in amateurs and professional musicians, even after controlling for accumulated practice time.

This quote means that even though practice time is usually associated with greater aptitude, this was not the reason why starting earlier was associated with greater aptitude. The results from a regression table might have looked like this:

DV/criterion variable: Musical aptitude
IV/predictor variable:                 Beta
Age starting musical training          -.21**
Total practice time                               .03           
(The betas above are simplified versions of those reported in the original journal article, Table 3, for the musician sample)

Question
b) Write a sentence describing what the beta of -.21** means. 

Therefore, it wasn't total practice time that accounted for the benefit of starting musical training early. On to the next explanation:

[The researchers] then evaluated starting age in a manner that accounted for the genetic data from the STAGE study.  

The results indicate that genetic factors—possibly related to musical interest and talent—have a substantial influence on the age individuals start music practice and their future musical aptitude. When controlling for familial factors, namely shared genetic and environmental influences, such as a home environment that is steeped in music, there was no additional association between an earlier start age and musicality. 

So, in other words, the researchers were proposing this explanation:

And they found results that supported this explanation. The statistical calculations that are used in twin studies are actually beyond the scope of this course and book--so you'll have to trust the researchers here. Suffice it to say that when they controlled for genetic interest or talent, there was no longer an association between age at starting training and musical aptitude.

c) Consider the construct validity of two of this study's variables: Musical aptitude and age at starting training. What questions could you ask to evaluate the construct validity of these measures? 

d) Here is a challenge question: In the empirical journal article (Table 2) the researchers reported that the correlation between between total practice hours and musical aptitude in the professional sample was r = .05, while the same association in the amateurs (i.e., the twins) was r = .30. One reason the correlation was weaker in the professional musician sample might have been restriction of range. Sketch two scatterplots--one for each sample but with the same x and y axes--that illustrates how restriction of range might have limited the correlation in the professional musician sample. 

Meta-analyses: Strength training treats depression and anxiety

Strength training seems to improve both depression and anxiety, according to two meta-analyses. Photo credit: romanchazov27/Deposit Photos

Many of us start the new year resolved to improve our exercise habits. Exercise, of course, is great for our physical health, and we also know that aerobic exercise (i.e., "cardio") can improve mood. The benefits of aerobic exercise on depression and mood have been well-documented by several studies (here's an example of one meta-analysis). 

A recent article in the Washington Post describes the benefits of strength, or resistance, training for depression. The piece provides a good example of how a journalist covers meta-analysis for a popular audience.

The author himself suffers from depression, and starts his article by explaining his own workout strategy:

It’s strength training — the muscle-building, body-toning and sometimes grunt-inducing workout — that has helped transform my chronic mental illness into better mental health. In fact, once I began a regular regimen of resistance training, the positive shift in my mood, my confidence and my self-image was so pronounced that I switched careers.

Of course, while an anecdote about one person's experience can be compelling, it doesn't carry the same weight as a systematic observation. It's possible that the author was a biased observer of his own improvement. Therefore, we need experiments that randomly assign people to different exercise groups to test whether resistance training has a causal effect on depression and mood. 

It turns out that several researchers have, in fact, studied the effect of strength training, enough to collect and combine:  

[Dr. Jacob] Meyer was among the authors of a recent meta-analysis of the link between resistance training and depressive symptoms ... The study, published in the June 2018 issue of JAMA Psychiatry, analyzed 33 clinical trials for the effects of resistance exercise on depression. Results showed that resistance exercise “significantly reduced depressive symptoms” among research participants.

a) In your own words, what does a meta-analysis do?

b) In the meta-analysis described in the paragraph above, there were 33 studies, all of which were experiments. Given the context, what was the independent variable in each of these 33 studies? What was the dependent variable? 

c) Reread the description above, which states that strength training "significantly reduced depressive symptoms". This phrasing does not indicate the meta-analytic effect size (that is, the effect size they obtained by averaging the effect sizes of all 33 studies). To see that effect size, you'd have to turn to the original empirical article, here.  The article is open-access and the forest plot you will find there depicts  the mean, or overall, effect. What is the overall effect size? 

The meta-analysis above showed that strength training can help improve depression. What about a related mental health condition, anxiety? Turns out there's a separate meta-analysis on that:

Resistance exercise doesn’t affect depression alone; research shows that it can reduce symptoms of anxiety, too. For a 2017 study published in the journal Sports Medicine, Gordon and his colleagues conducted another meta-analysis of 16 studies involving 992 total participants; it concluded that resistance training “significantly improves anxiety symptoms among both healthy participants and participants with a physical or mental illness.”

d) How many studies were in this meta-analysis on strength training and anxiety? Estimate how many participants were in each study.

e) What would have been the independent variable in each of these studies? What would have been the dependent variable?

f) Once again, the journalist explains that resistance training "significantly improves" anxiety symptoms, but we don't hear about the effect size. The original empirical article  is behind a paywall. You might be able to access it from your own library. If you can't find it, I'll tell you that the mean effect size (d) of the studies was 0.31, 95% CI [0.17, 0.44]. What information do you need to evaluate this effect size? Is the effect strong or weak? 

