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When You Are Popular on Facebook, Strangers Think You’re Attractive

2014 February 26
by Richard N. Landers

ResearchBlogging.orgFrom psychology, we’ve known for a while that people create near-instant impressions of people based upon all sorts of cues. Visual cues (like unkempt hair or clothing), auditory cues (like a high- or low-pitched voice), and even olfactory cues (what’s that smell!?!) all combine rapidly to create our initial impressions of a person. Where things get interesting is when one set of these cues is eliminated. For example, if we’ve never met a person in a real life, do we form impressions of people when all we know about them is their Facebook profile? And if so, what do we learn from those profiles?

As it turns out, it can be quite a lot. In an upcoming issue of the Journal of Computer-Mediated Communication, Scott[1] experimentally examined the impact of viewer gender, Facebook profile gender and number of Facebook friends on impression formation, finding that people with lots of friends appear more socially attractive, more physically attractive, more approachable, and more extroverted.

To determine this, the researcher first conducted a pilot study of 600 existing Facebook profiles (although the source of these profiles is not revealed). From that study, it appears that the researcher extracted wall posts at random to create new profiles, replacing the photo with one drawn from a database of photographs with known attractiveness to a photo of moderate attractiveness (all four within 0.05 SD of each other), updating the number of friends to match the needed condition (90-99 for unpopular and 330-340 for popular), and updating the number of photos to match the needed condition (60-80 for unpopular and 200-250 for popular).  This created a database of essentially random Facebook content, although the reason for these particular numbers (or their ranges) was never given.

In the main study, each of 102 undergraduate research participants saw all 4 conditions, in a random order.  All participants viewed the profiles on the same computers in the same research laboratory and completed a survey afterwards.

In analyses, neither research participant gender nor profile gender affected any of the five outcomes: social attractiveness, physical attractiveness, approachability, extroversion, and trustworthiness.  The only effects found were those from the experimentally controlled popularity manipulation, and these effects were noteworthy.


Figure 1 from Scott (in press), p. 9

This figure is a little misleading, since the y-axis doesn’t go down to the bottom of the scale (which was 1), but the effects are still fairly substantial: standard deviations were around 1.2, so these effects range from roughly 0.3 to 1.0 standard deviation – which are moderate to large in magnitude.  It is also a little misleading that the trustworthiness outcome is not within the graph, which was assessed but was not statistically significant.

Despite these very small limitations, this study quite cleanly demonstrates that people form a halo of impressions from relatively small cues, just as they do in “real life.”  Manipulating only the number of friends and number of posted photos led research participants to view the people behind the profiles as more physically attractive, among other outcomes.  This is a critical finding for research examining the value of Facebook profiles and other social media for real-life processes, like hiring or background checks – even relatively small cues can dramatically influence seemingly unrelated judgments made about a person, for better or worse.

  1. Scott, G. G. (2014). More than friends: Popularity on Facebook and its role in impression formation Journal of Computer-Mediated Communication DOI: 10.1111/jcc4.12067 []

Do Recommendation Letters Actually Tell Us Anything Useful?

2014 February 21
tags: , ,
by Richard N. Landers

ResearchBlogging.orgRecommendation letters are one of the most face valid predictors of academic and job performance; it is certainly intuitive that someone writing about someone else whom they know well should be able to provide an honest and objective assessment of that person’s capabilities.  But despite their ubiquity, little research is available on the actual validity of recommendation letters in predicting academic and job performance.   They look like they predict performance; but do they really?

There is certainly reason to be concerned. Of the small research literature available on recommendation letters, the results don’t look good. Selection of writers is biased; usually, we don’t ask people who hate us to write letters for us.  Writers themselves are then biased; many won’t agree to write recommendation letters if the only letter they could write would be a weak one. Among those that do write letters, the personality of the letter-writer may play a more major role in the content than the ability level of the recommendee.  So given all that, are they still worth considering?

In a recent issue of the International Journal of Selection and Assessment, Kuncel, Kochevar and Ones[1] examine the predictive value of recommendation letters for college and graduate school admissions, both in terms of raw relationships with various outcomes of interest and incrementally beyond standardized test scores and GPA. The short answer: letters do weakly predict outcomes, but generally don’t add much beyond test scores and GPA.  For graduate students, the outcome for which letters do add some incremental predictive value is degree attainment (which the researchers argue is a more motivation-oriented outcome than either test scores or GPA) – but even then, not by much.

