And looking at Clooney’s surface, you obtain a surface understanding of his persuasion source: Physical Attractiveness! As a Cue a guy like Clooney can’t be beat; If The Source Is Good Looking, Do What He Says.

Yet, if you scan the Primer and the CLARCCS Cues chapter, you don’t find Physical Attractiveness as one of those primary tools. Comparison, Liking, Authority, Reciprocity, Commitment/Consistency, and Scarcity glide past good looks with nary a glance.

How can something as obvious as this not have its own persuasion chapter?

Because good looks only persuade through positive affect. If you aren’t likeable, your good looks won’t close the deal.

Certainly pretty boys and girls can get your attention, but attention is not the final TACT, only an upstream precursor and not the downstream goal you seek. You realize that in large part Clooney succeeds with his fabulous good looks because he is fabulously likeable. He plays well with others, boys and girls, being both a Man’s Man and a Woman’s Man, too, with no tension in between. Clooney’s a guy anyone would want as a friend under any circumstances with his fabulous good looks as a fabulous side effect.

Realize another persuasion lesson with Clooney: Function. You can use good looks in the Cascade at that Reception and Exposure stage. Good looks arrest attention and start the Cascade. Now realize that those good looks alone cannot sustain the rest of the Cascade, especially with the behavior change you seek, unless those good looks also carry good feelings with them. Thus, you can use the Beautiful Jerk on a billboard, but friendly and optionally attractive sales associates on the floor with the product.

In a field study, we collected data in a restaurant and manipulated bite size by providing diners with small or large forks. We found that diners consumed more from smaller rather than larger forks. Utilizing motivation literature, which ties into the unique factors present in a restaurant consumption setting (e.g., diners have a well-defined goal of hunger satiation because they invest effort by visiting a specific restaurant, choose from a menu, and pay money for the meal), we present our rationale for the pattern of results.

Sounds pretty obvious, right. Small forks cue Big eating since each bite is Small. Big forks cue Small eating since each bite is Big. Easy-peesey. Here’s a graph to illustrate.

And, this is not a trivial effect.

We assessed the influence of fork size on the weight of the food left on the plate (less food on the plate indicated more consumption) while controlling for the weight of the initial food served, food price, meal occasion (lunch vs. dinner), appetizer (yes vs. no) and alcohol consumption (yes vs. no). This ANCOVA showed that the use of the larger fork resulted in more food left on the plate (i.e., less quantity consumed) than the smaller fork (Mlarge = 7.91 ounces vs. Msmall = 4.43 ounces; F(1, 98) = 7.80, p < .01, partial η2 = .07).

That η2 (eta squared) of .07 translates into a Small+ Windowpane, about 40/60 which would probably be obvious to an observant observer who was looking for an effect. Thus, if you were a dishwasher for this restaurant, you could probably see the difference on the amount of food left on plates between the Small and Big fork conditions. Hey, 4 ounces versus 8 ounces is a lot of spagetti and meatballs.

So the Small fork causes people to eat more because each bite is too small and so they take more bites and more food. The Big fork actually has the effect of reducing caloric consumption. Cue up the Food Police! If we can put calorie counts on menus why not fork size specifications!

Except.

Moreover, in a controlled lab study we demonstrate that when these factors are absent, the pattern of results is reversed.

What? Reversed? Yes. And practical, too.

Using ANCOVA, we assessed the influence of fork size on the weight of pasta left on the plate while controlling for the initial weight of the pasta served. The results showed that those assigned to the large fork condition left less pasta in the bowl (i.e., consumed more pasta) than those in the smaller fork condition (Mlarge = 4.09 ounces vs. Msmall = 5.19 ounces; F(1, 78) = 4.73, p < .03, partial η2 = .05).

This eta squared is another Small+ Windowpane, about a 40/60 difference. And again, someone who was really looking could probably see the practical difference here as in our sweaty dishwasher cleaning up the plates.

Small forks cue Small eating? Didn’t the first study report Big forks cue Small eating? What the hell is going on here? Which is it? A fork is a fork, right? The Food Police are not happy. Cancel that march on the White House.

This is a cross over interaction where a relationship is positive under one condition then negative under another condition.  Stated another way, it depends.  Stated under persuasion labels, the play depends upon the box or what’s the Local?

