How High is the Sky? Well, Higher than the Ground

Challenged by some exchanges in my own personal emails and over in Brent Robert’s “pigee” blog, I’ve found myself thinking more about what is surely the weakest point in my previous post about effect size: I failed to reach a clear conclusion about how “big” an effect has to be to matter. As others have pointed out, it’s not super-coherent to claim, on the one hand, that effect size is important and must always be reported yet to acknowledge, on the other hand, that under at least some circumstances very “small” effects can matter for practical and/or theoretical purposes.

My attempt to restore coherence has two threads, so far. First, to say that small effect sizes are sometimes important does not mean that they always are. It depends. Is .034 (in terms of r) big enough? It is, if we are talking about aspirin’s effect on heart attacks, because wide prescription can save thousands of lives a year (notice, though, that you need effect size to do this calculation). Probably not, though, for other purposes.

But honestly, I don’t know how small an effect is too small. As I said, it depends. I suspect that if social psychologists, in particular, reported and emphasized their effect sizes more often, over time an experiential base would accrue that would make interpreting them easier. But, in the meantime, maybe there is another way to think about things.

So the second thread of my response is to suggest that perhaps we should focus on the ordinal rather than absolute nature of effect sizes. While we don’t often know exactly how big an effect has to be to matter, in an absolute sense, there are many contexts in which we care which of two things matters **more**. Personality psychologists routinely publish long (and to some people, boring) lists of correlates; such lists draw attention to the personality variables that appear to be more and less related to the outcome of interest, even if the exact numerical values aren’t necessarily all that informative.

Social psychological theorizing is also often, often, phrased in terms of relative effect size, though the actual numbers aren’t always included. The whole point of Ross & Nisbett’s classic book “The Person and the Situation” was that the effects of situational variables are larger than the effects of personality variables, and they draw theoretical implications from that comparison that — read almost any social psychology textbook or social psych. section of any intro textbook — goes to the heart of how social psychology is theoretically framed at the most general level. The famous “Fundamental Attribution Error” is explicitly expressed in terms of effect size — situational variables allegedly affect behavior “more” than people think. How do you even talk about that claim without comparing effect sizes? The theme of Susan Fiske’s address at the presidential symposium at the 2012 SPSP was that “small” manipulations can have “large” effects; this is also effect size language expressing a theoretical view. Going back further, when attitude change theorists talked about direct and indirect routes to persuasion, this raised a key theoretical question of relative influence of the two effects. More recently, Lee Jussim wrote a whole (and excellent) book about the size of expectancy effects, comparing them to the effects of prior experience, valid information, etc. and building a theoretical model from that comparison.

I could go on, but, in short, the relative size of effects matters in social psychological theorizing whether the effects are computed and reported, or not. When they aren’t, of course, the theorizing is proceeding in an empirical vaccum that might not even be noticed – and this happens way too often, including in some of the examples I just listed. My point is that effect size comparisons, usually implicit, are ubiquitous in psychological theorizing so it would probably be better if we remembered to explicitly calculate them, report them, and consider them carefully.

Does (effect) Size Matter?

Personality psychologists wallow in effect size; the ubiquitous correlation coefficient, Pearson’s r, is central to nearly every research finding they report.  As a consequence, discussions of relationships between personality variables and outcomes are routinely framed by assessments of their strength.  For example, a landmark paper reviewed predictors of divorce, mortality, and occupational achievement, and concluded that personality traits have associations with these life outcomes that are as strong as or stronger than traditional predictors such as socio-economic status or cognitive ability (Roberts et al., 2007).  This is just one example of how personality psychologists routinely calculate, care about, and even sometimes worry about the size of the relationships between their theoretical variables and their predicted outcomes.

Social psychologists, not so much.  The typical report in experimental social psychology focuses on p-level, the probability of the magnitude of the difference between experimental groups occurring if the null hypothesis of no difference were to be true.   If this probability is .05 or less, then: Success!  While effect sizes (usually Cohen’s d  or, less often, Pearson’s r) are reported more often they they used to be – probably because the APA Publication Manual explicitly requires it (a requirement not always enforced) – the emphasis of the discussion of the theoretical or even the practical importance of the effect typically centers around whether it exists.  The size simply doesn’t matter.

