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Tom Johnson

The Demographics of Mobile News | Project for Excellence in Journalism (PEJ) - 0 views

www.journalism.org/...demographics_mobile_news

mobile demographic KSFR mobile news radio

shared by Tom Johnson on 11 Dec 12 - No Cached
  • Tom Johnson on 11 Dec 12
     
    The Demographics of Mobile News December 11, 2012 Men, College Grads and the Young are More Engaged In the growing realm of mobile news, men and the more highly educated emerge as more engaged news consumers, according to a new study by the Pew Research Center's Project for Excellence in Journalism, in collaboration with The Economist Group. These findings parallel, for the most part, demographic patterns of general news consumption. But there are some important areas of difference between mobile and general news habits-particularly among young people. While they are much lighter news consumers generally and have largely abandoned the print news product, young people get news on mobile devices to similar degrees as older users. And, when getting news through apps, young people say they prefer a print-like experience over one with high-tech or multi-media features. These are key findings of an analysis of mobile news habits across a variety of demographic groups. This report builds off an earlier PEJ and The Economist Group report, The Future of Mobile News, which found that half of U.S. adults now own mobile devices and a majority use them for news. Both reports are based on a survey of 9,513 U.S. adults conducted from June-August 2012 (including 4,638 mobile device owners). Men, especially young men, are heavier mobile news consumers than women. More than 40% of men get news daily on either their smartphone and/or tablet, compared with roughly 30% of women. On the tablet specifically, men check in for news more frequently and are more apt to read in-depth news articles and to watch news videos. Women, on the other hand, are more likely than men to use social networks as a way to get news.
Tom Johnson

Spinitron - Spinitron Home - 0 views

www.spinitron.com

KSFR playlist automate DJ how to

shared by Tom Johnson on 27 Jun 13 - No Cached
  • Tom Johnson on 27 Jun 13
     
    We are always looking for ways to help our radio stations operate more effectively and efficiently. We have recently come across a great tool to help DJs with a lot of their day to day efforts, Spinitron. Eva and Tom have put together a great solution. Let me tell you a bit more about what it does. Spinitron is an online playlist management solution for non-commercial educational and community radio stations. It is all web based; there is no software to install. It provides sophisticated features for playlist data entry (manual & automated) and dynamic online publishing. It solves your data storage, archiving and security issues. You can integrate station schedule, current and historical playlists, as well as other custom elements, into your web site. You can put "Now Playing" on your home page, webcast stream, mobile apps, RDS and others. With an advanced database design, Spinitron offers powerful data manipulation and analysis features. Compile Top-30 charts (or however many you like) with filtering by genre, time period, etc. Generate unlimited reports for CMJ, ASCAP, BMI, SESAC and SoundExchange. Track the popularity of artists, disks and songs. Query how often certain disks get played. Produce reports of what DJs at the station are playing to give to record companies and promotional agencies. Stay connected with your listeners. Watch for other interesting developments as we start working together with them on a few projects. Discover Spinitron with a 30-day no-obligation free trial. Contact: Eva Papp at eva@spinitron.com or 617-233-3115 to learn more. Tags: college radio, community radio, High School Radio, Playlist Management, Spinitron
Tom Johnson

Localism emphasis poses risk | Current.org - 0 views

www.current.org/...localism-emphasis-poses-risk

news public broadcasting public attitudes toward ksfr

shared by Tom Johnson on 17 Sep 13 - No Cached
  • Tom Johnson on 17 Sep 13
     
    "Localism emphasis poses risk But stations see value in filling news vacuum Published on Current.org, September 17, 2013 Analysis by Mark Fuerst Print This Page Of all the complex and potentially fateful decisions faced by public radio program directors as they navigate the emergence of multiplatform distribution, one of the most significant is the drive to "go local" and produce more local programs, especially news and information. This push signals a strategic shift for public radio, with potentially enormous consequences for growth or decline. Audience 2010, one of a series of landmark research reports on programming trends published in the previous decade, reported that much of the credit for the growth of public radio listenership could be traced to a shift "away from local production toward network production, away from music-based content toward news, information and entertainment." That shift was extraordinarily successful, representing two decades of impressive audience expansion and financial growth at a time when other parts of the radio industry struggled. Now, it appears that program decision-makers are changing course. But why would dozens of stations move off the path that worked so well and choose another approach that, viewed through the lens of audience research, would seem to be both more costly and less powerful in attracting listenership?"
Tom Johnson

A New Formula for Quantitative UX Decision Making | UX Magazine - 0 views

uxmag.com/...uantitative-ux-decision-making

formula decision making quantitative statistical analysis small sample

shared by Tom Johnson on 08 Aug 12 - No Cached
  • A New Formula for Quantitative UX Decision Making by Jeff Sauro, Jim Lewis Comments (0) Share on deliciousShare on diggShare on emailShare on hackernewsShare on redditShare on facebook_like32 Imagine a formula that would allow you to take data from a very small pool of users (often as few as 8; possibly as few as 3) and figure out why, for instance, Autodesk customers are calling support, whether Budget.com visitors can rent a car in under a minute, or why cardholders were reluctant to use a mobile payment site. Such a formula exists, and it’s not some abstract “formula for success” in management strategy or a design technique. We’re talking about a mathematical formula that’s easy to use but can transform the way you measure and manage the user experience. The formula is called the Adjusted-Wald Binomial Confidence Interval (“Adjusted-Wald Interval” for short), but its name isn’t as important as what it can do. Its power is in helping estimate the behavior of an entire user population, even when the sample size is small. It does this by taking a simple proportion as input and producing a confidence interval. For example, suppose 10 users have attempted a task and 7 completed it successfully. The simple successful completion rate is 70%. But, given such a small sample size, how can you have any faith in the result? Would it be reasonable to expect to get exactly 7,000 succe
  • Tom Johnson on 08 Aug 12
     
    A New Formula for Quantitative UX Decision Making by Jeff Sauro, Jim Lewis Comments (0) Share on deliciousShare on diggShare on emailShare on hackernewsShare on redditShare on facebook_like 32 Imagine a formula that would allow you to take data from a very small pool of users (often as few as 8; possibly as few as 3) and figure out why, for instance, Autodesk customers are calling support, whether Budget.com visitors can rent a car in under a minute, or why cardholders were reluctant to use a mobile payment site. Such a formula exists, and it's not some abstract "formula for success" in management strategy or a design technique. We're talking about a mathematical formula that's easy to use but can transform the way you measure and manage the user experience. The formula is called the Adjusted-Wald Binomial Confidence Interval ("Adjusted-Wald Interval" for short), but its name isn't as important as what it can do. Its power is in helping estimate the behavior of an entire user population, even when the sample size is small. It does this by taking a simple proportion as input and producing a confidence interval. For example, suppose 10 users have attempted a task and 7 completed it successfully. The simple successful completion rate is 70%. But, given such a small sample size, how can you have any faith in the result? Would it be reasonable to expect to get exactly 7,000 successes if the sample size was 10,000? Probably not, but how far off might it be?
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