In Psych 144, I always emphasize the importance of distinguishing phenomena (things that we observe) from theories (explanations for those phenomena). For example, there are sex differences in scores on the math portion of the SAT. No question about it. But there are many different ways to explain this fact that might involve biased tests, a biased society, genetics, and so on.
But this blog has nothing to do with sex differences. What it does have to do with, though, is the fact that no one has commented on any of my blogs. That's the phenomenon of interest for today. And in an exercise straight out of Psych 144, I'm going to generate as many theories to explain this phenomenon as I can. I'll worry about which ones are correct later.
1. Students never look at blog.
2. Students do not know how to post comments.
3. Blogs are too boring to comment on.
4. Blogs are incomprehensible.
5. Students are too dense to think up comments.
6. Settings do not allow comments (because blogger is too dense).
7. Students are afraid to interact with instructor.
8. Students are afraid of appearing to be apple polishers.
9. Students find topics uninteresting.
Other suggestions?
Thursday, February 21, 2008
Friday, February 8, 2008
In Praise of Statistical Thinking
In an essay in the Chronicle of Higher education, Eric G. Wilson (an English professor at Wake Forest) writes "In Praise of Melancholy."
Melancholy, by the way, is just a fancy word for depression. The word itself derives from the ancient Greek meaning "black bile," because it was once thought that depression was caused by an excess of black bile in the body.
Anyway, the author of the essay mentions that recent polls show that 85% of Americans rate themselves as happy. He also claims that this is a disturbing trend because melancholy has been the inspiration for much of the world's great art, music, and literature. Yet we are content to "annihilate" it through positive psychology, psychotherapy, and the use of antidepressants like Prozac.
Although there are a lot of interesting things about this argument, there is a fundamental problem that prevents it from ever getting off the ground ... and it's something that I emphasize over and over in Psych 144. In the abstract it's this: evaluating a claim about a statistical relationship requires a comparison of one variable across levels of the other. In the concrete: the author claims that people are happier than they used to be, but he only presents data on how happy they are now. Who's to say they weren't just as happy in the "olden days?" If they were, then the whole idea that we are in the process of "annihilating" melancholy falls flat.
I'm not an expert on happiness (or what psychologists are more likely to refer to as "subjective well being"), but I'm pretty sure there are data out there that show whether or not there has been a change in the happiness levels of Americans across time. I'm also pretty sure that those data show that there really hasn't been much change over time--certainly not changes on the order of what the Wilson suggests in his essay.
But this is a blog, not a journal article. So you'll have to look up those data yourself.
Melancholy, by the way, is just a fancy word for depression. The word itself derives from the ancient Greek meaning "black bile," because it was once thought that depression was caused by an excess of black bile in the body.
Anyway, the author of the essay mentions that recent polls show that 85% of Americans rate themselves as happy. He also claims that this is a disturbing trend because melancholy has been the inspiration for much of the world's great art, music, and literature. Yet we are content to "annihilate" it through positive psychology, psychotherapy, and the use of antidepressants like Prozac.
Although there are a lot of interesting things about this argument, there is a fundamental problem that prevents it from ever getting off the ground ... and it's something that I emphasize over and over in Psych 144. In the abstract it's this: evaluating a claim about a statistical relationship requires a comparison of one variable across levels of the other. In the concrete: the author claims that people are happier than they used to be, but he only presents data on how happy they are now. Who's to say they weren't just as happy in the "olden days?" If they were, then the whole idea that we are in the process of "annihilating" melancholy falls flat.
I'm not an expert on happiness (or what psychologists are more likely to refer to as "subjective well being"), but I'm pretty sure there are data out there that show whether or not there has been a change in the happiness levels of Americans across time. I'm also pretty sure that those data show that there really hasn't been much change over time--certainly not changes on the order of what the Wilson suggests in his essay.
But this is a blog, not a journal article. So you'll have to look up those data yourself.
Friday, February 1, 2008
The Meaning of Imply
While lecturing the other day on correlational research, I had a thought about the famous dictum, "Correlation does not imply causation." No, I'm not going to try to debunk it. But I am going to suggest that it be reworded because the word imply is ambiguous.
One meaning of imply is something like guarantee. This is the intended meaning in "Correlation does not imply causation." The simple fact that two variables are statistically related does not guarantee that the underlying relationship between them is causal. It could be causal, but this is not a sure thing.
A second meaning of imply--which is probably more common in everyday conversation--is something like hint at. So if my wife casually notes how full the trash can in the garage is getting, some might say that she is implying that I carry it out to the dumpster. (Note that I wouldn't say this ... but some might.) So "Correlation does not imply causation" could be interpreted to mean that the fact that two variables are statistically related does not even hint at there being a causal connection between them.
But this isn't right because it does hint at it. For example, if caffeine consumption is correlated with later miscarriages, then this suggests that researchers ought to take a look at caffeine consumption as a possible cause of miscarriages. It doesn't guarantee that caffeine consumption is a cause ... but it could be.
So let me propose a rewording of the old dictum: "Correlation does not guarantee causation."
One meaning of imply is something like guarantee. This is the intended meaning in "Correlation does not imply causation." The simple fact that two variables are statistically related does not guarantee that the underlying relationship between them is causal. It could be causal, but this is not a sure thing.
A second meaning of imply--which is probably more common in everyday conversation--is something like hint at. So if my wife casually notes how full the trash can in the garage is getting, some might say that she is implying that I carry it out to the dumpster. (Note that I wouldn't say this ... but some might.) So "Correlation does not imply causation" could be interpreted to mean that the fact that two variables are statistically related does not even hint at there being a causal connection between them.
But this isn't right because it does hint at it. For example, if caffeine consumption is correlated with later miscarriages, then this suggests that researchers ought to take a look at caffeine consumption as a possible cause of miscarriages. It doesn't guarantee that caffeine consumption is a cause ... but it could be.
So let me propose a rewording of the old dictum: "Correlation does not guarantee causation."
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