Magnifying glass over the word analyse to illustrate analysis of TR PbR Figures

TR PbR Figures – Context and Drivers

This blog post summaries a presentation I gave at our recent event: Transforming Rehabilitation: Learning from the PbR results.

 

Overall results  

In this blog, I will discuss the data I presented at our recent event, ‘Transforming Rehabilitation: Learning from the PbR Results’. The event was held shortly after the publication of the first reoffending figures which showed that reoffending in the first cohort had been reduced by 1.9%. My colleague Jack Cattell has already discussed these figures in an excellent blog, so this blog is about the how external drivers across the criminal justice system might be affecting these results.  

By CRC? – PbR Figures, who is in control of reoffending? 

In order to make conclusions from these results, one must be aware that there are many factors that can influence reoffending. When faced with (improved or reduced) performance results – it’s too easy to fall into the trap of thinking that the reasons for this were outside of your control, or that the results were dominated by one particular driver.  

In understanding the reoffending rates by Community Rehabilitation Companies (CRCs), there are many possible drivers across the CJS, and they can be complex to understand. So, could the CRCs have achieved the difference in performance we have seen under TR? Internal factors such as CRC procedures and workflows, and offender profiles will have an effect.  But so too will a myriad of external influences from other agencies of the CJS, for example police positive outcome rates, court conviction rates and timeliness of police & court procedures. Since these factors are outside a CRC’s control, I wanted to investigate and determine whether any might have had an effecting change in the reported reoffending rates.  

National context 

Chart showing - No. of offences thousands

First off, it is important to bear in mind the national context for the period from baseline to first cohort results, which was a time when the total estimated crime fell and police recorded crime remained constant.

 Chart showing proportional change from 2011

Further, positive outcome rates, specifically charges and cautions had been reduced – in general police arrest rates were down and police had been using other disposals (such as community resolutions) to reduce the number of people – particularly young people – in custody.

 

Chart showing days from offence to completion

And individuals were being processed more quickly during the baseline: on average court processes are now taking 10-15 days longer.

Give this context, the national reoffending rate was reduced by 1.8%. That’s a surprisingly resistant figure, given the changes in context nationally. Large changes in this binary rate will not be seen regardless of policy or systematic change in CRCs.

Chart showing reoffending rate

(Of course, the full picture of the reoffending stats has the rate down, but the frequency up – suggesting a shrinking group of more prolific reoffenders. But that is for another post).

Differences in CRCs?

I will now look at these potential drivers of reoffending rates at the CRC level and determine if contexts really were different or helped to drive different performance. To highlight any of these potential differences, I will look at two CRCs who were at opposite ends of the reoffending rate changes.

What is surprising, is that there are no large, obvious differences between those two CRCs when comparing police outcome rates, court effectiveness and court timeliness measures. For example, the police positive outcome rates were reasonably different at the start of the period, but by the end of 2015 had converged to be broadly similar – the best performing CRC had reduced reoffending while police positive outcomes fell by a significant amount.

Chart showing positive outcome rate

Similarly, the lowest performing CRC had raised rates against the baseline despite court timeliness both increasing, and being higher than other CRCs.

So What?

We’ve seen that reoffending rates are complex measures dependent on many factors, from individual, to regional to national level, and the interaction between them. The rates don’t change very much, even though the contexts can differ wildly.

Even comparing the CRCs at either end of performance spectrum there were not huge differences in the police and court factors. This can be seen in different results for CRCs operating in widely similar contexts.

So, my advice is this: don’t fall into the trap of feeling the results are not within your ability to affect. Understanding the wider picture is helpful to contextualise local results. In the following blogs, my colleagues will be focusing on what is in your control, and my colleagues will be identifying what works and using that to inform good practice.

Get involved in the conversation by joining our LinkedIn group.

Julia Douglas-Mann, GtD's new team member

Hello to Our New Team Member – Julia Douglas-Mann

We’re very pleased to announce the appointment of a new researcher, Julia Douglas-Mann, to the team.

Julia, who joined GtD last month, has an MSc in Psychological Research Methods from the University of Exeter. She is working on questionnaire design, qualitative interviewing, project management, and statistical analysis. Julia has been involved with, and run, several research projects in psychology and mental health, especially within the areas of morality and decision making, empathy, self-compassion, depression, and post-traumatic stress disorder.

 

Julia Douglas Mann
Jack Cattell, said: “We’re thrilled that Julia has joined the team, she is a very talented and experienced researcher. Her appointment further strengthens our service offering and she’ll be working at the forefront of our social impact analytics and managing our project delivery.”
 