The trick here is the motivational set of the eater. In the first study with Small Fork, Big Eats:

In our consumption context, we observe that diners visit the restaurant with a well-defined goal of satiating their hunger, and, because of this well-defined goal, they are willing to invest effort and resources to satiate their hunger. Since research has shown that free choice captures realistic behavior more accurately than forced choice situations (Dhar and Simonson 2003), a restaurant offers diners several methods to exercise free choice in satiating their hunger. For instance, diners select a restaurant of their choice, choose an entrée (or entrées) from the menu of offerings, pay for their food, and have the option to take home leftovers. Therefore, people invest effort in order to satiate their hunger.

When the Other Guy has the goal of Satisfying Hunger, then we get Small Forks, Big Eats. But, in the second study with Small Fork, Small Eats:

Eighty-one participants took part in this study for partial course credit. They were told that this was a food consumption study, and each participant was taken to a separate table. They were then offered a preweighted bowl of pasta salad with either a small or a large fork and a bottle of water. The same forks from the restaurant study were used. A pasta salad was served, since several bites are required for consumption rather than a single forkful. Participants were left alone and allowed to consume as much as they wanted.

When the Other Guy has the goal of fulfilling the requirements of a study, we get Small Forks, Small Eats. The effect of fork size depends upon why the Other Guy is using the fork. For Hungry Other Guys fork size means something different than for UnHungry Other Guys doing a marketing study.

Mavens, understand persuasion by the function of a variable, not its structure, content, or appearance. How, not What. You can always spot the amateur on this play. Understand the function or how the thing does what it does, not what it is. See the difference between Doing and Being.

Wow. Philosophical Persuasion Theory!

More entertaining is the Cool Table media comment on this article. You can read the NYT, WSJ, Huffington Post, and Time for their details, but here’s the main point: They missed the main point! Each media comment on this study catches only the first study with Small Forks and Big Eats and completely misses the functional truth of the cross over interaction.  Those Cool Table players drop their maven masks to reveal muggle mugs underneath.  They each and all think Small Forks, Big Eats, so buy a Bigger Fork to lose weight!

Want to join the Cool Table? Carry a Big Fork and Eat Softly.

Want to Change the Other Guys eating? Determine Their goal, then fork them appropriately.

It Depends!

All Persuasion Is Local.

Arul Mishra, Himanshu Mishra and Tamara M. Masters. (2012). The Influence of Bite Size on Quantity of Food Consumed: A Field Study. Journal of Consumer Research , Vol. 38, No. 5, pp. 791-795

DOI: 10.1086/660838

Feedback!

Consider this method.

The study was conducted in an 17-bed intensive care unit from June 2008 through June 2010.  We placed cameras with views of every sink and hand sanitizer dispenser to record hand hygiene of HCWs. Sensors in doorways identified when an individual(s) entered/exited.  When video auditors observed a HCW performing hand hygiene upon entering/exiting, they assigned a pass; if not, a fail was assigned.  Hand hygiene was measured during a 16-week period of remote video auditing without feedback and a 91-week period with feedback of data.  Performance feedback was continuously displayed on electronic boards mounted within the hallways, and summary reports were delivered to supervisors by electronic mail.

Okay.  Big deal.  A video camera observes people as they enter rooms and a human coder merely notes a pass or a fail for hand washing.  That pass/fail information is collected first as a baseline, then as Feedback to everyone in two forms – that electronic boards in hallways and in summary email reports to supervisors.  Big deal.

During the 16 prefeedback weeks, there were 60,542 hand hygiene events observed and 3,933 events were categorized as passing, for an overall hand hygiene compliance rate of 6.5%, ranging from a weekly low of 3.5% to a high of 9.8%. For the 16 postfeedback weeks, there were 73,080 observations, with 59,627 categorized as passing, for an overall compliance rate of 81.6%, with rates ranging from 30.8% to 91.2%. During the 75-week maintenance period, 298,860 observations were made, with 262,826 in compliance (87.9%). Weekly rates ranged from 83.5% to 91.6%.

Here’s the graphic.

The Interested Reader does the Windowpane math for us.  I quote.