As a bonus, the researchers are exploring why strength training might improve depression and anxiety. That is, they are exploring potential mediators of the relationship between strength training and mental health. Here is the first of two mediator explanations, as summarized by the journalist:

One notable finding was that participants showed an improvement in their mood regardless of whether they grew physically stronger from the exercises. ... “Perhaps it might be the sense of accomplishment and confidence that comes from exercising, rather than the achievement of actual strength gains, to explain why study subjects felt better,” Meyer says.

And the second:

Resistance training, like other exercise, induces the release of a protein called BDNF, or brain-derived neurotrophic factor, into the hippocampus region of the brain. Among other functions, the hippocampus is responsible for mood regulation, and in people who are depressed, it shrinks up to 25 percent of its normal volume. The release of BDNF triggers the growth of new brain cells in the hippocampus, restoring it to its full size and improving communication between cells.

“That’s the physiological theory behind why you feel so good after working out,” says Jason Sawyer, an assistant professor of wellness and exercise science at Rhode Island College in Providence.... 

g) Choose one of the mediator descriptions above and sketch a figure depicting the mediator argument. Use a box for each variable, and arrange the boxes in a triangle shape, using the figures at the end of Chapter 9 as a model. 

 

Selected answers

c) The overall (meta-analytic) effect size was d = 0.66 95% CI [0.48, 0.83]. Therefore, strength training groups scored about 2/3 of a standard deviation better than control groups, on average. To evaluate how strong this effect is, it might be helpful to compare the effect of strength training to some other treatments for depression, including cognitive-behavioral therapy or medication. (A 2013 meta-analysis found that CBT's effect on depression is at least d = 0.53, 95% CI [0.43, 0.62].)

d) There were 16 studies and a total of 992 participants, so that's about 165 participants per study. 

e) Each study would have used exercise condition as an IV and anxiety level as a DV. 

f) As you can see, the overall (meta-analytic) effect size of resistance training on anxiety was not as strong as the overall effect size of resistance training on depression (the two CIs do not overlap). This pattern suggests that strength training does more for depression than for anxiety. 

g) Here's how you might sketch one of these mediation proposals:

[strength training condition] -->  [sense of accomplishment] --> [improved mood]

Be quiet! Psychological effects of noise

In one of the studies, researchers found that students whose classrooms were on the train-track side of the school made less progress in reading than those on the quieter side.  Credit: Alon Adika/Shutterstock

The weekly Freakonomics podcast recently put together an episode about the psychological impact of loud noises in our environment. You can listen to it (or read the transcript) here. 

The host and guest review several studies with a variety of methodologies, which include some excellent examples of quasi-experiments.

The introduction to the transcript reads, 

The modern world overwhelms us with sounds we didn’t ask for, like car alarms and cell-phone “halfalogues.” What does all this noise cost us in terms of productivity, health, and basic sanity?

 

Example I.

The first example comes from animal behavior and behavioral ecologist Peter Tyack, who studied whales in the Bay of Fundy. The Bay provided lots of food for the whales, but as a site of many shipping channels, the same place always had the background noise of ships going to and fro. 

Researchers like Tyack had never really thought much about this background noise. It was just there. But then all of a sudden it wasn’t. The change came with the 9/11 terrorist attacks.

TYACK: All of a sudden, the ships that were plying the ocean in that area stopped. 

This drop in ship traffic was only temporary. But it happened to coincide with some other whale research that was happening in the Bay of Fundy:

TYACK: Researchers from the New England Aquarium had been sampling feces from whales to look at stress hormones. It wasn’t part of a noise experiment at all. 

It was just your standard whale-feces research.

TYACK: But what they found was that if you compared the stress hormones in whales before 9/11 and after 9/11, their stress hormones actually went down after 9/11. 

a) In the quasi-experiment described here, what is the quasi-independent variable? What are its levels? What is the dependent variable?

b) Let's assume that the researchers collected and tested whale feces nearly every day, several days before 9/11 and several days afterwards, as well. What type of quasi-experiment is it (nonequivalent control groups, nonequivalent control group prettest-posttest, nonequivalent control groups interrupted time series, nonequivalent control groups interrupted time series)?  

c) Sketch a graph of the results of the whale study. 

