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  1. #1
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    Wow! How incredibly time consuming this must have been. I'm going to have to read through it a few times to fully appreciate all the information you present.
    Roland


  2. #2
    Registered User Topcat's Avatar
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    Map Man,
    I hate to mooch others work but i would love to see the raw data on this study. I do statistics for work and also teach it. This would be great for me to use, something different and interesting compared to the dry stuff in our curriculum. Thanks for the interesting analysis.

  3. #3
    LT '79; AT '73-'14 in sections; Donating Member Kerosene's Avatar
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    Whoa, someone's got a little bit of extra time on his hands!

    This is quite interesting, map_man. I'll bet that the ATC might be interested in your analysis.

    When all is said and done, though, I hope that no one (except potential record-setters) use this to modify their thru-hike. A lot of the pre-planning goes out the window when you get an injury, hit a big storm, get off the trail with your new-found buds, forget that the post office is closed on Sunday, etc. They will, however, be able to classify themselves in retrospect and see how they stack up to "Joe Hiker".
    GA←↕→ME: 1973 to 2014

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    Donating Member Cuffs's Avatar
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    That is one piece of phenomenal work! I do have a couple questions for you...

    Do you have the numbers on the genders? And what about the age brackets?

    I read many many trailjournals, and those are the 2 things I look for. Looking to find people (women) in my age group (35-40) that are doing a thru. I find I can understand and empathize with them and learn alot from them.
    ~If you cant do it with one bullet, dont do it at all.
    ~Well behaved women rarely make history.

  5. #5
    GA-VA 2005, VA-CT 2007, CT-ME ??
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    Okay. Wow. What a cool study. Let's put this one in the Articles section. Great work, map man.

    -Mark

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    Simply awesome!
    On step at a time, One mile at a time. All the way Baby!

  7. #7
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    Wow, Oh my godness. I can not imagine how much time was spent on this. Maybe this thread should be moved to the Articles section. What do you think Doctari?
    AT Troll (2010)
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  8. #8
    tideblazer
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    I think your right, Atroll. This is a good piece of work. Still, I'd like to hear more about the methods.

    How were the averages weighted?

    What did the distributions look like? Normal? How did you deal with outliers?

    VERY COOL analysis, map man.... thanks for sharing your work.
    www.ridge2reef.org -Organic Tropical Farm, Farm Stays, Group Retreats.... Trail life in the Caribbean

  9. #9

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    Some thoughts.

    The TJK's were grouped without regard to start date, start year. It hasn't been demonstrated that this is a reasonable assumption. There could be year-to-year mean differences, or even start month-to-month differences that have been masked by taking an overall grand mean. Further, this grand mean may be biased if there are significant year-to-year or start month-to-month differences.

    Kudos for defining the sample population. It is a self-selected sample, not a random sample however. That bothers me, not necessarily anyone else. I will say though that while everyone who starts hopes to finish, the reality is that about 80% don't.
    The result of all this journal reading and number calculating is a DESCRIPTIVE study of a certain thru-hiking population (NOBO thorough journal keepers) and may or may not be representative of all thru-hikers (though I suspect it's probably close).
    It is also stated that journalers with ambiguous zero days were dropped, another subsetting. Just splitting hairs here .


    But I think it can be useful for some hikers who are so inclined to get an idea of the rate of progress a typical hiker might experience on their way there.
    I may be taking this out of context but ... the typical hiker fails. About 80-85% of them. The population that these numbers may apply to are successful thru-hikers. Drawing inferences is not possible, because starting out, no hiker really knows if they are going to finish. This is a descriptive study, as stated.


    Regarding zero days. On average, by the results presented, it takes about 16 days to get to Fontana. IMO, that may not be enough time before the long reality of the journey sets in. In other words, injuries may not have seriously developed yet, mental fatigue may have yet to develop, the weather could still go bad, etc. I don't feel like the data is sufficient for the following.
    After having read a fair number of journals for my own enjoyment I hypothesized that hikers would need to take a lot of the short term breaks in their earlier days on the trail to cope with hiker's fatigue and with the sometimes nasty weather in the southern Appalachians in March and April, and that these short term breaks would lessen in frequency as a hiker walked north. Turns out I was dead wrong, as Table 5 shows.
    The data could be available. The journals might give a reasonable idea as to the purpose of the zero day. "I was nursing a sore tendon", "I needed a beer", "I had to make repairs", etc.

