Getting ahead in the Lucrative Field of Data Massaging

Evan Warfel has an excellent comment on a post of Andrew Gelman's. Reproduced in full:

Perhaps we are teaching statistics backwards. Instead of teaching students to try and come up with the correct result, we could teach what it feels like to rationalize one’s way through to non-objectivity.

A final exam question might go: This dataset consists of 5 completely uncorrelated variables — I’ve labeled the columns as ‘weight of cat’, ‘probability of attrition’, ‘color of cat [in RGB]’, ‘current age of subject’ and ‘SAT verbal score’. Find a way to make 3 statistically significant correlations and one non-significant correlation. You get an extra point for each spurious t-test you can come up with. The catch is that your entire analysis has to form part of a coherent story. Bonus points go to the 5 most concise answers.

(Obscure title reference explained [Spotify link])

Predictions Concerning Migration to Germany

1. The current love-fest, remindful of the opening of the Berlin Wall, will soon end and something in the range between disillusionment and xenophobia will set in. Like the post-reunification hangover, really, only on steroids, coke and speed.

2. Family reunification legislation (Familienzusammenführung) will be severely tightened within the next three years.

Robin Hanson's Final Words on Signaling

"Falsifiability is just not a very useful concept in social science. Really."

A Two-Step Model of Class-typical Behaviour

Let's start with the example: In the U.S., high-SES people used to smoke more than low-SES people until about 1965. Then the lines crossed, once, and they never crossed again. These days, there are many high-SES people that you don't have to tell about health risks: to them, smoking is prole. And who wants to be prole?

More generally, there are many behaviours that low-SES people show more frequently than low-SES people, and vice-versa. Why? Let me propose a two-step model. First, there is some initial reason why a certain behaviour is shown more often by low-SES people. Then, the behaviour becomes associated with being low SES. Then, the behaviour is reduced even more by high-SES people. 

Smoking is, I think, a good example. Initially, high-SES people may have had access to better information, or have been better at processing the information, or had more self-control, or have put a higher value on health, or what have you, or all of the above. This created an initial smoking gap. This helped associate smoking with being prole. This, in turn, caused people who don't want to be seen as prole to smoke less.

In some cases, the reason for the initial reason could simply be chance.

The model implies that SES differences in smoking were easier to explain in terms of the psychological factors mentioned above (more self-control, etc.) in 1970 than today. Generalizing this is left as an exercise to the reader.

Negative Externalities

It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest.

Adam Smith, The Wealth of Nations

Overjoyed, yet slightly appalled that Richard would even think of, let alone do, such a thing, the man gave him ten thousand dollars for the contract, and a second ten thousand dollars for the incredible suffering the mark had experienced.

"You did a good job," he said. Richard liked to please his customers; that was how his business had grown over the years.

 Philip Carlo, The Ice Man: Confessions of a Mafia Contract Killer

Economics, Sociology, Extrinsic and Intrinsic Motivation, and Variance, or, Advice for the Tribal Social Scientist

Someone whose name I've forgotten said that economics is all about how people act rationally and sociology is all about how they don't. That's catchy, but not very helful, because it makes you think about the exact meaning of the term "rational", and before you know it, you're writing a book. Let me propose instead (and of course I'm using the broad brush here) that economics is how people are driven by extrinsic motivation and sociology is about how they're driven by intrinsic motivation.

Of course, people are driven by both, so a good social scientist should consider both. But there's more to be said about the two. Extrinsic and intrinsic motivation are functional equivalents, and the less variance there is in one of the two, the more variance in your dependent variable the other is going to explain.

That's a little abstract, so here's an example. For the purposes of the example, please accept the simplification that the two types of motivation are completely independent of each other.

Consider a company unit in which variance in intrinsic work motivation is low, and the mean is also low - that is, everybody's a lazy bastard. Then variance in extrinsic motivation will explain a lot of variance in behaviour. That is, those who have a higher incentive, such as financial rewards, to work, will work harder, while those that have little incentive will work little (high variance, middling mean).