Is this description an unfair caricature of social psychological research practice?  That’s what I thought until recently.  Even though the typical statistical education of many experimentally-oriented psychologists bypasses extensive discussion of effect size in favor of the ritual of null-hypothesis testing, I assumed that the smarter social psychologists grasped that an important part of scientific understanding involves ascertaining not just whether some relationship between two variables “exists,” but how big that relationship is and how it compares to various benchmarks of theoretical or practical utility.

It turns out I was wrong.  I recently had an email exchange with a prominent social psychologist who I greatly respect. [i] I was shocked, therefore, when he wrote the following[ii]:

 …the key to our research… [is not] to accurately estimate effect size. If I were testing an advertisement for a marketing research firm and wanted to be sure that the cost of the ad would produce enough sales to make it worthwhile, effect size would be crucial. But when I am testing a theory about whether, say, positive mood reduces information processing in comparison with negative mood, I am worried about the direction of the effect, not the size (indeed, I could likely change the size by using a different manipulation of mood, a different set of informational stimuli, a different contextual setting for the research — such as field versus lab). But if the results of such studies consistently produce a direction of effect where positive mood reduces processing in comparison with negative mood, I would not at all worry about whether the effect sizes are the same across studies or not, and I would not worry about the sheer size of the effects across studies. This is true in virtually all research settings in which I am engaged. I am not at all concerned about the effect size (except insofar as very small effects might require larger samples to find clear evidence of the direction of the effect — but this is more of a concern in the design phase, not in interpreting the meaning of the results). In other words, I am yet to develop a theory for which an effect size of r = .5 would support the theory, but an effect size of r = .2 (in the same direction) would fail to support it (if the effect cannot be readily explained by chance). Maybe you have developed such theories, but most of our field has not.

To this comment, I had three reactions.

First, I was startled by the claim that social psychologists don’t and shouldn’t care about effect size. I began my career during the dark days of the Mischelian era, and the crux of Mischel’s critique was that personality traits rarely correlate with outcomes greater than .30. He never denied that the correlations were significant, mind you, just that they weren’t big enough to matter to anybody on either practical or theoretical grounds. Part of the sport was to square this correlation, and state triumphantly (and highly misleadingly) that therefore personality only “explains” “9% of the variance.”  Social psychologists of the era LOVED this critique[iii]! Some still do. Oh, if only one social psychologist had leapt to personality psychology’s defense in those days, and pointed out that effect size doesn’t matter as long as we have the right sign on the correlation… we could have saved ourselves a lot of trouble (Kenrick & Funder, 1988).

Second, I am about 75% joking in the previous paragraph, but the 25% that’s serious is that I actually think that Mischel made an important point – not that .30 was a small effect size (it isn’t), but that effect size should  be the name of the game.  To say that an effect “exists” is a remarkably simplistic statement that on close examination means almost nothing.  If you work with census data, for example, EVERYTHING — every comparison between two groups, every correlation between any two variables — is statistically significant at the .000001 level. But the effect sizes are generally teeny-tiny, and of course lots of them don’t make any sense either (perhaps these should be considered “counter-intuitive” results). Should all of these findings be taken seriously?

Third, if the answer is no, then we have to decide how big an effect is in fact worth taking seriously. And not just for purposes of marketing campaigns! If, for example, a researcher wants to say something like “priming effects can overwhelm our conscious judgment” (I have read statements like that), then we need to start comparing effect sizes. Or, if we are just going to say that “holding a hot cup of coffee makes you donate more money to charity” (my favorite recent forehead-slapping finding) then the effect size is important for theoretical, not just practical purposes, because a small effect size implies that a sizable minority is giving LESS money to charity, and that’s a theoretical problem, not just a practical one.  More generally, the reason a .5 effect size is more convincing, theoretically, than a .2 effect size is that the theorist can put less effort into explaining why so many participants did the opposite of what the theory predicted.