    Of course, standard errors or confidence intervals would greatly improve the understanding of the means . There is no description of the variability of these estimates.

    A very detailed analysis though and I applaud the effort .
    "Sleepy alligator in the noonday sun
    Sleepin by the river just like he usually done
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  10. #10
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    Quote Originally Posted by Alligator View Post
    Some thoughts.
    ...
    A very detailed analysis though and I applaud the effort .
    Actually, I didn't understand may of the thoughts in between.
    For me it is very important to look at only data for those who completed the trail.

    I recalculated Table 4 for my 2004 hike. I took the number of days in Table 1, multiplied them by the days hiked (100%-%zero)/100 and adjusted by the (total non-zero/my nonzero days). Even though the number of days I hike was relatively far from the mean, I was only off from these calculated days by +/- 1 day. Also, almost all of the discrepancy can be explained by snow around Franklin and slowing down for the last section between Stratton and Katahdin. I was over a day faster for these two sections in 2005. (On the other hand I lost a couple days for snow near Erwin and a couple days for tendonitus in NJ in 2005)

    Was there a greater variation in non zero day pace for this last section? It seemed to me that half the hikers were putting their head down and charging to Katahdin and the other half were trying to keep the hike from ending.
    Rambler

  11. #11
    2006 Thru-hiker in planning dje97001's Avatar
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    Alligator, the value is in that looking at hikers who finish the whole thing may provide lessons as far as pacing (esp. at the beginning when you are worried about people passing you, or wondering whether you started a week or two too late to make it to ME in time) and setting more realistic approximations for mail-drop locations.

    It might be interesting to examine those who don't finish and compare daily mileage... maybe they pushed themselves too hard too fast.... maybe they realized that they would never make it to maine at the speed they were capable of... all of it is interesting stuff.

    Yes, it is obvious this isn't a predictive model... but who cares? Consider the years he examined to be the population that exists. The means for the population may be significantly different than the means for those who don't complete the thru, those who don't journal online at trailjournals.com, those who completed in a previous year (not included in the analysis), or those who completed the hike by sections, etc. ... but he didn't claim this extended to those pops. Still, to worry about kurtosis or skewness in this case is pretty much a waste of time--most people don't even bother checking for that stuff anyway... they just live and die by the central limit theorem. Sure, an exceptionally rainy year may have slowed people early on (resulting in more zero days) but then really dry years may have resulted in faster paces (with fewer zeros). It should all even out (life is all about probabilities). We're only talking about 5 years (and ideally we'd have more) so there is likely to be a larger SE than you'd like, but without anything else to use, this is damn good stuff. You could always compare the 3 measures of central tendency to get a better idea of whether or not you have some outliers screwing with the data if you are really worried about it, but again, why bother, this is really interesting to chew on. Thanks map man!

  12. #12
    Registered User Peaks's Avatar
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    Lot of great work. Thanks Map Man.

    Roland Mueser did a limited survey in 1988 of thru-hikers. His survey shows a mean of 174 days, and 24 zero days average. I'd say it's a close correlation. So, while many things have changed, your analysis shows that some things have not changed.

  13. #13
    tideblazer
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    I think the last two folks who've responded to Alligator have missed his point that the data is good, but there really is no way of knowing how close to reality it is without understanding the nature of the data set. If you have a heavily skewed distribution, then mean averages can be very misleading. In those cases, the outliers could be dropped, the data could be weighted (he already said they were, but by what factor?) or the medians used in place of means.

    Like what gater said, the sample population is what it is. It's like all the psychology studies that can only be extrapolated to college students, becuase they were the sample group.

    All in all, valiant effort, and I think it can be made better to really solidify the results. Alligator isn't just being picky, but adhering the assumptions of stats (normal distribution, random sample, ect.).

