In 1994 I went surf fishing on the Outer Banks of North Carolina and up around Duck or Corolla there were a bunch of huge stumps washed ashore, remains of trees that had been swept into the Mississippi River and out into the Gulf of Mexico two years earlier by Hurricane Andrew's second landfall, in Louisiana.
Stop me if I've said that already.
My eyes weren't deceiving me - here's quantitative evidence Scott Brown dominated social media in the MA Sen special election
Over the past few years I've researched and written a bit - for general audiences and the survey research profession - on the implications for survey research of the trend away from landlines and toward cell phones. My latest on this topic is now up at Pollster.com, a report on updated federal estimates of the size of the cell-only and "cell-mostly" populations in the United States.
I have an interest in startups namely because I have a few ideas of my own. Here's a fantastic, thoroughly reported and beautifully written* story on how startups must conserve their resources these days.
* Disclaimer: The author is my wife
Among my interests is skiing, particularly in fresh powder, in the mountains of northern New England. This means I monitor all sorts of weather forecasts and ski area reports to try to figure out where best to get the goods on any given day.
So when academic researchers proffer empirical evidence that ski resorts exaggerate snow totals, well, that's one paper I'm going to read and evaluate closely. Throw in a "technology shock" earlier this year that may be stopping the trend in its tracks and the geek in me says: "Pass the popcorn."
Via Eric Wilbur on Boston.com I learned today of a preliminary study by two Dartmouth researchers who compared snow reports by ski resorts throughout the U.S. and Canada with government snowfall data.
I'm still poring over the study with a critical eye on its methodology, particularly re the timing and measurement of snowfall by resorts and government agencies. It appears the conclusion is based mainly on a "weekend effect" in which resorts reported 23% more snowfall on Saturdays and Sundays than on weekdays, when there was no such statistically significant effect in government snowfall total data. (The authors found that ski areas reported more snow than government observations on all days of the week, but acknowledge that that could be because of the location of ski areas -- presumably sited in locations most favorable for snow -- vs government weather observation stations.)
The researchers, Jonathan Zinman and Eric Zitzewitz, say the weekend effect was larger early in the season and in January and March, while being essentially zero during Christmas week and April/May: "These results are consistent with skier decisions being more sensitive to snowfall early in the season, with purchase decisions being made in advance during holiday periods, and with skiers being less sensitive to new snow during the 'spring conditions' portion of the season."
The study also found: "Weekend effects in snow reporting are larger for resorts with more expert terrain and those within driving distance of population centers. This is consistent with expert skiers valuing fresh snow more highly and with resorts near cities having more potential to attract weekend skiers."
Particularly fascinating is the researchers' report that the exaggeration fell sharply since January of this year, when Skireport.com introduced an iPhone app that makes it easy for skiers to give their own real-time reports. "But first-hand reports spike only at resorts with adequate coverage from AT&T's data network," Zinman and Zitzewitz wrote, "and these covered resorts experience a disproportionate post-launch drop in exaggeration."
Lots to chew over here. Meanwhile, there's no way to put lipstick on the pig of a forecast for New England ski areas tonight and tomorrow: Heavy rain.
I've posted several times here about a big question facing legacy and new news organizations: Will people pay for news online?
There's some new data that purports to answer that question. The results could be viewed as glass-half-full (yes, a lot of people will pay) or glass-at-least-half-empty (but they won't pay very much at all). One big question is to what extent the results can be considered representative of the entire market of online consumers. I have my doubts.
Today the Boston Consulting Group released results of a survey in nine countries that purported to find about half of American online consumers and upwards of 60 percent in several western European nations say they would pay for only news -- but what they'd pay ranges from only around $3/month in the United States and Australia to $7 in Italy. For one anecdotal comparison, I currently pay $46/month for a seven-day-a-week subscription to the dead-trees edition of the major metro in my area, and around $25/month for the six-day-a-week local paper.
The BCG news release quoted one of the firm's senior partners saying this new revenue could help offset one to three years of expected declines in advertising revenues. Neither the release nor the New York Times report on the study addressed whether that estimate factored in possible further declines in ad revenues from imposition of online pay walls, which inevitably would significantly reduce traffic to sites that go that route (by half or more, if we can extrapolate from the willing-to-pay data).
The full survey does not appear to be online and I've been too busy today to explore this further with BCG but their news release says this about the survey methodology:
The survey was conducted via the Web in October. A total of 5,083 respondents participated in nine countries: the United States (1,006 respondents), Germany (1,006), Australia (529), France (510), the United Kingdom (506), Spain (505), Italy (504), Norway (259), and Finland (258). The respondents were equally divided between men and women and among four age ranges. Respondents came from throughout each country, except in Australia, where the results were deliberately skewed toward Melbourne and Sydney.
I'm going to guess these were not probability samples but drawn from volunteer online panels. Aside from the broad question of the validity of non-random surveys, I would suspect a real possibility of bias for a survey on the topic of online news when respondents are sampled from a panel of people who are so active online that they sign up to take Internet surveys. I can only speculate but they may differ from online users in ways that make the BCG survey -- as fairly bleak as it is for the news industry -- overly optimistic in its assessments of who would pay for online news and how much. Other questions to ask about this survey include how the would-you-pay questions were worded, and how (if at all) the results were weighted -- for example, do the proportions in the four equal age ranges match the age distribution of the online population?
UPDATE: Still more data out today, this time from Forrester, which estimates only 20% of Americans would pay for online content. This is from a U.S. mail survey of 4,711 consumers. Again, sorry I don't have the time to dive deeper into this (nor do I have $499 to shell out for the complete Forrester report) but one question I'd ask is: Does the 80% of Americans who say they wouldn't pay for online content include the 20% who still don't have access to the Internet? If so, re-percentaging the results so they're based only on online users would bump that 20% to around 25%. I'd also ask the usual questions about sampling (was the sample drawn from a panel or from all U.S. households or ???), question wording, data weighting, etc.
Once again I have let this posterous go a spell without fresh content. Sorry. For now, see my survey research blog for some of the latest in my world.
Alan Mutter has some additional detail today on reader research conducted for the American Press Institute, this time an estimate of how much consumers might be willing to pay on average for online news. In short: Not as much as some proponents of pay walls assert. The estimates come from a sample of 450 readers in one purportedly "typical" newspaper market, so my previously expressed concerns about generalizability still stand. There's also the fact that it's challenging to measure what respondents might really be willing to pay for a good or service. All that said, my original underlying point remains: We have yet to see evidence supporting the assumptions in the JournalismOnline business model.