Still, it’s difficult to set a threshold for how big is big enough.  As my colleague pointed out in a subsequent e-mail – and as I’ve written myself, in the past — there are many reasons to take supposedly “small” effects seriously.  Psychological phenomena are determined by many variables, and to isolate one that has an effect on an interesting outcome is a real achievement, even though in particular instances it might be overwhelmed by other variables with opposite influences.  Rosenthal and Rubin (1982) demonstrated how a .30 correlation was enough to be right, about two times out of three.  Ahadi and Diener (1989) showed that if just a few factors affect a common outcome, the maximum size of the effect of any one of them is severely constrained.  In a related vein, Abelson (1985) calculated how very small effect sizes – in particular, the relationship between batting average and performance in a single at-bat – can cumulate fairly quickly into large differences in outcomes (or ballplayer salaries).  So far be it from me to imply that a “small” effect, by any arbitrary standard, is unimportant.

Now we are getting near the crux of the matter.  Arbitrary standards – whether the .05 p-level threshold or some kind of minimum credible effect size – are paving stones on the road to ruin.  Personality psychologists routinely calculate and report their effect sizes, and as a result have developed a pretty good view of what these numbers mean and how to interpret them.  Social psychologists, to this day, still don’t pay much attention to effect sizes so haven’t developed a base of experience for evaluation. This is why my colleague Dan Ozer and I were able to make a splash as young beginning researchers, simply by pointing out that, for example, the effect size of the distance of the victim on obedience in the Milgram study was in the .30’s (Funder & Ozer, 1983).  The calculation was easy, even obvious, but apparently nobody had done it before.  A meta-analysis by Richard et al. (2003) found that the average effect size of published research in experimental social psychology is r = .21.  This finding remains unknown, and probably would come as a surprise, to many otherwise knowledgeable experimental researchers.

But this is what happens when the overall attitude is that “effect size doesn’t matter.”  Judgment lacks perspective, and we are unable to separate that which is truly important from that which is so subtle as to be virtually undetectable (and, in some cases, notoriously difficult to replicate).

My conclusion, then, is that effect size is important and the business of science should be to evaluate it, and its moderators, as accurately as possible.  Evaluating effect sizes is and will continue to be difficult, because (among other issues) they may be influenced by extraneous factors, because apparently “small” effects can cumulate into huge consequences over time, and because any given outcome is influenced by many different factors, not just one or even a few.  But the solution to this difficulty is not to regard effect sizes as unimportant, much less to ignore them altogether.  Quite the contrary, the more prominence we give to effect sizes in reporting and thinking about research findings, the better we will get at understanding what we have discovered and how important it really is.


Abelson, R. P. (1985). “A variance explanation paradox: When a little is a lot.” Psychological Bulletin, 97, 129–133.

Ahdadi, S., & Diener, E. (1989). Multiple determinants and effect size. Journal of Personality and Social Psychology, 56, 398-406.

Funder, D.C., & Ozer, D.J. (1983). Behavior as a function of the situation. Journal of Personality and Social Psychology, 44, 107-112.

Kenrick, D.T., & Funder, D.C. (1988). Profiting from controversy: Lessons from the person-situation debate. American Psychologist, 43, 23-34.

Nisbett, R.E., (1980). The trait construct in lay and professional psychology. In L. Festinger (Ed.), Retrospections on social psychology  (pp. 109-130). New York: Oxford University Press.

Richard, F.D., Bond, C.F., Jr., & Stokes-Zoota, J.J. (2003). One hundred years of social psychology quantitatively described. Review of General Psychology, 7, 331-363.

Roberts, B.W., Kuncel, N.R., Shiner, R., Caspi, A., & Goldberg L.R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives in Psychological Science, 2, 313-345.

Rosenthal, R., & Rubin, D.B. (1982). A simple, general-purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74, 166-169.

[i] We served together for several years on a grant review panel, a bonding experience as well as a scientific trial by fire, and I came to admire his incisive intellect and clear judgment.

[ii] I obtained his permission to quote this passage but, understandably, he asked that he not be named in order to avoid being dragged into a public discussion he did not intend to start with a private email.

[iii] See, e.g., Nisbett, 1980, who raised the “personality correlation” to .40 but still said it was too small to matter.  Only 16% of the variance, don’t you know.