    Interesting conversation.
    www.ridge2reef.org -Organic Tropical Farm, Farm Stays, Group Retreats.... Trail life in the Caribbean

  14. #14
    Registered User camich's Avatar
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    I think this is great. Thanks for all the time you put into it. I'm always happy to have additional information to help plan.
    Camich

  15. #15
    2006 Thru-hiker in planning dje97001's Avatar
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    I get all of that. I think we all do. But you know as well as I do that very few studies actually use anything other than convenience samples (which are non-random--I'm not talking about random assignment here) because of cost, time and difficulty in compiling the true pop list. So basically we all assume normality, again unless we look at the skewness (or how flat or peaked the distribution is), which no one does. I'm sure you could do it, but again, I'm not sure what good it would do. Just compare the mean to the median... the closer they are to eachother the less likely skew exists.

    But let's be honest, we aren't doing significance tests on this data, nor ANOVAs nor Correlations nor anything else for that matter. If you wanted a study that could be published in a journal you probably want to worry about these things--yet again, this is a content analysis not necessarily subject to the same issues of experimental research (samples for CA are often non-random). Frankly with a sample size of 105 (unless map man included Squeaky) there aren't likely to be substantial outliers (again, these people are already outliers from the "normal" population... most people wouldn't walk over 2000 miles). I think that this compilation of data is awesome in and of itself and really doesn't need anything else.

    I probably over-reacted, but Academics (who have had sufficient training in stats and methodology) commonly do what Alligator did: finding potential flaws/holes and making it apparent that if the flaws did exist then any conclusions would be shaky at best and then finishing it up by saying something nice about the effort--no knock on Alligator, I've seen it a million times. While it can be beneficial in improving the research, it also can be perceived as really jerky (esp. to people who aren't in academia). It is always easier to critique a study than to conduct one yourself. My apologies, Alligator, if my comment came across jerky.

    Anyway, none of that matters to anyone outside of grad students in quantitative programs, faculty who are obsessed with statistics and methodology and people who review/edit quantitative journal submissions (this last group is comprised of people who had previously been in the other groups).

  16. #16

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    1.
    Quote Originally Posted by dje97001 View Post
    Alligator, the value is in that looking at hikers who finish the whole thing may provide lessons as far as pacing (esp. at the beginning when you are worried about people passing you, or wondering whether you started a week or two too late to make it to ME in time) and setting more realistic approximations for mail-drop locations.
    2.
    Quote Originally Posted by dje97001 View Post
    Yes, it is obvious this isn't a predictive model... but who cares?
    IMO, the above two statements are contradictory. But, referencing 1., that's why a confidence interval/s.e. would be useful. If it says that it takes a mean of 8 days +/- 0.5 days that would be helpful. If however, it says it takes 8 days +/- 3 days, that creates a different situation. See?

    Quote Originally Posted by dje97001 View Post
    It might be interesting to examine those who don't finish and compare daily mileage... maybe they pushed themselves too hard too fast.... maybe they realized that they would never make it to maine at the speed they were capable of... all of it is interesting stuff.
    Sure.
    Quote Originally Posted by dje97001 View Post
    Yes, it is obvious this isn't a predictive model... but who cares? Consider the years he examined to be the population that exists.
    It is most certainly a sample and not the population.
    Quote Originally Posted by dje97001 View Post
    The means for the population may be significantly different than the means for those who don't complete the thru, those who don't journal online at trailjournals.com, those who completed in a previous year (not included in the analysis), or those who completed the hike by sections, etc. ... but he didn't claim this extended to those pops.
    I fully understand that. But if there are actual differences, saying the mean is the same for all groups (even just thruhiker groups) masks what may be important underlying differences. Map man is making a serious effort. I understand that, that is why I gave what he presented a serious review. Items placed up for Articles are subject to review. It makes them better. It's extremely common to have things reviewed. Relax, I didn't mark the article rejected for review box .

