Loud Pipes Research Study
Couple of questions:
1. Do you think that this study is way too generalized to prove one way or another that louder pipes have an effect on cagers. The combination of variables, e.g. Window up/down, radio on/off, speed, size of pipe, direction, etc., is endless and will weaken the conclusion. A very specific question like "Does a 2008 Softail Deluxe with Vance and Hines Longshots approaching at 30 mph perpendicular to a vehicle at a stop sign with the windows up and the radio on increase the awareness of the driver of the vehicle to the motorcycle?" will lend more credibillity to the study.
2. How do you approach this study without making the driver of the vehicle aware that the study is about pipes on motorcycles. Telling the driver of the vehicle this will automatically make him or her more aware of the motorcycle.
Good luck.
I have heard quiet pipes, sort of loud, pretty loud, loud, really loud and damn, that hurts my ears loud. Your study is going to need a variety of bikes with a variety of pipes to determine the level of sound required to elicit a reaction for drivers in various circumstances. I wonder too if the color of the bike, number and intensity of lights and color of the bikers helmet and clothes would make a difference. Could turn into a pretty complicated project but I'd sure like to see what kind of results you can come up with.
Interesting, doesn't the assumption that the noise is important create a bit of a validity problem? More specifically, how are you going to deal with the problems of external validity, especially as it relates to measurement of noise as a factor in collission avoidance. Seems there is a whopping problem with generalizability ... what do you think?
I vote for multiple linear regression. Too many independent variables to ignore them all here, and MLR allows you to use a least squares function. I like this approach, especially for a study of this type, since it allows you to better control one or more of the model parameters.
Of course, one of the immediate fallacies in using any linear regression is the problem created by the assumptions that the sample is selected at random from the population of interest. By the way, how do you plan to pick the population of interest? Cagers? Bikers? Experienced bikers? Or are you using a convenience sampling technique?
Speaking of convenience, there is that pesky problem of the full derivation, but that's something for later...
How do you plan to address the problems of residuals against the explanatory variables in the model? Once you define any residuals then shouldn't they be combined in the variables for testing? I've considered this problem and thought that maybe a normal probability plot might do the job, but then that redefines the method itself...
I'd also say that you start with Kim, Park & Bien's (2007) paper on rear-end collision avoidance; this will give you an idea of the problems with the longitudinal control method. That's certainly an issue in this type of research, don't you think?
In addition, I'd say the study should use a hierarchical approach to control for driver intelligence (especially in terms of experience, training, etc.) and the variables for collision conditions (i.e., traffic conditions, environment, time, weather, etc.). That's one reason I'd say that a linear regression approach is likely best for this type of study. Of course that brings us back to the validity problem and generalizability.
Then again ... maybe you're just looking for a quick descriptive study that supports a narrowly defined hypothesis such as "Loud Pipes Save Lives."
That approach certainly provides a clear distinction for generalizability. Not sure you can get much more generalized than the bumper sticker approach. If it fits on a bumper sticker then it must be true.

I'd have to run a pilot and then run a power analysis to see how many participants I would need, but I was just planning to recruit only drivers through traditional participant recruitment avenues. It's just a sample of the pop, but with enough people it should be a fairly accurate representation. That does bring up the point that this could be a very long study to conduct.
The motorcycle riders would be confederates so I would only need a few of them - pretty much one per exhaust type. The course would have to be a closed course, like a large car park as someone mentioned in another post. This is mainly for two reasons, firstly, I'd never get IRB approval if I proposed doing this on the open road, and secondly, I can control for sound much better if the course was closed. Yes, this decreases external validity, but that's always the trade off in research it seems.
Lastly, this study won't be able to answer the question of do loud pipes save lives, as there are too many assumptions tied up into that statement, but it would help to shed light on 'do loud pipes increase awareness of bikers on the road?' And from there, I would hopefully be able to drill-down a bit and see in what situations do they increase awareness, and in what situations do they not.
2. How do you approach this study without making the driver of the vehicle aware that the study is about pipes on motorcycles. Telling the driver of the vehicle this will automatically make him or her more aware of the motorcycle...
I have heard quiet pipes, sort of loud, pretty loud, loud, really loud and damn, that hurts my ears loud. Your study is going to need a variety of bikes with a variety of pipes to determine the level of sound required to elicit a reaction for drivers in various circumstances. I wonder too if the color of the bike, number and intensity of lights and color of the bikers helmet and clothes would make a difference. Could turn into a pretty complicated project but I'd sure like to see what kind of results you can come up with.
I hadn't really thought about the visual aspect of the riders. I was thinking that this study would only look into being aware of a rider without seeing them...but that does introduce a good point - colour, lights, and even sunlight conditions would all have an affect. Shoot, I may just tape their rearview and side mirrors to remove that variable. So all they would have to rely on is sound until the bike is in front of them. I'll have to give that more thought.
How I know I have one with loud and one stock pipes. Big different in a days daily ride.
My neighbors hate my noisy bike (as they call it), but I"m ok with that....
The Best of Harley-Davidson for Lifelong Riders
I am personally sick of all the loud pipe slammer threads myself.......Harleys have been loud for 100 years. To be seen and heard is part of the Harley heritage. Like it or not.
So a preliminary hypothesis is that loud pipes do increase awareness due to the dual-coding theory. Basically it has been found that audio and visual perception occurs along different channels in the brain, and these channels have limited capacity. So, when a cager is driving along, it could be assumed that their visual channel is more quickly filled to capacity, but their audio channel is not. Taking this into account perhaps the sound of a motorcycle's pipe will have more potential to 'get to the brain' and be processed resulting in an increase in awareness of that sound. I haven't done a lit review or anything so this still remains just an idea.
Another thing to note is even if I find that loud pipes do increase awareness, I would probably have to conduct a follow-up study on social responsibility to investigate the trade-off between heightened awareness and "pissing off the neighbors" for lack of a better word.
For this second piece, you need to take into account the other environmental factors such as; surrounding ambient noise ( big city highway vs country roads ), radio volume, windows up or down, A/C on or off, blond or brunette.
I think this has the potential to be an interesting study. A corollary study would be at what volume does a "typical" cage driver become aware and at what point does hearing damage to the rider begin. I think you will be very surprised by the results of this piece!
Last edited by DoomBuggy; Jun 15, 2009 at 11:34 AM.