Julia, said: “I’m delighted to be working for GtD. The company is very innovative and forward-thinking and it’s great to be a part of the team. I’m really looking forward to advancing and evolving the social impact analytics tools and systems, and it’s very rewarding knowing that in doing so, I’m ultimately helping other organisations make things better for people in society.”

 

TR: PbR results - speakers who presented to Community Rehabilitation Companies and other justice sector employees

Community Rehabilitation Companies: PbR Results Event

Transforming Rehabilitation is the UK government’s programme of outsourcing probation services to new community rehabilitation companies. In a radical move, the government is now paying these new companies by the reduction in reoffending results they achieve. GtD is at the forefront of this by providing our cutting-edge social impact analytics to Sodexo Justice Services who manage a number of these new companies.

The first PbR figures were published last month and GtD has been active in informing the debate on their significance. As part of this debate, we recently hosted a sell-out event for senior management and practitioners working in community rehabilitation companies and the justice sector.

An expert panel comprising Prof. Darrick Jolliffe of Greenwich University (above left), Dr Sam King of Leicester University (above right) and GtD’s own Jay Hughes (above centre left), considered the initial findings and what to do next, with Jack Cattell (above centre right) setting out a new vision of how predictive analyses can be used by practitioners to improve performance.

Prof. Darrick Jolliffe – University of Greenwich

If you were unable to attend but would like to learn more about how GtD could support you in evaluating your social impact outcomes or for a free predictive analytic roadmap for your CRC, contact Jack Cattell  The event presentations can also be viewed via the link below:

Transforming Rehabilitation – Learning from the PbR results presentations

Dr Sam King – University of Leicester

 

Jack Cattell – GtD

 

We’ve also set up a LinkedIn group as a forum for shared learning and discussion, for individuals who work or have an interest in, the fields of probation, offender rehabilitation and Transforming Rehabilitation. Click here to request to join – Transforming Rehabilitation

Image to illustrate no data can mean no voice and no idea

No Data? No Voice, No Idea – The Importance of Data

The collection and analysis of data must never be allowed to fall by the wayside – it’s a founding stone, not a ‘nice to have’.

Of course, I would say that, wouldn’t I? But here are six concrete reasons why data is important, drawing on Get the Data’s recent work with the Advice Services Alliance (ASA) as a case study.

  1. Data Is the Great Persuader

There is no more powerful tool for influencing stakeholders than data, as I explained in more detail in this recent blog post on using data to influence stakeholders.

ASA works with its members – various associations who provide advice services – to capture and analyse data from the front-line. This gives weight to conversations with all of those who have an interest in ASA’s direction of travel, reassuring them that strategic decisions are being made in response to changing needs, and reinforcing the professionalism that underlies the work they and their partners do.

 

  1. Data Means Funding

In particular, data is invaluable in drawing in new sources of funding, and persuading potential funding providers. Faced with a choice of projects or programmes in which they might invest, with ever-tighter budgets, funding bodies will regard convincing data as a good reason to choose your work over others. ASA will use data to add weight to its funding applications for this reason.

 

  1. Data Gives You the Power to Lead the Debate

ASA seeks to lead thought, representing its members in national discussions by highlighting issues affecting the people who use advice services. The body of data and analysis to which ASA will refer confers authority and allows the organisation to direct the debate and steer collective thinking around youth issues.

 

  1. Data Defines Good (and Bad) Practice

Using data provided by its members ASA will be able to identify areas for improvement in front line practice and also to pinpoint what is working especially well so that good practice can be shared across the community. It informs training programmes, service improvements and helps determine how resources should be employed for maximum impact.

 

  1. Better data and measurement development 

Good data leads to better data. Use of data means we learn its limitations and how to outcome those. We also learn how to measure the right outcomes, better; particularly in how to measure informal outcomes along said formal attainment.

 

  1. Data Is Cheaper and Easier Than Ever

Cloud-based databases are cheap and easy to implement compared to the cumbersome systems of the past. They make it easier for people to enter data and share it. So there’s really no excuse for failing to collect and analyse data in this day and age.

If you would like to find out more about our cutting-edge approach to data capture and analysis please get in touch.

Scales of Justice representing Transforming Rehabilitation

Transforming Rehabilitation: Payment by Results Figures

Last week saw the release of the Transforming Rehabilitation (TR) Payment by Results figures for the October to December 2015 cohort.

