When I submitted my evidence to the Commission on Racial and Ethnic Disparities (CRED) in November 2020, I recommended that ethnicity pay gap reporting (EPGR) using the Big 5 ethnicities of White, Asian, Black, Mixed & Other be made mandatory. In the CRED report published in March 2021, recommendation 24 called for analysis of ethnicity to use the Office of National Statistics (ONS) 18+1 ethnicities as much as possible and it is my opinion this is one of the reasons why CRED stopped short of recommending mandatory EPGR in recommendation 9. In June 2021, I published 7 + 5 recommendations for improving gender pay gap reporting (GPGR) and I made it clear that all 12 of my recommendations would also apply to any EPGR system introduced by the government. However, I stated my 2nd recommendation on number of categories to be reported needed more details before it could be applied to EPGR and this article will fill in those details whilst also recording my response to recommendations 9, 10, 23 & 24 of the CRED report.
The core expertise that Statisticians offer to the world is drawing conclusions from small samples. Therefore, knowing how to design surveys, estimate the right sample size, decide on the right way to ask the question or measure a property are all essential skills for any statistical thinker. The skills you need to be competent in Sampling & Surveys are best captured by my Survey Wheel.
Theresa May has just asked the EU Council for a 3 month extension to Article 50, the Speaker won’t allow another meaningful vote without meaningful changes, Jeremy Corbyn is pondering backing another referendum and in 9 days time, the UK could be leaving the EU without a deal. Just another week in the Parliamentary Brexit Maze but I have updated my Brexit Voting Factions after last week’s votes and identified an 8th faction for you to play with in your voting permutations.
[Read more…] about EU Referendum #6 – Find your way out of the Brexit maze in 9 Days!
*** This post is not yet complete. However you will find a link to the data near the bottom and a link to a twitter thread for some of the images ***
Within the next 10 days, the House of Commons will get a second Meaningful Vote on the Withdrawal Agreement which could be followed by 2 more significant votes on No-Deal and Article 50 Extension. I have been tracking how MPs have voted on the first Meaningful Vote and subsequent Amendments which I summarised in two posts “Find your way out of the Brexit Maze in 57 days and 43 days.” Following further amendments at the end of February and with no more amendments planned before the next meaningful vote, I have redone my cluster analysis to predict what the outcome of these votes might be. As far as possible, I am trying to base my predictions on what MPs have done rather than what they say but I will compare my analysis with that of Election Maps who have been tracking MP’s statements.
[Read more…] about EU Referendum #5 – Find your way out of the Brexit maze in 16 Days!
Rather than celebrating love on Valentine’s day, Parliament chose to use the occasion to emphasise their discord over the EU withdrawal process, 43 days before the UK is due to leave the EU. Three amendments were voted on and this allows me to update my Brexit voting blocks which I first described in “Find your way out of the Brexit maze in 57 days!”.
January 2019 has been a month of considerable parliamentary drama in the UK as MPs wrestle over whether to approve the Withdrawal Agreement between the UK and the EU. There is no shortage of political punditry and quotes from politicians and the whole episode is proving to be a classic example of uncertainty. For statisticians like myself, uncertainty occurs when you cannot properly price the odds of an event happening unlike risk which occurs when you can price the odds. Since the current state of affairs will ultimately be determined by parliamentary votes one way or the other, is it possible to use parliamentary vote data so far to estimate the odds of certain scenarios?
If I were to remark to you that “the weather is very nice today” or “I didn’t like that person”, it is unlikely that I would have made such statements based on a single variable. It is more likely that a combination of variables were evaluated to arrive at these statements. When we analysis datasets with multiple variables, we are undertaking Multivariate Statistical Analysis.
Multivariate Analysis comes in two flavours :-
- Analysis of Correlations between Multiple Variables – Known as R-Analysis – Informally known as reducing the dimensionality of your dataset.
- Analysis of Distance between Many Objects – Known as Q-Analysis – Informally known as mapping, clustering or segmentation of your dataset.
My wife is American and so it should be easy to guess what we were talking about on the morning of 9th November 2016. Donald Trump’s victory in the US Presidential election was a surprise to many people and prompted much discussion on the similarities between Trump voters and the Leave voters in June. However, my wife remarked that people may be looking at this the wrong way round and perhaps the correct question to ask is whether there is greater similarity between Clinton & Remain voters.
Identifying similarities and differences between groups of people is a cornerstone of the field of market research known as customer segmentation. It is one of my favourite areas of statistics and can be used regardless of whether the data comes from a survey or from customer records. When my wife posed her question I immediately thought of 2 ways I could answer this using segmentation methods.
- Look at how people feel (their sentiments) which is what this post is about.
- Look at how people voted (their behaviour) which I will cover in another post “Who has more in common? Leave & Trump voters or Remain & Clinton voters? Analysis of voting behaviour”