By my calculations, the  comparison between the postfeedback results and the prefeedback results gives an odds ratio of 63.8 (63.79), a log odds ratio of 4.2 (4.16), an r of .75 .(749), and a BESD of 87/13. The comparison between the follow-up results and the prefeedback results gives an odds ratio of 105.0 (104.98), a log odds ratio of 4.7 (4.65), an r of .70 (.697), and a BESD of 63/37.

What IR labels as BESD we call the Windowpane, but that still means 13/87 smells just as sweet and clean as . . . newly washed hands!  Considering that a 25/75 outcome is Large, we’re into Huge, Stupendous, Awesome, perhaps even and either Groovy or Gear!

And, just from Feedback.  Now, quickly note the feedback is not provided to each person, but rather is given as a running rate to everyone on those public electronic boards.  That’s a great persuasion wrinkle in this Feedback Play.  It adds the power of Norms whether as a Comparison Cue (If Everyone Else Is Doing It, You Should, Too) or as a Argument (I’m a serious health care worker dedicated to safe and healthy behaviors).  Thus, this study is truly Feedback Plus with that Plus being a healthy dose of social norms and comparison.  And just look at that compliance curve.  Man, it looks like a guided missile launched in Week 16.  You know you’ve hit a winner when you get a behavior curve like that with that effect size.

I’d encourage you to get this paper and read it.  This crew worked like dogs for what most people would think of as a little study.  Just count pass/fail and aggregate.  Big deal.  I’m only showing the tip of a research iceberg.  My compliments to these researchers.  This is hard damn work, smartly done.  To those who would shrug at it:  If you can’t praise it, beat it!

Donna Armellino, Erfan Hussain, Mary Ellen Schilling, William Senicola, Ann Eichorn, Yosef Dlugacz, and Bruce F. Farber. Using High-Technology to Enforce Low-Technology Safety Measures: The Use of Third-party Remote Video Auditing and Real-time Feedback in Healthcare. Clin Infect Dis. (2012) 54(1): 1-7 first published online November 21, 2011

doi:10.1093/cid/cir773

P.S. Bonus! Palmore and Henderson provide a tough and fair review of this research and note privacy concerns and several important methods and measurement issues. This is just great peer review science in action. Great research, great criticism – that’s how you advance.

P.P.S.  Thanks, too, to Interested Reader.  You can tell that IR is a better gearhead bean counter than I with those two and three decimals of accuracy.  And, what IR calls BESD is what we label the Windowpane also again demonstrating the performance superiority of IR to yours truly.  Yes.  The Windowpane is actually the BESD or Binomial Effect Size Display.  If you are speaking Research Geek, you should use BESD.  If you’re just a plain old geek like me, Windowpane!

P.P.P.S.  Binomial Effect Size Display.  Really.  Is that the best Rosenthal could come up with?  No wonder smoothies like Gladwell and those Cool Table elites sell all the books and get all the boys and girls.  The Igon and the Ecstasy!

Consider the strategy inherent to these questions.

1. Does Israel have the ability to cause severe damage to Iran’s nuclear sites and bring about a major delay in the Iranian nuclear project? And can the military and the Israeli people withstand the inevitable counterattack?

2. Does Israel have overt or tacit support, particularly from America, for carrying out an attack?

3. Have all other possibilities for the containment of Iran’s nuclear threat been exhausted, bringing Israel to the point of last resort? If so, is this the last opportunity for an attack?

This, according to a published article based on face to face interviews with key Israeli leaders is how they are thinking about responding when and if they believe Iran will possess nuclear weapons. The long article develops their strategic planning over the past ten years and the following tactics. In many ways, the article is a blueprint for thinking and acting, strategically and tactically. I highly recommend that anyone who pretends to persuasion maven status read it.

I observe and approve of the clear-eyed or hard-headed focus on concrete outcomes. The strategy produces observable, countable, physical changes. There’s no flowery self-persuasion as if you need to justify the strategy to yourself. It directly aims at doing explicit activities at an explicit group of Other Guys. A persuasion plan falls naturally out of the strategy behind these three key points.

The three key points also provide a great hierarchy of concerns. The first concerns sheer ability and enhancing that. The second concerns allies and public opinion. The third seeks alternatives to the first point. You know how to prioritize with this hierarchy and you also understand you need to address all three simultaneously.