The overall result was encouraging, and defy the view that Transforming Rehabilitation’s radical changes to probation, and the ensuing problems, would result in increased reoffending – though it is very important that I point out that this is just the first set of results of many and overall judgement should be reserved for at least a year. The reoffending rate for all CRCs was 45.6% compared to a 2011 baseline rate of 47.5%. I had to make some (conservative) assumptions to estimate the baseline rate but I think it is also safe to say that the difference was statistically significant, suggesting reoffending rates have reduced under TR. Please see the the note at the end of this blog to understand better how I completed the analysis.

 

Transforming Rehabilitation – CRC Performance

The chart below describes each CRC’s reoffending rate in relation to the baseline 2011 rate. The grey line represents the range of reoffending rates that would indicate no change from 2011 (the baseline confidence interval). If the CRC’s rate is outside this range, we are confident in statistically terms to state that the CRC’s performance was either better or worse than the reoffending rate achieved in 2011. The green bars represent the reoffending rates of CRCs that outperformed 2011, the orange bars represent those that performed the same as 2011 and the red bars present those that performed worse than 2011.

Chart to show CRC Performance

Source: Ministry of Justice Final Proven Reoffending Rates TR (Oct to Dec 2015 cohort).

Thirteen of the CRCs beat the baseline rate. The best performing CRC was Cumbria and Lancashire, which beat the baseline rate by 8.2% (49.9% to 41.7%). The nest best was Hampshire and the Isle of Wight which beat the baseline by 5.4% and the third best was Northumbria with a better rate by 4.3%. Two of the CRCs performed worse than the 2011 baseline. Warwickshire and West Mercia recorded a reoffending rate 3% worse than the baseline rate, and South Yorkshire’s rate was 2.8% worse. With most CRCs, however, outperforming the reoffending rate form 2011, the figures are a promising set of results.

 

Transforming Rehabilitation – Comparing CRC performance

Now that the baseline rates have been published, we can better understand how well each area was performing in 2011 and whether a CRC is now being asked to better good or bad performance achieved in that year. The chart below describes the difference between the actual baseline rate and the 2011 baseline’s OGRS score (in other words their expected rate of reoffending). A negative result in the chart means the area performed better in 2011 than the OGRS score expected.

Chart showing the difference between a CRC's actual baseline rate and the 2011 baseline’s OGRS score

Source: Ministry of Justice Final Proven Reoffending Rates TR (Oct to Dec 2015 cohort).

The charts highlights that six of the CRCs are being asked to beat better than expected performance in 2011 (in other words to be better than good). Whereas other CRCs, notably London and Wales, are being asked to outperform potentially poor performance in 2011. It it interesting that South Yorkshire and, Warwickshire & West Mercia – the two areas that recorded poor performance for TR – are being asked to beat good performance from 2011. Merseyside and Cheshire & Greater Manchester, however, are equally being asked to beat good performance from 2011 and were able to do so for the October to December 2015 cohort. The OGRS score does not allow for area effects, which will exist and could explain the differences between the OGRS score and the baseline rate. It not possible now to conclude whether payment by results will be easier in some areas than others, but, going forward, I will monitor the impact of whether a CRC is being asked to perform better than good or poor performance from 2011 on their ability to achieve payment by results bonuses.

 

Notes on analysis

The latest Ministry of Justice bulletin released more data than was previously available and I was able to complete a statistical analysis of the impact of TR. This could only be completed with making conservative assumptions that would make finding a statistically significant result less likely. The following actions were taken:

  • I assumed the spread of offenders across CRCs in 2011 was exactly the same as it was in the October to December 2015 cohort. This would not be the case but any analysis would want to weight the two samples so they represented each other so the impact of this assumption is minimal.
  • The 2011 sample size was assumed to be the same as that of the October to December 2015 cohort. The 2011 sample will be considerably bigger, so this assumption meant the standard error used for the analysis was larger than it should be.
  • A t-test with unequal variances assumed was used to test the difference between the cohort’s and the baseline’s reoffending rate. The t statistic result was 4.6.

Blog originally guest posted on http://www.russellwebster.com 31st October 2017 with additional commentary from Russell Webster.

GtD Transforming Rehabilitation Event

Transforming Rehabilitation: Learning from the PbR results

Transforming Rehabilitation challenged Community Rehabilitation Companies (CRCs) to reduce reoffending significantly and October will see the publication of the reoffending rates of the first Transforming Rehabilitation cohort.

These results are an important test of the government’s Transforming Rehabilitation agenda and the success of the new CRCs. Some CRCs will be happy with their results, while others will need to improve. The published results also provide opportunities to commission new services for offenders.

