FAQ
What is an MRP?
‘Multilevel Regression with Post-stratification’ (MRP) uses data from a voting intention poll to model how people will vote based on their demographics, voting behaviour and information about their constituency. These results are then applied to the demographic and electoral makeup of each constituency to make a constituency-level projection. The model is 'multilevel' because it uses both individual and constituency-level data.
How is this different from your normal voting intention poll?
The voting intention regularly published by More in Common is a national estimate based on a representative sample of at least 2,000 people. It indicates roughly how many people in Great Britain intend to vote for one party or another. This is simple to calculate and allows us to track changes through time.
But if you want to project a national seat count, this isn’t as useful. No political party performs equally well in every seat, because their supporters are not evenly spread across the country. For example, a 70-year-old man who didn’t go to university and lives in a small village has a higher likelihood of voting Conservative than a 25-year-old woman renting a flat in a major city. The benefit of MRP is the ability to use information about the different people who live in every constituency across the country to project how many people will vote for each party.
How does the model account for those who don't know how they will vote?
When we ask people their voting intention, some people say they don’t know. We push them to say who they would vote for if they were forced to choose, and we use this response as their expected vote. Some people, when asked to imagine that they were forced to choose, still don’t know who they would vote for. Using our MRP model, we’re able to make a better guess at how these “double don’t knows” might end up voting. When training the model to project people’s voting intention based on their demographics, voting behaviour and information about their constituency, we excluded the responses of people who didn’t know who they would vote for (after the squeeze) from the training data. When we apply the model to all the voters in the constituency, it effectively means we estimate the votes of people who don’t know, according to how people like them (in terms of demographics and past voting behaviour) but who do know, intend to vote. So if someone lives in a rural area, is over 75 and voted Conservative in 2019, the model uses the fact that most over 75s in rural areas who voted Conservative in 2019 and do know who they’ll vote for say they will vote Conservative, to guess that if they do vote it will likely be for the Conservatives.
How does the model account for who will vote?
When we survey the public about who they will vote for, many of the responses come from people who will not vote in the General Election. Since past election turnout is one of the strongest and most consistent predictors of future turnout, we factor in whether people voted in 2019 into our projections of who will turnout in this election rather than relying solely on self-reported voting likelihood.
How does your final model differ from your previous models?
Fieldwork for this model was conducted over a shorter period of time than previous models - from 24th June to 1st July.
Also, in previous models, we included information about voters’ second choice parties - recognising their voting dynamics might change before election day as they became more engaged about the realistic competitors in their individual seat. Now, at the end of the campaign, we are more confident these dynamics are already reflected in the responses the model uses without the need for additional information.
Finally, we have used oversampling in constituencies with particular dynamics to better inform our model. MRP models are good at indicating how the parties might perform across different constituencies based on their demographic makeup. But they don’t account for local factors that impact a small number of constituencies, such as a popular incumbent, well known or controversial council policy. In certain areas oversampling helps us to capture outperformance of independent candidates or smaller parties. That said, it isn’t feasible to oversample in every constituency - so it would be a mistake to draw too much from the projected vote share in an individual constituency.