The article then develops how this strategy has played out and is playing out in tactics, some of which I consider as persuasion plays rather than power plays. Even events that involve killing people function more persuasively than just the removal of a key Other Guy. Such violent acts frighten some Other Guys who remove participation or support for the Iranian project – that’s delay and damage. These acts also encourage internal dissenters and opposition. Finally, these acts force potential allies to think about the Iranian project.

It’s also interesting to note how talkative these Israeli leaders are right now. Normally you associate silence with Israel on issues like this. They do or don’t do what they do and always refuse public comment on everything. The fact of their public talk demonstrates a more clear communication application of persuasion than the persuasive effects of killing lead scientists. Consider this quote from Ehud Barak, the defense minister of Israel.

At various points in our conversation, Barak underscored that if Israel or the rest of the world waits too long, the moment will arrive — sometime in the coming year, he says — beyond which it will no longer be possible to act. “It will not be possible to use any surgical means to bring about a significant delay,” he said. “Not for us, not for Europe and not for the United States. After that, the question will remain very important, but it will become purely theoretical and pass out of our hands — the statesmen and decision-makers — and into yours — the journalists and historians.”

Here the Israelis are using fairly traditional persuasion – interviews with journalists – as a persuasive tactic in the service of the three key points. This interview and in particular this quote speaks directly to the second key point regarding allies. We see an Argument from Barak regarding the Iranian nuclear project and what should be done and that Argument is aimed squarely at allies, especially the US.

Regardless of your opinion on this issue, please see the persuasion planning and execution in it. Focus on the three key points that express the strategy and their implications for persuasion. Learn how to devise strategy that is this clear, behavioral, measurable, and operational.

Of course, this is just one fool’s opinion and he’s cherry picking examples to fit his argument. Show me something other than your sarcasm, Steve.

Okay. How about this demonstration of sophistical statistics.

We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance.

This summary gives it up nicely. Three researchers reanalyzed the published statistics in 49 papers in either the 2005 issues of Journal of Personality and Social Psychology of the Journal of Experimental Psychology: Learning, Memory, and Cognition, two well respected psychology journals. These particular papers were chosen because another research team had contacted the authors of the studies in a previous project, merely asking for a copy of the datasets used in the publications. Some of the 49 authors provided the data, some didn’t. After waiting five years (5 years!!!), the current team pulled the studies and checked the results sections for errors and inconsistencies.

As the researchers noted in the Abstract they found that authors who would not disclose data had more errors of statistical analysis and that the tests of statistical significance were much more likely to be extremely close to the p < .05 level. Here’s a pie chart that displays errors by data shared or not.

Even among researchers who shared the data, there were errors in their analyses, but just eyeballing the differences between the two groups, you can see that folks who refused to share data (after five years!) made more of all kinds of errors. And, the differences are Medium to Large Windowpanes, 35/65 to 25/75 differences, so they are obvious, practical, relevant. What’s more, authors who did not share had data with marginal results; they were more likely to report p values at or near the traditional .05 alpha while authors who shared data found results with much smaller alphas (> .001). Here’s a bar chart to illustrate.

You can see that the gray bars represent authors who did not share and that they had more errors at or near .05 and .01, traditional, almost ritualistic, markers of effect. You can understand why they were reluctant to share.  If you found results, but didn’t share your data, chances were good the results were small effects that you had to finagle to achieve even statistical significance. No wonder these authors found good reasons to withhold their data even after five years of waiting.

Oh, and if you’re not familiar with the publication ethics of publishing in these journals, you need to know that all authors have to sign a contract when they publish stating that they will share data when it is requested. This is not a matter of personal preference or taste; it is a professional standard of behavior with your signature of agreement and consent on it.

Authors who don’t share data are not doing good science. Their inaction violates both the letter and spirit of a contractual agreement they made when publishing. They obviously withhold data because they know they engaged in sophistical statistics and if anyone else ran the data, they’d expose the rhetorical research.

So, through a thoughtful research project on statistical analysis in peer review journals we actually learn a lesson about human nature and persuasion.

All Bad Science Is Persuasive!

Wicherts JM, Bakker M, Molenaar D (2011) Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results. PLoS ONE 6(11): e26828.

doi:10.1371/journal.pone.0026828