Now consider a company unit in which variance in intrinsic work motivation is low, and the mean is high. Here, everyone will work hard (low variance, high mean), and differences in incentives will have little effect.

Next, start by thinking about extrinsic motivation. If extrinsic motivation's variance is low with a low mean (everyone gets minimum wage, regardless), then how hard people work will be driven by their intrinsic motivation (high variance, middling mean).

Finally, if extrinsic motivation's variance is low with a high mean, everyone will try hard (low variance, high mean).

What does that leave us with? First, if variance in one independent variable is lower, then variance in the other dependent variable will explain more of the variance in the dependent variable. Admittedly, that's a mathematical necessity, and not a new insight. More specifically, it means that economists who want to stick it to the guys in the other building had better select topics where variance in people's intrinsic motivation is low, while sociologists who want to teach those arrogant dicks in the Adam Smith building a lesson should select reasearch areas in which variance in people's extrinsic motivation is low.

And if you own a company, you ought to put a lot of time into selecting people with high intrinsic work motivation, and also think hard about how to taylor rewards to employees' behaviour. You'll want to do both, because you'll be perfect at neither.

Genetics, Human Capital, and the Thomas Theorem

In this short presentation on "Genetics and Society" (video), Gregory Cochran points out an obvious incompatability between human capital theory and empirical findings in behaviour genetics: Human capital theory proceeds as though differences in human capital were solely the product of environmental influences, and especially decisions made by parents, but this is known to be false. Cochran goes on to say this has an impact on a point made in human capital theory concerning the quality-quantity tradeoff: The standard view is that you can have many kids and low investment per kid, leading to relatively low human capital in each kid, or you can have many kids, invest heavily in them, and expect them to exhibit high human capital. To the extent that human capital is influenced by genetics, and to the extent that it is not influenced by parents, this tradeoff does not exist. For example, IQ shows practically no response to differences in parental behaviour, hence your kids' expected IQ is independent from your investments, hence independent from the number of kids you have.

But that's a normative point. Empirically, how much people plan to invest in their kids and how many kids they hence choose to have, should be influenced not by the truth itself, but by what people believe to be true. Belief in genetic influences on people's characteristics decreased ca. 1920-1950 and is now low. While research results from the 1970s onwards have shown the popular view to be wrong, these results are not widely known and believed. Hence, decreases in the number of kids people have might still in part be explained by the above-mentioned aspect of human capital theory, if it is combined with the Thomas theorem: "If men define situations as real, they are real in their consequences."

Does Altruism Exist? Against the "Warm Glow" Argument

Models of human decision-making are just that: models, not the real thing. Another way to put this is to say that models are wrong. As the famous saying goes, all models are wrong, but some are useful. This leads to the question, useful for what? That is, just because a model is useful in one domain, doesn't mean it's useful in another domain.

Case in point: economists. Traditionally, they have been working with a model of rational, egoistic decision-makers. Pretty much everybody knows that model is wrong because people sometimes act altruistically, but the model is useful model for many purposes. The trouble starts when people forget that it's just a model that is supposed to be good for some purposes (and not others) and start to defend the model as though it were an entirely accurate description - a view that obviously does not conform to the empirical evidence.

Bryan Caplan is not one of those people. He points out that altruism is real. Anticipating counter-arguments, he adds:

Sure, true believers in ubiquitous selfishness can grasp at straws to protect their dogma.  Perhaps people donate blood for the free cookie, join the army because they might run for office one day, or give to charity in order to make business connections.  Or maybe millions of average joes are clueless enough to believe that the blood supply, the safety of the free world, and the availability of charity hinge on whatever they personally choose to do. 

Anything is possible, but that doesn't mean that anything is plausible.  [...] Genuine altruism is all around us.  Benevolence doesn't explain why bakers bake bread for paying customers, but it does explain why blood donors give blood to strangers for free.