Besides the implications for policy and practice, the results also present an opportunity for your organisation to learn how best to reduce reoffending with the tools at your disposal. You might want to increase a CRC’s performance, identify “what works?” and roll that out across all your CRCs, or prove that your specific service will make the difference.

Come join our experts at our TR PbR event, to discuss what needs to be learnt, what is within your control, and how embedding impact analytics in your CRC or service delivery organisation will help you to reduce reoffending.

Transforming Rehabilitation: Learning from the PbR results

Tuesday 28th November

2 – 4.30 pm

The Space Centre

94 Judd St, Kings Cross, London WC1H 9NT

SPEAKERS:

Professor Darrick Joliffe, University of Greenwich

“How to measure the quality of relationships between offenders and probation officers.”

Darrick is Professor of Criminology at the University of Greenwich. Darrick is interested in the broad areas of developmental life-course criminology, programme evaluation, prison research and psychology, individual differences and offending. He has developed tools to measure the satisfaction of offenders and the quality of their relationships with probation officers, and has estimated the association between relationship quality and reoffending. Darrick will also chair a discussion after the presentations.

 

Dr Sam King, University of Leicester

“What is high quality offender management?”

Sam is an expert on offender management and rehabilitation, and has contributed significantly to developments in desistence theory. His talk will discuss the latest evidence on what makes high quality offender management and what can be implemented to reduce reoffending. Sam has published widely on probation work and desistence theory, and has helped CRCs to implement innovative offender management tools to measure offender motivation.

 

Jack Cattell, Get the Data

“Predict your PbR results and continuously learn how to improve them before they happen.”

Jack is an expert on the prediction of reoffending rates, and the analysis of how a probation service can audit and improve its reoffending rate. He has worked for the Ministry of Justice, former probation trusts and Sodexo Justice Service’s six CRCs on research and analysis projects. He heads up GtD’s thought leadership on the use of predictive analysis to improve adult and youth offender management.

Free event – register today! Places are limited.

 

Headed by Jack Cattell, Get the Data is located in the UK and USA. We are an international thought leader on how to reduce reoffending through high quality analyses of offender management data. We have delivered high-profile projects for the Ministry of Justice, HMPPS (formally NOMS), and the police. We are proud to provide our Social Impact Analytics service to Sodexo Justice Service’s six CRCs.

 

Databse word cloud

Choosing a Database? Just Keep It Simple

When choosing a database for your project or programme, made to measure software or flashy online tools aren’t necessarily the right choice.

If you’re going to gather and analyse data in a serious way of course you need a database of some sort and, having spent much of my academic and professional career buried in them, I’m very much an advocate. But I too often see evidence of the database itself being regarded as the end rather than the means – as the solution to the challenge at hand rather than a tool for addressing it.

Perhaps that’s because we all so often feel under pressure, either external or self-imposed, to demonstrate what we have delivered in concrete terms. A specially commissioned database, perhaps with a catchy name and fancy interface, is something a project manager can point to and say, ‘I made this.’

The problem is that it takes huge amounts of time, resources and testing to create excellent software from scratch or to implement a powerful online tool, meaning that in practice these products all too often become clunky, creaky and frustrating. And products that don’t work well don’t get used.

As always, the answer is to focus on what you want to achieve. That will help you understand what kind of data you need to collect, who will be collecting and managing it and, therefore, what kind of system you need to house it.

Sometimes, of course, a custom-designed database or powerful online application are absolutely the right choice but, in my view, those occasions are actually very rare. More often than not the humble, rather plain Microsoft Access, or a similar tried and tested generalist professional product, is not only cheaper but also better suited to the task. Remember, these programmes have been worked on over the course of not only years but decades, and have huge amounts of resources behind their development and customer support programmes. Standard software also makes sharing, archiving and moving between systems faster and easier in most cases.

It is also possible to customise Access databases to a fairly high degree, either in-house using the software’s in-built features, using third-party software, or by hiring developers to create a bespoke front end interface without getting bogged down in the complicated underlying machinery.

And cloud data storage has made all this easier and cheaper than ever.

In conclusion, I’d like people to recognise that the real deliverable isn’t necessarily software, and that there’s no shame in off-the-peg. After all, a database is only as good as the information it holds and a clean set of useful, appropriate data is what people should really be proud of.

We’re hiring! Quantitative Researcher / Analyst

Vacancy – Quantitative Researcher / Analyst Senior Quantitative Researcher / Analyst

Get the Data (GtD) is a successful and growing company, both in London and in Atlanta USA. GtD’s exciting social impact analysis approach is helping organisations on both sides of the Atlantic to measure, learn and prove their social impact.

