Very happy: The folks at Sage have kindly signed a contract with Jocelyn Evans, Michael Lewis-Beck, and my own good self to edit a two-volume tome on Electoral Behaviour in their Handbook series. The final product will have some 50 chapters on all aspects of psephology and should come out in 2016. Cat-herding 50+ academics for the next 18 months. What’s not to like?

I’ve recently discovered Rfacebook, which lets you access public information on Facebook from R. In terms of convenience, no package for R or Python that I have seen so far comes near. Get yourself a long-lived token, store it as a variable, and put all posts on a fanpage you are interested in into one R object with a single function call. Check it out here.

An Imperial Duck. Because.

My wonderful PhD students are running a series of short online surveys, two of them with a slightly unusual and rather intriguing format. If you read German, do them a favour and click on the link. You should be done in 10 minutes or less. And while you’re about it, share the link with your networks to make this a bit less of a convenience sample.

When I drove home from work a couple of days ago, I noticed a policeman flagging down precisely every tenth car in the other lane and directing the drivers towards a lay-by. He was in uniform, wearing hi-vis gear and his government-issued Walther, so non-compliance was clearly not an issue. The scene was completed by a large billboard, stating that this was no ordinary vehicle spotcheck but rather a road use survey. I badly want these guys on our team.

Those old enough to remember that Bill Murray had a career before Lost in Translation (or to remember Bill Murray) will instantly recognise this scene: Punxsutawney Phil is predicting six more electoral cycles of political misery for Germany’s Liberal Democrats. Granted that the animal is a bit on the small side, but first, This is not America, and second, the choice of rodent is rather apt: Aren’t we all guinea pigs when it comes to policy making?

Punxsutawney Phil predicting six more cycles of electoral Misery

The hopeful candidate molesting the furry bugger promises  that he will listen, not ignore (whom?). He might change his mind once the beast sinks its front teeth into that yummy finger.

The Pirates are running a rather cheap electoral campaign: No faces (models or not) but only drawings in their trademark orange/blue tones. Their stinginess even extends to the meaning of their slogans. I was a bit thrown off by “Borders are so 80″, then discovered the small “er”, so borders are so 1980s, apparently. Well, yes, I get the implication for Europe. But why is there a “Herzschlag” (heartbeat? or heart attack???) between fear and courage, and why would that make me vote for the Pirates? I have a feeling that Literal Campaign Video Clips might become a thing very soon.

Pirates Posters: Say what?

The local Liberal Democrats never fail to amaze me. Just when I thought it could not get any better, I found another gem for my ever growing collection.

Local Campaigns: The Hour of Amateurs

“Höhenflug” is the act of (figuratively) ascending to some higher plane (not an imminent danger here) but losing touch in the process. “Bodenhaftung” is literally grip (get one, please!) or traction, so best illustrated by sitting on a tractor. What better way to show that you are down to earth (pun intended) and in no way out of touch than riding this nifty little machine in your best dark suit, as any local farmer would? Bonus points for gratuitous use of “frischer Wind” (a breath of fresh air), quite possibly the most overused phrase in German politics and code for not being incumbent.

## The Problem: Assessing Bias without the Data Set

While the interwebs are awash with headline findings from countless surveys, commercial companies (and even some academics) are reluctant to make their raw data available for secondary analysis. But fear not: Quite often, media outlets and aggregator sites publish survey margins, and that is all the information you need. It’s as easy as $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$.

## The Solution: surveybiasi

After installing our surveybias add-on for Stata, you will have access to surveybiasi. surveybiasi is an “immediate command” (Stata parlance) that compares the distribution of a categorical variable in a survey to its true distribution in the population. Both distributions need to be specified via the popvalues() and samplevalues() options, respectively. The elements of these two lists may be specified in terms of counts, of percentages, or of relative frequencies, as the list is internally rescaled so that its elements sum up to unity. surveybiasi will happily report k $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$s, $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$ and $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$ (check out our paper for more information on these multinomial measures of bias) for variables with 2 to 12 discrete categories.

## Bias in a 2012 CBS/NYT Poll

A week before the 2012 election for the US House of Representatives, 563 likely voters were polled for CBS/The New York Times. 46 per cent said they would vote for the Republican candidate in their district, 48 per cent said they would vote for the Democratic candidate. Three per cent said it would depend, and another two per cent said they were unsure, or refused to answer the question. In the example these five per cent are treated as “other”. Due to rounding error, the numbers do not exactly add up to 100, but surveybiasi takes care of the necessary rescaling.

In the actual election, the Republicans won 47.6 and the Democrats 48.8 per cent of the popular vote, with the rest going to third-party candidates. To see if these differences are significant, run surveybiasi like this:


. surveybiasi , popvalues(47.6 48.8 3.6) samplevalues(46 48 5) n(563)
------------------------------------------------------------------------------
catvar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
A'           |
1 |  -.0426919   .0844929    -0.51   0.613     -.208295    .1229111
2 |  -.0123999   .0843284    -0.15   0.883    -.1776805    .1528807
3 |   .3375101   .1938645     1.74   0.082    -.0424573    .7174776
-------------+----------------------------------------------------------------
B            |
B |   .1308673   .0768722     1.70   0.089    -.0197994    .2815341
B_w |   .0385229   .0247117     1.56   0.119    -.0099112    .0869569
------------------------------------------------------------------------------

Ho: no bias
Degrees of freedom: 2
Chi-square (Pearson) = 3.0945337
Pr (Pearson) = .21282887
Chi-square (LR) = 2.7789278
Pr (LR) = .24920887




Given the small sample size and the close match between survey and electoral counts, it is not surprising that there is no evidence for statistically or substantively significant bias in this poll.

An alternative approach is to follow Martin, Traugott and Kennedy (2005) and ignore third-party voters, undecided respondents, and refusals. This requires minimal adjustments: $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$ is now 535 as the analytical sample size is reduced by five per cent, while the figures representing the “other” category can simply be dropped. Again, surveybiasiinternally rescales the values accordingly:


. surveybiasi , popvalues(47.6 48.8) samplevalues(46 48) n(535)
------------------------------------------------------------------------------
catvar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
A'           |
1 |  -.0162297   .0864858    -0.19   0.851    -.1857388    .1532794
2 |   .0162297   .0864858     0.19   0.851    -.1532794    .1857388
-------------+----------------------------------------------------------------
B            |
B |   .0162297   .0864858     0.19   0.851    -.1532794    .1857388
B_w |   .0162297   .0864858     0.19   0.851    -.1532794    .1857388
------------------------------------------------------------------------------

Ho: no bias
Degrees of freedom: 1
Chi-square (Pearson) = .03521623
Pr (Pearson) = .85114329
Chi-square (LR) = .03521898
Pr (LR) = .85113753



Under this two-party scenario, $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$ is identical to Martin, Traugott, and Kennedy’s original $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$ (and all other estimates are identical to $How to Measure Survey Bias without Having Access to the Raw Data (Surveybias Example 2/3)$‘s absolute value). Its negative sign points to the (tiny) anti-Republican bias in this poll, which is of course even less significant than in the previous example.