Naturally, this prompts his readers to double down on the altruism-is-really-egoism stuff. For example, his reader Caliban Darklock writes:

I suggest that if there exists an incentive, the activity is not altruistic.

If I give a person $10 for drugs, and then I take the drugs and they give me an endorphin rush, that is not altruistic.

If I give a person $10 because it makes me feel good about myself, which gives me an endorphin rush, how is that altruistic?

I traded $10 for an endorphin rush either way. What is the rational distinction between them?

This is generally known as the "warm glow" argument: If you give money to charity, for example, you get a positive feeling ("warm glow") in return. A counterargument to this comes from Jon Elster (cited from memory): If you could raise your future utility by taking a pill that erases all your altruistic motivations, would you do it? Probably not, because you think it would induce you to do things that are morally wrong.

Antoher counterargument that I've just though up: If, for example, giving a person $ 10 makes you feel good about yourself, this already presupposes you're an alturist. If you weren't, it wouldn't make you feel good about yourself. That is, the argument presupposes what it tries to disprove. I guess philosophers have a name for this; I don't.

Cops Don't Shoot People, Guns Do

Eric Crampton:

I'm a fan of the right to keep and bear arms. But I prefer unarmed police and restricted gun rights to strong gun rights combined with a police force that regularly shoots civilians 'by accident'.

This is in the context of a discussion that uses the U.S. as an example of a country where cops bear arms as a default and New Zealand as an example of a country where they don't. The danger of police carrying guns is readily apparent given recent events and discussions about them in the U.S.: if they have guns, cops might use them all too often. I'm guessing a comparison of death by cop statistics between New Zealand and the U.S. would support that view.

But there's another important variable: the availability of guns to citizens. Apparently (based on information in the thread I link to above), it is pretty limited in NZ, whereas in some U.S. states, any Tom, Dick and Harry can buy a gun. Let me submit the theory that this is what really counts. I'm basing this view on a third data point: Germany. Here, cops routinely carry guns. If you play your music too loud at 10.01 p.m., the cops you'll find knocking on your door will be carrying fully loaded pistols. And yet, in 2011, police fired only 85 bullets while on duty (presumably not counting training), of which 49 were warning shots and 36 aimed at people; 15 people were injured and 6 killed. The numbers for 2010 were 96, 59, 37, 17 and 7, respectively.

Let me wildly generalize from that small heap of data and assumptions: When the probability is high that the other person has a gun, police will be quick to shoot. Part of this is split-second rational(ish) decision making, but there is also a wider institutional context in which this occurs - such as police guidelines about when to shoot and where to aim. The way to reduce police killings of citizens is hence to make it hard for citizens to bear arms.

Biological Reality of Race? What Does It Even Mean? (Also: Free access to Sage journals)

Via Dan Hirschman at Scatterplot comes a debate in Sociological Theory about the nature of race: is it social and/or biological? The new contributions consist of three critical reactions to an article in the 2012 volume of the same journal by Jiannbin Lee Shiao, Thomas Bode, Amber Beyer and Daniel Selvig called "The Genomic Challenge to the Social Construction of Race", and a rejoinder by Shiao. 

The topic isn't new, and the sub-exchange between Shiao in one corner and Daniel Martinez HoSang in the other confirms what something I've long been thinking about this.  

As you may know, variants of cluster analysis can be used to group individuals' genomes on the basis of similarities and dissimilarities, and it has been shown that the resulting clusters correspond to racial categories, as measured by self-identification, for example. One of the two main arguments in the initial Shiao et al. paper is that this clearly shows that the view that race has no biological basis, held by so many sociologists, is wrong.