Kuncel and colleagues came to this conclusion by conducting a meta-analysis of the existing literature on recommendation letters, which unfortunately was not terribly extensive. The largest number of studies appearing in any particular analysis was 16 – most analyses only summarized 5 or 6 studies. Thus the confidence intervals surrounding their estimates are likely quite wide, leaving a lot of uncertainty in the precise estimates they identified. That doesn’t necessarily threaten the validity of their conclusions – since these are certainly the best estimates of recommendation letter validity that are available right now – but it does highlight the somewhat desperate need for more research in this area.

Another caveat to these findings – the studies included in any meta-analysis must have reported enough information to obtain correlation estimates of the relationships of interest. In this case, that means the included studies needed to have quantified recommendation letter quality. I suspect many people reading recommendation letters instead interpret those letters holistically – for example, reading the entire letter and forming a general judgment about how strong it was. That holistic judgment is probably then combined with other holistic judgments to make an actual selection decision. Given what we know about statistical versus holistic combination (i.e., there is basically no good reason to use holistic combination), any particular incremental value gained by using recommendation letters may be lost in such very human, very flawed judgments.

So the conclusion? At the very least, it doesn’t look like using recommendation letters hurts the validity of selection. If you want to use such letters, you will likely get the most impact by coming up with a reasonable numerical scale (e.g. 1 to 10) and assign each letter you receive a value on your scale to indicate how strong the endorsement is. Then calculate the mean of that number alongside the other components of your statistically derived selection system (e.g. GPA and standardized test scores).

  1. Kuncel, N. R., Kochevar, R. J., & Ones, D. S. (2014). A meta-analysis of letters of recommendation in college and graduate admissions: Reasons for hope International Journal of Selection and Assessment, 22 (1), 101-107 : 10.1111/ijsa.12060 []

NSF Report Flawed; Americans Do Not Believe Astrology is Scientific

2014 February 14
by Richard N. Landers

A report from the National Science Foundation recently stated that a majority of young people believe astrology to be scientific, as reported by Science News, Mother Jones, UPI, and Slashdot, among others. Troubling if true, but I believe this to be a faulty interpretation of the NSF report. And I have human subjects data to support this argument.

What the NSF actually did was ask the question, “Is astrology scientific?” to a wide variety of Americans. The problem with human subjects data – as any psychologist like myself will tell you – is that simply asking someone a question rarely gives you the information that you think it does. When you ask someone to respond to a question, it must pass through a variety of mental filters, and these filters often cause people’s answers to differ from reality. Some of these processes are conscious and others are not. This is one of the reasons why personality tests are criticized (both fairly and unfairly) as valid ways to capture human personality – people are notoriously terrible at assessing themselves objectively.

Learning, and by extension knowledge, are no different. People don’t always know what they know. And this NSF report is a fantastic example of this in action. The goal of the NSF researchers was to assess, “Do US citizens believe astrology is scientific?” People were troubled that young people now apparently believe astrology is more scientific than in the past. But this interpretation unwisely assumes that people accurately interpret the word astrology. It assumes that they know what astrology is and recognize that they know it in order to respond authentically. Let me explain why this is an important distinction with an anecdote.

It wasn’t until around my sophomore year of college that I discovered the word “astrology” referred to horoscopes, star-reading, and other pseudo-scientific nonsense. I had heard of horoscopes before, sure, but not the term astrology. I had, as many Americans do, a very poor working vocabulary to describe scientific areas of study. Before that point, in my mind, astrology and astronomy were the same term.

I did not, however, think that horoscopes were scientific. I simply did not know that there was a word for people who “study” horoscopes. If you’d asked me if astrology was scientific before college, I would have said yes – because to me, astrology was the study of the stars and planets, their rotations, their composition, the organization of outer space, and so on. Of course, in reality, it isn’t. Astronomy is a science. Astrology is the art of unlicensed psychological therapy.

When I saw the NSF report, I was reminded of my own poor understanding of these terms. “Surely,” I said to myself, “it’s not that Americans believe astrology is scientific. Instead, they must be confusing astronomy with astrology, like I did those many years ago.” Fortunately, I had a very quick way to answer this question: Amazon Mechanical Turk (MTurk).

