Slate and Rolling Stone continue to be some of my favorite websites strictly because of this level of reporting. When I first heard of Gottam's claim in a separate article on another site my immediate thought was "anyone can. They'll just say everyone stays together and get 80% accuracy." This is the problem with non-scientific researchers. They don't <i>want</i> to learn about statistics. They want easy, simple claims that everyone can understand. The corresponding problem with the media is that they do not understand science well enough to be critical of researchers' claims.
I agree with the several commenters who have commented that statistically speaking, the cited researcher has not yet achieved "prediction." He has done some interesting curve-fitting on a smallish data set, but has not tested his curves on fresh data sets--something that every responsible scientist must do sooner rather than later, if the scientist wishes to speak of "prediction." I indicated my agreement with other comments by a bunch of upvotes, and also posted the citation for Feynman's "Cargo Cult Science" lecture.<p>That said, my wife and I have been using a couple of Gottman's recent books to reexamine aspects of our twenty-six-year-plus marriage, and there is some good advice for couples in Gottman's writings. It may not all be rigorous science, but clinical experience based on close observation of many couples can be helpful to any one couple who want to enjoy their marriage more and to pass on advice to their children (four, in our case) about how to have happier marriages in the next generation. So once we all agree that proper science involves TESTING models developed through analysis of one data set on other data sets, we can start some application of the clinical observations on ourselves and see what we think after trying this out at home. My wife and I have been pleased to become acquainted with these writings and to discuss them together.
Scribd link to original paper:
<a href="http://www.scribd.com/doc/28394063/353438" rel="nofollow">http://www.scribd.com/doc/28394063/353438</a><p>JSTOR link to original paper:
<a href="http://www.jstor.org/stable/353438" rel="nofollow">http://www.jstor.org/stable/353438</a>
tl;dr Researcher coded fairly short conversations between about 60 married couples, then six years later he fed their marital status and self rated happiness into the computer. He used modeling software to create an optimal equation for prediction of marriage and happiness based on the variables he had coded for originally.<p>It's a good first step, but it's not strictly predictive. It's like an older financial model.
I'm surprised to that the discussion here is only about how Gottam came to his results, although that's fair game and I agree with you.<p>But there is learning here, too. My mother is a psychologist and she says that the most destructive thing she sees in marriages is the level of toxicity. Couples can have lots of fights, but if they never lose respect for each other, they're usually ok. It's when this respect devolves that trouble begins.
There's a journal article criticizing that exact assumption: "The Hazards of Predicting Divorce Without Crossvalidation"<p><a href="http://www3.interscience.wiley.com/journal/118971429/abstract?CRETRY=1&SRETRY=0" rel="nofollow">http://www3.interscience.wiley.com/journal/118971429/abstrac...</a>
Is about whether metrics gathered from a 15 minute session, in which a man and woman argue a contentious issue, can accurately predict divorce rates. The metrics are primarily measured using facial recognition software it seems. John Gottman, who claims to predict divorce with a 91% accuracy, is the main subject. QI.
If he now interviews a new set of couples, does his predictions according to the developed model, and it _still_ gives high accuracy, then we can call it science.<p>The power of a good model lies not in how good it predicts what you know, but how it can be used to model unknown/new phenomena.
I'm surprised he didn't use other resampling (e.g. <a href="http://en.wikipedia.org/wiki/Resampling_%28statistics%29" rel="nofollow">http://en.wikipedia.org/wiki/Resampling_%28statistics%29</a>) techniques to validate the models. Not that resampling is a perfect answer, but...