HoSang's article ends in an attempt at character assassination that stops just short of holding Shiao et al. personally responsible for the gas chambers in Auschwitz, but the earlier portions actually have serious content. HoSang voices misgivings about the validity of the cluster analyses and their interpretation by Shiao et al. and others, but then goes on to say (p. 233):

And even if one accepts the (contested) finding that self-identified race or ethnicity correlates with population structure, this finding does not justify a conclusion that “race” (or clinal class) has a biological basis. At the most quotidian level, the findings suggest that a statistical analysis of genetic ancestry informative markers of a population in the United States that self-identifies as “black” is likely to bear a relationship to an analysis of populations sampled in some region of sub-Saharan Africa. And a population that self-identifies as Chinese is likely to be statistically related with a population in China (Dupré 2008). That a new statistical technique has validated a high probability of such histories of migration is hardly revelatory; it does not establish a biological basis of race.

But  Shiao et al. clearly think just that: These findings show that race has a biological basis.

I suggest that people who wish to have this debate take a step back and start by reaching an agreement on the following:

1. What does it means to say, "Race has a biological basis"? What does it mean to say "Race is a biologically meaningful concept"? Are the two the same?

2. What evidence, if it existed, would show that race has a biological basis/is a biologically meaningful concept? What evidence, if it existed, would refute those claims?

If you don't do that, you'll debate ad infinitum.

Added: Along similar lines, Fabio Rojas comments.

**************

By the way, you can download all of the articles above, as Sage allows open access to all of its journals until October 31st (registration required).

Seth Roberts Is Dead

Today, from his siter Amy, via his blog, came the message that Seth Roberts has passed away. My condolences to his family and friends.

I never met him, only had a few exchanges with him on this and his blog. Generally, I felt he went to far in his criticism of standard approaches, and put too much weight on low-quality evidence. But, as long as I knew of his work - and I certainly include his blogging here - I valued him as an original, unusual, and stimulating thinker. I believe that once the great weight gain in affluent countries ca. 1970-present is better understood, his learning theory of the set point will be a large part of the explanation.

Here are posts in which I discuss Seths work (some of them quite critical):

• "Seth Roberts coins a new term"
• "The Best of Both Worlds"
•  "That's Not the Fast Lane to Stockholm"
• "Problems with Evidence-based Medicine"
• "The Long and Winding Post"
• "Original Medical Opinion of the Day"
• "Effect Size=Holy Crap!"
• "Do Rising Gas Prices Decrease Obesity?"
• "Appreciative Thinking and Cross-sectional Studies"

Two of his posts made it onto my year-end "Best Blogposts of..." lists:

• Will Like vs. Might Love vs. Might Hate
• Two Dimensions of Economic Growth: GDP and Useful Knowledge

Here are quotes of his that I found worth keeping. Here is his paper on self-experimentation in Behavioral and Brain Sciences. Here is his paper "What Makes Food Fattening?" Blowhard, Esq. remembers. Ben Casnocha remembers. Andrew Gelman remembers.

Social Scientist of the Month

The best answer in quite a while to the question, "Why do people look down on social scientists?" comes from Roger Matthews, professor of criminology at the University of Kent. The context is the idea that the removal of lead from gasoline may have played a role in falling crime rates, given that higher lead levels have been linked to aggression at the individual level. Here comes Matthews, as quoted by Dominic Casciani (via):

"I don't see the link," he says. "If this causes some sort of effect, why should those effects be criminal?

"The things that push people into crime are very different kinds of phenomena, not in the nature of their brain tissue. The problem about the theory is that a lot of these [researchers] are not remotely interested or cued into the kinds of things in the mainstream.

"There has been a long history of people trying to link biology to crime - that some people have their eyes too close together, or an extra chromosome, or whatever.

"This stuff gets disproved and disproved. But it keeps popping up. It's like a bad penny."

If you tried to come up with a parody of the daftness of those mushy-heads in the social sciences, could you think of anything better?

