Reader Survey Reminder

Legacy blog posts MP Housekeeping

It is hard for me to believe that I posted my first message on this blog barely six week ago. Yesterday, this site had over 120,000 visitors and had been averaging 10,000 to 20,000 for the previous week. I thank all of you for your interest and support. This has been an amazing experience.

I started this blog as an experiment. Now I have to think hard about its future. I have trouble thinking about anything without survey data, so I created a short reader survey. If you have not done so already, could you take 3-5 minutes and fill it out? It will not track respondent identities, and as such, your participation is completely anonymous and confidential.

As I said before, I will not try to use the survey to predict how many of you will return in the future. I have no illusions: Most of you have been obsessed with because of the election, and your interests will quickly move elsewhere until the next close election comes along.

However, I am very curious about those who have been reading this blog for the last few weeks, how you got here and what you think about it. I’d also want to use the survey to learn more about those who think they might want to return every now and then, even at times when an election is months or years away.

So, again, if you have found this site useful, please take a moment and complete the survey. If you have problems with it, please email me. Feel free to post comments or email them to me directly. I will leave the survey up for another 24 hours or so.

Mark Blumenthal

Mark Blumenthal is political pollster with deep and varied experience across survey research, campaigns, and media. The original "Mystery Pollster" and co-creator of Pollster.com, he explains complex concepts to a multitude of audiences and how data informs politics and decision-making. A researcher and consultant who crafts effective questions and identifies innovative solutions to deliver results. An award winning political journalist who brings insights and crafts compelling narratives from chaotic data.