MTurk is a fantastic tool available to quickly collect human subjects data. It pulls from a massive group of people looking to complete small tasks for small amounts of money. So for 5 cents per survey, I collected 100 responses to a short survey from American MTurk Workers. It asked only 3 questions:

  1. Please define astrology in 25 words or less.
  2. Do you believe astrology to be scientific? (using the same scale as the NSF study)
  3. What is your highest level of education completed? (using the same scale as the NSF study)

After getting 100 responses (a $5 study!), I first sorted through the data to eliminate 1 bad case from someone who entered gibberish when responding to the first question. Then I tried to replicate the findings from the NSF study by looking at a bar chart of #2 for the remaining 99 people. It was very similar to what NSF reported, as shown below.


Across the sample, approximately 30% found astrology to be “pretty scientific” or “very scientific.” This is lower than the NSF report found (42% for “all Americans”), but this is probably due to the biases introduced by MTurk in comparison to a probability sample of US residents – MTurk users tend to be a little more educated and a bit older. Still a pretty high proportion though.

Next, I went through and coded the text responses to identify who correctly differentiated between astrology and astronomy. 24% of my sample (24 of 99 people) answered this question incorrectly. And given the biases of MTurk, I suspect this percentage is higher among Americans in general. Some sample incorrect responses:

  • Astrology is the study of the stars and outer space.
  • Astrology is the study of galaxies, stars and their movements.
  • the study of how the stars and solar system works.
  • Astrology is the scientific study of stars and other celestial bodies.

These are in stark contrast to the correct responses:

  • Trying to determine fate or events from the position of the stars and planets
  • Astrology is the prediction of the future. It is predicted through astrological signs which are influenced by the sun and moon.
  • Astrology is the study of how the positions of the planets affect people born at certain times of the year.
  • The study the heavens for finding answers to life questions

For those statistically inclined, a one-sample t-test confirms what I suspected: if people generally did not have trouble distinguishing between astrology and astronomy, we would not have seen such an extreme number of incorrect answers: t(98) = 17.032, p < .001.  There is definitely some confusion between these terms.

Next, I created the bar graph above again, but split it by whether or not people got the answer correct.


Quite a big difference! Among those that correctly identified astrology as astrology, only 13.5% found it “pretty scientific” or “very scientific”. Only 1 person said it was “very scientific.” Among those that identified astrology as astronomy, the field was overwhelmingly seen as scientific, exactly as I expected. This is the true driver of the NSF report findings.  Both an independent-samples t-test and a Mann Whitney U test (depending on what scale of measurement you think Likert-type scales are) agree that the differences in science perceptions between those responding about astronomy and those responding about astrology is significantly different (U = 119.00, p < .001; t(97) = 10.537, p < .001).  Massive effect too (d = 2.48)!  Thus I conclude that it is invalid to simply ask people about astrology and assume that they know what that term means.

Various media reports have noted that the NSF report discussed how 80-99% of Chinese respondents in a 2010 Chinese study reported skepticism of various aspects of astrology, interpreting this difference as evidence supporting the decline of US science education. Instead, I suspect that this difference only reveals that in Chinese, the words for astronomy and astrology are not very similar.  86.5% is right in the middle of the range of findings from the Chinese sample, although I expect a broader US sample than MTurk would probably be a little lower. But at the least, I feel comfortable concluding that we are safe from Chinese scientific dominance for at least another year.

So why might this effect have been worse for young people? My guess is that the long-term effect is exactly the opposite from what the NSF reports. I suspect that young people are in fact more skeptical of astrology than ever before – and I believe this skepticism is driven by reduced exposure in youth. Young people just aren’t as likely to hear the word astrology in connection to horoscopes anymore, and probably less likely to hear about horoscopes at all because virtually no one reads newspapers anymore (which I also suspect is where most people were historically first exposed to them; I remember learning of horoscopes for the first time in the Tennessean many years ago as a child in Nashville). As people age, they’re more likely to hear the term “astrology” and say, “Oh, astrology means horoscopes? That’s obviously fake! Nothing like astronomy!” Perhaps the demise of print journalism isn’t as bad as we thought!

If you’d like to take a look at my data from MTurk, it is available to download here. Overall, I think the lesson from this is quite clear: more NSF funding for social scientists to prevent these problems in the future!!

Update 2/18Thanks to @paldhaus and @ChrisKrupiarz, I discovered a European Commission report corroborating my findings, available here (see p. 35-36).  In the Commission survey of the EU, a “split ballot” approach was used, asking half of respondents about how scientific “horoscopes” are whereas the other half were asked about how scientific astrology was.  41% of those in the EU identified astrology as scientific, whereas only 13% identified horoscopes as scientific.  Since this was a 2005 study, it it surprising NSF has not altered their methods since.