Intelligence Researchers: "Regression to the mean [...] is purely a statistical artifact"

Whoa Nelly! In a very interesting ask-a-researcher thread on Pschological Comments, researcher Michael A. Woodley drops the following, which surprised me a fair bit. The context is that there is intergenerational regression to the mean in intelligence. That is, very smart parents tend to have children who are less smart than they are; very dull parents tend to have children who are less dull than they are. Or so I thought - and not just I, I'm sure. Woodley disagrees. He quotes from a book by himself and Aurelio Jose Figueredo. Here's the central bit:

Furthermore in the case of parent-offspring correlations on g, oversampling parental scores with positive errors of measurement on IQ, as by selecting those identified as high-g individuals based on high observed IQ scores for special study, will produce regression to the mean when assessing the IQ of their offspring, even if the offspring were genetically identical to the parents, given the nature of this statistical artifact. This can be confirmed by retesting the parents themselves, which is rarely done, because one will then no doubt observe regression to the mean of the parental IQ scores in the parents themselves, presumably without having undergone any genetic recombination whatsoever. The proposition that offspring are necessarily closer to the mean of the general population in their actual latent g-factor (as opposed to their observed IQ scores) is therefore a fallacy, especially under conditions of assortative mating.

Quite a claim. Is this generally accepted, or perhaps Woodley & Figueredo's minority position? When they say that the claim "can be confirmed by retesting the parents themselves, which is rarely done", does this mean it has been done? Repeatedly?

This would explain a puzzle, though: If there were regression to the mean in a substantial sense, then it should not be over after a generation, which would mean that, by now, we should all be pretty much equally intelligent, right? With the above interpretation, that problem does not exist.

I just hope he means to restrict his statements about the nonexistence of regression to the mean to the context at hand. The phenomenon is certainly real in other contexts - unless you want to redefine, for example, a particularly hot day in a certain city as just an expression of a city's underlying latent hotness measured with upward error, and the like.

Ich weiß ja nicht, mit wem Jan Fleischhauer so abhängt

Will Wilkinson meint, Mad Men sei für viele männliche Zuschauer so attraktiv, weil die Serie zu zeigen scheint, "how sweet it would be to have women take care of all the annoying details of life and smoke at work." Laut Jan Fleischhauer geht es vielen Deutschen mit mit Vladimir Putin ähnlich:

Nicht trotz, sondern wegen der Erziehung zu Pazifismus, Geschlechtersensibilität und fortwährender Antidiskriminierung ist ein Gutteil der Deutschen so fasziniert von Russland und seinem Anführer.

Putin steht für das unterdrückte Andere, das gerade, weil es so selbstbewusst und unverstellt auftritt, einen unwiderstehlichen Reiz ausübt.

Diese Erklärung wäre freilich überzeugender, wenn erst mal etabliert würde, dass der zu erklärende Tatbestand überhaupt zutrifft. Mir zumindest ist in Deutschland keine besondere Putin-Begeisterung aufgefallen.

Vielleicht täusche ich mich aber auch, und Fleischhauer hat recht. Das Problem ist, dass weder Fleischhauer noch ich valide Repräsentativdaten zu der Meinung der Deutschen über Putin haben. So hängt die Weltwahrnehmung dann von dem ab, was man so mitkriegt. Ein grundlegender Wahrnehmungsfehler der Menschen ist es, "das, was man so mitkriegt" für repräsentativer zu halten als es ist. Soziologen machen sich nicht deshalb so einen Kopf um Sampling-Probleme und Frageformulierungen, weil man so toll gelehrte Artikel darüber schreiben kann, sondern weil sie bemüht sind, über das Niveau der Alltagswahrnehmung hinauszugehen.

Exit, Voice, and Loyalty, and Spotify

O.k., I'll admit I've never read Hirshleifer's book, but I'm wondering. In the last post I describe how you can react to the Spotify relaunch, which makes the product considerably worse, by switching back to, and remaining on, an older version of the software, using a trick not approved by Spotify (subverting the auto-update function). In Hirshleifer's scheme, what is this? The post itself can, I guess, be interpreted as voice, but what I (and many others) do cannot. It is exit, sort of, because I exit the use of the current software, as envisioned by the company, but then, it's not, but rather loyalty, because I remain a paying customer. Should Hirshleifer have called his book Exit, Voice, Loyalty, and Creative Adjustment?