Example 2

Here's another example: This one took place in a public school in New York City. The school was built right next to an elevated subway track, so kids in one of the school were exposed to the loud noise of subway trains approximately every four minutes. 

One side of the school building faced a nearby elevated subway; the other side faced away. Bronzaft matched second-, fourth-, and sixth-grade classrooms on the quiet side and on the noisy side, where a passing train would push the sound readings from 59 decibels to 89 decibels. Then she compared the average reading scores from the two sets of classrooms.

BRONZAFT: And the children exposed to the transit noise were nearly a year behind in reading by the sixth grade, and the teacher had difficulty teaching.

d) In the quasi-experiment described here, what is the quasi-independent variable? What are its levels? What is the dependent variable?

e)  What type of quasi-experiment is it (nonequivalent control groups, nonequivalent control group prettest-posttest, nonequivalent control groups interrupted time series, nonequivalent control groups interrupted time series)?  

f) Sketch a graph of the results of the reading study. 

g) In the podcast, they make two caveats:

It’s worth noting that Bronzaft’s subway research, as with similar studies at airports and elsewhere, have some limitations. For one thing, Bronzaft couldn’t randomly assign students to the noisy versus quieter classrooms. There were also relatively few classrooms to choose from, so there might have been some natural variation.

Which of the four big validities is the first caveat about? Which of the four big validities is the second caveat about?

 

 

 

Will an "awe walk" do wonders for your well-being?

The study asked all volunteers to go on a daily walk, but randomly assigned only some of them to seek out experiences of awe.  Credit: Studio Grand Web/Shutterstock

A recent headline reads, "An 'awe walk' might do wonders for your well-being."  Here is the journalist's summary of an empirical journal article.

The journalist starts by explaining that psychological researchers already know that exercise can improve mood. And we also already know that a feeling of awe can improve mental health. The journalist introduces the concept:

A somewhat nebulous emotion, awe generally is defined as the sense that you are in the presence of something larger and more consequential than yourself and that this something is mysterious and ineffable. 

In the study,

They concentrated on people in their 60s, 70s and 80s, an age when some people can face heightened risks for declining mental health.

Read the journalist's description of the study, identifying the study's independent variable, dependent variables (there are more than one DV), and control variable(s):

The scientists asked 52 of the study volunteers if they would mind adding a weekly 15-minute walk to their normal schedules. ...Fresh, baseline studies of their mental health showed they were psychologically well-adjusted as well, with little anxiety or depression.

The scientists randomly divided these volunteers into two groups. One, as a control group, was asked to start walking, at least once a week, for 15 minutes, preferably outside, but given few other mandates.

The members of the other group likewise were asked to walk once a week, but also were instructed in how to cultivate awe as they walked.

Not surprisingly, they found that the awe walkers seemed to have become adept at discovering and amplifying awe. One volunteer reported focusing now on “the beautiful fall colors and the absence of them among the evergreen forest.” A control walker, in contrast, said she spent much of a recent walk fretting about an upcoming vacation and “all the things I had to do before we leave.”

The researchers also found small but significant differences in the groups’ sense of well-being. Over all, the awe walkers felt happier, less upset and more socially connected than the men and women in the control group. The volunteers in the control group reported some improvements in mood, but their gains were slighter.

Questions

a) What was this study's IV? What were its levels? Was the IV manipulated as independent groups or within groups? 

b) Name three distinct DVs from this study, based on the journalist's description.

c) Which type of experiment is this? Posttest only? prettest-posttest? repeated measures? concurrent measures?

d)  Pick one of the DVs you listed in (b). Sketch a bar graph of this DVs. (Hint: you are going to need to plot four data points--do you see why?).

e) Construct validity question: What's going on in the paragraph that starts, "Not surprisingly, they found that the awe walkers seemed to have become adept at discovering and amplifying awe."? Use the term "manipulation check" in your response, and explain why a manipulation check is relevant to construct validity.

f) Statistical validity question: The journalist describes "small but significant differences". What does this phrase mean? Use the term "effect size" in your answer. 

g) External validity question: Can we generalize from this sample, or is generalization unknown? defend your answer (but please don't refer to sample size!)

h) Internal validity question: What role did random assignment play in this study? What if somebody says, "some people just like going for walks"--would you consider this to be a threat to internal validity? Why or why not? 

The journalist also mentions another DV. The researchers asked people to take a couple of selfies during their walks:

Over the course of the eight weeks, the size of awe walkers’ countenances shrank in relation to the scenery around them. Their faces grew smaller, the world larger. Nothing similar occurred in the photos from the control group.

i) It sounds like the researchers did not preregister prediction about the selfie content. Is lack of preregistration a potential problem?