    Quote Originally Posted by dje97001 View Post
    Still, to worry about kurtosis or skewness in this case is pretty much a waste of time--most people don't even bother checking for that stuff anyway... they just live and die by the central limit theorem. Sure, an exceptionally rainy year may have slowed people early on (resulting in more zero days) but then really dry years may have resulted in faster paces (with fewer zeros). It should all even out (life is all about probabilities).
    But if there are differences, that evening out may not have any meaning.
    Quote Originally Posted by dje97001 View Post
    We're only talking about 5 years (and ideally we'd have more) so there is likely to be a larger SE than you'd like, but without anything else to use, this is damn good stuff. You could always compare the 3 measures of central tendency to get a better idea of whether or not you have some outliers screwing with the data if you are really worried about it, but again, why bother, this is really interesting to chew on. Thanks map man!
    I didn't mention any distributional problems. Given his 105 samples, I wouldn't expect serious problems with his means. Also, I actually liked that he presented the medians as an alternative. He should be cognizant of any extreme outliers though. Further, I only asked for the SE, I haven't commented on how large it may or may not be.

    Sure, a lot of effort was put into it. It may certainly be reasonable to use. But care needs to be taken to ensure that any underlying biases are at least considered and hopefully controlled for. For instance, wouldn't it be interesting to know if there are age differences among hikers and if the sample was representative age-wise? Someone previously mentioned they pick a hiker similar to themself to compare to. And wasn't there a really wet year on the AT in that pool of data? Do Feb. starters really complete the trail on average the same as April hikers? It is important to consider factors such as these and not to immediately discount them.

    It is good stuff! I'd consider using it.
    "Sleepy alligator in the noonday sun
    Sleepin by the river just like he usually done
    Call for his whisky
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  17. #17
    2006 Thru-hiker in planning dje97001's Avatar
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    WRT the "contradiction"... I was making the statement that it wasn't predictive in the sense of an academic model. But there are massive differences between theory and practice. Theoretically, you can't make causal assumptions about correlational data (unless you've taken care of all of those pre-requisites, i.e. temporal ordering, etc.)... but practically? I would definitely use this data to "predict" where I will be at x days out... especially since this is the best compilation of numbers that I've seen.

  18. #18
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    Im goin to section hike from erwin tenn to hot springs nc in may has anyone done the section lately and whats it like trail wise ? Thanks.

    Rick

  19. #19

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    Quote Originally Posted by dje97001 View Post
    I get all of that. I think we all do. But you know as well as I do that very few studies actually use anything other than convenience samples (which are non-random--I'm not talking about random assignment here) because of cost, time and difficulty in compiling the true pop list. So basically we all assume normality, again unless we look at the skewness (or how flat or peaked the distribution is), which no one does. I'm sure you could do it, but again, I'm not sure what good it would do. Just compare the mean to the median... the closer they are to eachother the less likely skew exists.
    BTW, skewness refers to the distributions symmetry--heavy tails on the right, heavy tails on the left. Kurtosis refers to "peakedness".

    And I conduct my own research for applied science all the time.
    "Sleepy alligator in the noonday sun
    Sleepin by the river just like he usually done
    Call for his whisky
    He can call for his tea
    Call all he wanta but he can't call me..."
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  20. #20
    2006 Thru-hiker in planning dje97001's Avatar
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    Yeah thanks pal. I know that (lepto, platy, meso...). I was giving you options... consider it a grammatical mistake.

    The point is, whether you believe it or not, you and I probably agree on 98+% of this crap. Save possibly your stance on standardized vs. unstandardized (correlation vs. covariation) or maximum likelihood vs. least squares assumptions. I agree with your statements from a research standpoint. The funny thing is that those who haven't spent much time in stats/methods don't realize that these debates can be just as intense as the "hammock vs. tent" or even the dreaded "purist" debate.

    The point I'm trying to make--one that Chris made to me a while ago (but it took a while to accept)--is that there are already too many numbers for most people to spend much time on. Hikers understand that their mileage may vary. Confidence intervals, while very useful in gaining more precision (most of the time extremely desirable), in this case will simply obscure the value of these numbers to most people (i.e. they don't want to know that there is a 95% chance of them making it from Springer to the Georgia border in 7.25 to 8.45 days) they just want the best guess, for which the mean (or median) should suffice. Yes the pace for april starters may be different from march or feb starters for only the first 2 sections... but map man didn't ask for a critique. He didn't even ask that it be placed in the articles section. Such a detailed criticism without prompting will only make it less likely for people to share potentially valuable information. I for one, think that in its present form it is definitely of value to the hiking community.



    That set of numbers map man listed is complicated enough.

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