If You Make One Wrong Assumption, All Kinds of Shit Can Follow

From Steve Sailer's review of Gregory Clark's new book, The Son Also Rises (which I have not  read but might):

Economists [...] assumed that social mobility multiplies at the same rate with each new generation. If the correlation coefficient [...] of income between father and son is 0.4, then between grandfather and son it was imagined to be 0.4 squared or 0.16. Instead, it’s somewhat higher (0.26 in one study) due to regression toward the mean [...]. If a rich man has a son of only average income, his grandson is likely to earn somewhat above average.

This doesn't seem like such an advanced insight to come up with, so one might think that someone should have thought of it long ago and convinced all the others.

But then, it is not that surprising. Mainstream economics is a blank-slate science, as is the other discipline studying intergenerational mobility, sociology. To paint with a broad brush, the two differ in the timing of the influences they deem important. Standard economics sees everybody as basically the same, but subject to different opportunities and restrictions in a given situation. In contrast, sociologists typically think that people enter situations exhibiting vast differences, which result from social influences from birth onwards. Neither considers that large and important differences may already exist at birth (Hence, how could regression to the mean be important? What mean?).

This assumption has been known to be wrong for decades. Clinging to it causes all kinds of problems. Perhaps the main symptom of this in sociology is researchers' tendency to view a host of things as exogenous which are, in fact, endogenous. Such as, oh, socioeconomic status, the discipline's favourite variable. Once you realize there may be an endogenous component to status, you'll start doing lots of eyerolling when reading sociology journals. After a while, eyeache sets in.

The Best Blog Posts of 2013

It's about time, so here.

As usual, brackets are appended to each link to indicate whether the post is Long, Medium lenght or Short; High-Brow, Mid-Brow or Low-Brow, and Funny or Not.

For other years' lists, use the tag.

15. Offsetting Behaviour: "Social Costs and HPV", by Eric Crampton

14. Discover: "Why Race as a Biological Construct Matters", by Razib Khan (L; HB; N)

13. The Power of Goals: "Home Sweet Home", by Mark Taylor (L; MB; N)

12. Crooked Timber: "New Tools for Reproducible Research", by Kieran Healy (S; MB; F)

11. German Joys: "The Metamorphosis (US Summer Movie) Elevator Pitch", by Andrew Hammel (S; MB; F)

10. Code and Culture: "You Broke Peer Review. Yes, I Mean You", by Gabriel Rossman (L; MB; N)

9. EconLog: "The Homage Statism Pays to Liberty", by Bryan Caplan (M; MB; N)

8. Scatterplot: "Annals of Self-Refuting Tweets", by Jeremy Freese (S; MB; F)

7. Overcoming Bias: "Future Story Status", by Robin Hanson (M; HB; N)

6. Gulf Coast Blog: "Defamiliarization, Again for the First Time", by Will Wilkinson (L; MB; N)

5. Armed and Dangerous: "Preventing Visceral Racism", by Eric S. Raymond (L; MB; N)

4. Askblog: "It Is Sometimes Appropriate . . .", by Arnold Kling (M; HB; N)

3. EconLog: "Make Your Own Bubble in 10 Easy Steps", by Bryan Caplan (M; LB; N)

2. Armed and Dangerous: "Natural Rights and Wrongs?", by Eric S. Raymond (M; HB; N)

1. Falkenblog: "Great Minds Confabulate Like Small Minds", by Eric Falkenstein (L; HB; N)

Thanks and congrats to all above.

Three Answers to the Question, "What Is Intelligence?"

The Quip: “Intelligence is what you need when you don’t know what to do”. Carl Bereiter coined this elegant phrase. [...]

