Polling

2016 California Republican Presidential Primary Poll

From March 2-5 and 7-9 SmithJohnson Research conducted two California statewide surveys. The target population was registered “2016 likely voters”. Registered Republicans were asked who they supported in the presidential primary. In interviews conducted March 7-9 we also included a question rating Donald Trump’s favorability among voters from all parties.

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Robo-Polls: Wave Of The Future?

They’re fast, they’re cheap, and you can easily get larger sample sizes, but are they worth it? There is controversy over the accuracy of automated polls, also known as Interactive Voice Response (IVR) polls. Traditional pollsters are still skeptical, while IVR proponents claim they’re the wave of the future. More unsettling, however, is new research that suggests some IVR polls may have been biased to conform to traditional polls.

Whether or not those concerns are accurate, there are certainly advantages that come with IVR polls. In addition to being cost-effective, they ensure that questions are asked the same way every time. You also don’t have to worry about issues with accents from the interviewers. And since there isn’t a real person talking, the respondents may feel less pressure and express their honest opinions.

A 2009 Pew Research Center study found that telephone polls “did very well in forecasting the outcome of the election in 2008.” The American Association for Public Opinion Research produced a paper in 2009 on presidential primary polling which concluded that the use of IVR polls “made no difference to the accuracy of estimates.”

Of course, automated polls aren’t without their own drawbacks. The main problem is that auto-dialed calls have a bad reputation, largely due to the annoying commercial calls that people get in the evening. Respondents may hang up before even listening to the purpose of the call. Questions have to be short and you can only ask so many questions to retain respondent interest.

Perhaps, a greater cause for concern was raised by Dr. Joshua Clinton and Steve Rodgers, political scientists at Vanderbilt and Princeton, respectively, who published a paper in 2013 which suggested that IVR polls from the 2012 GOP primary […]

Data Mining and The Princess Bride

Political consultants and the campaigns they work for are poised to spend millions in the coming cycle on data mining. For some, that’s going to be a great decision.  For others – unless they take the time to understand what data mining is and what it isn’t – they just may wake up with a bad case of buyer’s remorse.

Data mining has its roots in the pragmatic-oriented fields of business and computer science. Its main contribution has been the capability of using ever-expanding computing power to process large volumes of data and find important patterns and anomalies. Financial institutions have been able to improve their ability to detect fraud in credit card transactions using these techniques.

The idea is that with enough computing power, data, and variables thrown into a regression, eventually something “interesting” will emerge. This serendipitous approach is why many statisticians dismissed data mining as a forecasting tool early on, warning like those mutual fund notices that “past results are not necessarily an indication of future performance.”

The problem, of course, is that political strategists are far less interested in a history lesson than they are in a crystal ball that will predict the future.  As much as we may wish for data mining to be that crystal ball, that’s simply not what this tool is all about.

Statisticians are concerned with making inferences (generalizations) that can be used for making predictions. Professor David J. Hand, in his 2008 article for the International Journal of Forecasting, writes, “Forecasting is fundamentally an inferential problem. That is, it is not simply a question of summarizing data, but is rather a question of generalizing from the available data to new data — and in particular to new situations […]