The study made some strong remarks about the kind of people who would modify their car’s exhaust. If psychopathy and sadism aren’t bad enough, apparently loud truck owners would do even worse.
- A professor in Ontario, Canada, has released results of a study of people’s attitudes toward loud vehicles.
- Having asked undergraduate business students whether they think such vehicles are “cool,” the result, not totally surprisingly, was that many of them do.
- Respondents also scored high on the “psychopathy and sadism” scale, but the study was only for cars. Truck and motorcycle owners, the study suggests, might score even worse.
A new study by Western University in Ontario says that if you’ve got a car with a modified exhaust system, odds are you’re a guy and probably also psychotic and sadistic. Slapping a Cherry Bomb glasspack on your Monte Carlo doesn’t (necessarily) mean you’re a Ted Bundy–level psycho, but the data someone points to a personality that enjoys inflicting unpleasantness on others. The study—catchily titled, “A desire for a loud car with a modified muffler is predicted by being a man and higher scores on psychopathy and sadism”—was commissioned by professor Julie Aitken Schermer, who heard many a loud car in London, Ontario, and wondered what kind of person would want their car exhaust to be louder than normal. She probably could have saved a lot of time by simply looking up Cadillac Escalade-V registrations. …
You’re right to be sceptical. The paper is poorly written, and overstates many of the results they found. The correlations identified between the car score and the dark tetrad scores aren’t really very high, the highest is 0.51! They produced a regression model and deduced that because the F-test had a low p value that the dark tetrad scores predicted the car score. The F-test, for clarity, determines if a model predicts the response variable better than a model with no explanatory variables.
Also worth noting that there were stronger correlations between the explanatory variables than for any of the explanatory variables with the response. They should have included interactions in their regression model to incorporate this, or even better tried a set of models and compared them with ANOVA or similar. As is it’s impossible to say if the model they found is actually very good. It only explains 29% of the variance which… Well, it’s a statistic which is better for comparing models, but it suggests quite clearly they most of the variance in the car score is not explained but the dark tetrad scores.
There’s a smattering of evidence in here that there’s some statistical link between the scores, but it’s not been well explored or presented, and there are issues with the statistical approach. Based on some comments in the discussion section I’d agree with your suggestion that the author is simply trying to confirm their hypothesis.
What are you talking about? A correlation coefficient of .5 is in the ballpark of or bigger than the correlation between human height and weight. I wouldn’t be surprised if the bottleneck isn’t in the reliability of the measurement.
Unmodeled interactions here also would only be able to suppress the explained variance - adding them in could only increase the R-squared!
"They produced a regression model and deduced that because the F-test had a low p value that the dark tetrad scores predicted the car score. The F-test, for clarity, determines if a model predicts the response variable better than a model with no explanatory variables. "
Yes, when you wanna know if a variable predicts another, one thing you can do is that you compare how well a model with the predictor included fares compared to a model without the predictor. One way of doing that is by using an F-test.
In case your 101 course hasn’t covered that yet: F-tests are also commonly used when performing an analysis of variance.
“As is it’s impossible to say if the model they found is actually very good.”
You say that after quoting explained variance, which is much more useful (could use confidence intervals… but significance substitutes here a little) in this context for judging how good a model is in absolute terms than some model comparison would be (which could give relative goodness).
Your criticism amounts to “maybe they are understating the evidence”.
Do you think the paper drew sensible conclusions, or do you just not like my arguments?
This is fair enough, my background is not in social research so to me 0.5 is a moderate correlation. Not sure what you mean by the ‘bottleneck’ here, are you suggesting that the correlations could be higher with a different survey?
Given that the explanatory variables are in some cases more strongly correlated with each other than the response, do you think the model without interactions is likely to be an appropriate way to analyse the relationship between the response and the explanatory variables? It doesn’t at all make sense to me to do one single regression model and say “The F test says this is a good model, so the explanatory variables explain the response”, especially with a relatively low R^2, and given the fact that there is evidence of multicollinearity presented alongside!
The paper presents the fact that they have done a regression model with a few good significances without any real analysis of if that model is good. We don’t see if the relationships are linear, we don’t see if the model assumptions are met. Just doing a regression is not enough, in my opinion.
There’s no need to be rude. It’s perfectly acceptable to disagree with me, but you could do it politely.
Yes, I’m well aware, although I’m not sure what your point is. They haven’t done any analysis of variance.
My point is that they haven’t made any effort to find a model that best fits the data, they have just taken all the available variables, smacked them into python or R or whatever, and written down the statistics it spits out. There’s no consideration in the paper given to interpreting the statistics, or to confirming their validity.
From the study:
Not only was p-value for age clearly not significant, the confidence interval for the coefficient was [–.21, .17]… This includes 0 ffs! There’s no evidence here that there is greater endorsement of the car items in younger respondents. Why was age even included in the model in the first place, given that the correlation was near 0?
Like I said - there is some evidence here of an interaction, I’ll concede that in context the correlation isn’t bad for 2 of the dark tetrad items, Wild and Crafty, but the analysis they have used to present this information is not well thought out or presented. Personally I don’t think that a linear regression model is even the right way to analyse the data they have, I especially don’t think this regression model is a good way to analyse the data.