The Explanation: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings — ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.” Linda Gottfredson and 52 leading psychometricians agree with this explanation. [...]

The formula: g+group+specific skill+error, where g accounts for about 50% of the variance. [...]

That is from James Thompson's blog Psychological Comments, which I've added to my roll. It has what it says on the label with a focus on - you've guessed it - intelligence. I'm particularly grateful to him for providing me with a label for an error (popular with sociologists and the general public) that has long been getting on my nerves. The error is automatically interpreting correlations between socioeconomic status (not my favourite concept in the first place) and some outcome as an effect of the former on the latter, without even considering the possibility that there may be psychological constructs that influence both SES and the outcome (e.g., see my discussion here). He calls it the sociologist's fallacy. Not that imaginative, really, is it? Man, I really should have thought of that myself.

Anyway, the blog's recommended.

The Law of Yawn

Regular readers may remember a post about identification of causal effects I wrote in August. Here's the full text:

The better a model is at identifying a causal effect, the less likely it is the effect is going to look substantial. That's because of (i) publication bias, (ii) how the world works.

You may note that that text contains zero examples - it's just a general impression plus some armchair theorizing. Thankfully, Steve Sailer provides an example from commercial marketing research:

In fact, one side effect of bad quantitative methodologies is that they generate phantom churn, which keeps customers interested. For instance, the marketing research company I worked for made two massive breakthroughs in the 1980s to dramatically more accurate methodologies in the consumer packaged goods sector. Before we put to use checkout scanner data, market research companies were reporting a lot of Kentucky windage. In contrast, we reported actual sales in vast detail. Clients were wildly excited ... for a few years. And then they got kind of bored.

You see, our competitors had previously reported all sorts of exciting stuff to clients: For example, back in the 1970s they'd say: of the two new commercials you are considering, our proprietary methodology demonstrates that Commercial A will increase sales by 30% while Commercial B will decrease sales by 20%.

Wow.

We'd report in the 1980s: In a one year test of identically matched panels of 5,000 households in Eau Claire and Pittsfield, neither new commercial A nor B was associated with a statistically significant increase in sales of Charmin versus the matched control group that saw the same old Mr. Whipple commercial you've been showing for five years. If you don't believe us, we'll send you all the data tapes and you can look for yourselves.

Ho-hum.

In the social sciences - and I would include marketing - there probably are few cases when the effect of X on Y is genuinely zero. Just about everything influences everything else, in a roundabout way. There's a flipside to that: The influence of single factors is usually very small. A core reason for that is that people's personalities and behaviour are pretty stable, which is why the concept "personality" makes sense.

Of course, there's also Xs that have a large influence on Y. The problem is that researching this is, or soon becomes, pretty boring. In fact, when people say "Did we really need a study for that?", they sometimes have a point. When an influence is large, it will usually (though not by logical necessity) be readily apparent. You don't need to be a social scientist to see that adolescent's friends influence their behaviour.

So, shut up shop? I think not. One, you do need a social scientist to tell you how large an obvious effect is. Two, the above allows for a sweet spot where effects are not obvious, but large enough to detect. Three, and this is perhaps the most important point, it is a worthwhile endeavour to show that the effect of X on Y really is close to zero, contrary to what some people would have you believe. Especially if X costs money.

Around the Blogs, Vol. 103

Bit late today, but here's some recent posts that may be worth your time.

1. Andrew Gelman knows how randomization works in animal studies. (Post starts off with disturbing image)

2. Gabriel Rossman has tips on how to be a better journal reviewer, with a focus on decreasing turnaround times. Fabio Rojas links and summarizes.

3. Christian Jarrett summarizes a new paper by Brian D. Earp, Jim A. C. Everett, Elizabeth N. Madva, and J. Kiley Hamlin, who cannot replicate the "Macbeth effect", i.e., the finding that feelings of disgust increase the desire for physical cleaning.