At GtD we offer our employees an opportunity to gain extensive experience within all aspects of social impact analytics (SIAs) whilst working alongside leading social impact analysts and thought leaders in the industry.

Our service offering is unique in the industry. This means you will be gaining invaluable insights into, and experience of, SIAs. We are innovative and forward thinking so you will be helping to advance and evolve our social impact analytics tools and systems knowing that whilst you are doing so, you are ultimately helping other organisations improve the impact they have on society.

We offer our employees a supportive working environment, with training provided, to help you develop and enhance your skills.

We are looking for both newly qualified and more experienced quantitative researchers and analysts. You could be looking for your first role or want to apply your analytical skills to something more rewarding. But you must be passionate about how high quality, well communicated analysis can improve social outcomes. You will be looking forward to influencing senior people in the police, courts, probation services and third sector organisations on both sides of the Atlantic.

In other words, we are most interested in your potential.

Therefore you will be a self-starter, relish the opportunity to help a business grow and want to learn and apply advanced analytical and statistical techniques. You will provide quantitative analysis skills to our evaluation and social impact analysis practice in the areas of criminal justice, education and housing. Specifically you will support an evaluation of an intervention to reduce offending by high harm offenders, a project to predict reoffending rates and an evaluation of a young person’s training initiative. You will also work on police projects with our sister company Crest Analytics.

Quantitative researcher / analyst

You can demonstrate these essential skills:

  • Graduate degree in a subject with a substantial mathematical or statistics component
  • Report writing for non-technical audiences
  • Experience of using Microsoft Excel skills
  • Experience using SPSS syntax or R
  • Experience of applying statistical modelling techniques including regression analysis
  • Team working and ability to update and inform directors and project managers on progress and risks, and how to mitigate those.
  • Work independently to complete project tasks
  • Ability to work independently to improve your quantitative research and analysis skills, and to support the director to develop new products and business opportunities
  • Microsoft Office or equivalent and email
  • Willingness to travel abroad

 

Desirable skills for this opportunity are:

  • Understanding of or experience of the role of research and analysis in either criminal justice policy, careers and training, homelessness or third sector
  • Experience of using databases and writing SQL
  • Masters degree or PhD with a substantial statistical analysis component
  • One or more years’ experience of quantitative research or analysis

 

Senior Quantitative Researcher / Analyst

You can demonstrate these essential skills:

  • Graduate degree in a subject with a substantial mathematical or statistics component
  • Three or more years’ experience of quantitative research or analysis
  • Report writing and presentations for non-technical audiences
  • Good Microsoft Excel skills
  • Good SPSS syntax or R skills
  • Experience of SQL
  • Experience of applying statistical modelling techniques to complex social issues
  • Team working and ability to update and inform directors and project managers on progress and risks, and how to mitigate those.
  • Project management of research or analytical projects
  • Work independently to complete project tasks
  • Ability to work independently to improve your quantitative research and analysis skills, and to support the director to develop new products and business opportunities
  • Microsoft Office or equivalent and email
  • Willingness to travel abroad

 

Desirable skills for this opportunity are:

  • Understanding of or experience of the role of research and analysis in either criminal justice policy, careers and training, homelessness or third sector
  • Masters degree or PhD with a substantial statistical analysis component
  • Experience of web development with C# skills

 

Making an application

You will work from our London office (pro rata 38 hours per week). A competitive salary will be paid dependent upon skills and experience.

If you are interested please send your CV and cover letter with any current salary stated to iqqra.aziz@getthedata.co.uk by 5pm on the 27th October 2017.  Please indicate in your letter whether you are interested in a full-time or part-time position. Please also indicate if there are any dates in late October or early November you cannot make for an interview. If you wish to discuss the role please email jack.cattell@getthedata.co.uk.

 

Predicting the Final CRC Re-Offending Rates

Predicting the Final CRC Reoffending Rates

Predicting the Final CRC Reoffending Rates

On October 26th 2017, the Ministry of Justice will publish the first Transforming Reoffending proven reoffending rates. These will describe the October to December 2015 cohort’s proven reoffending rate and compare this to a 2011 baseline rate (go here for an explanation). If a Community Rehabilitation Company (CRC) does better than the baseline rate they will be paid a bonus. In this blog I am using descriptive statistics to present a prediction of what the final rates will be.

England and Wales Reoffending Rate

Over the last year the Ministry of Justice has published this cohort’s, and each subsequent cohort’s, reoffending rate every 3 months. The national reoffending rate across all CRCs is described in the figure below (these figures are adjusted for differences in likelihood of reoffending across cohorts). Each bar represents a different cohort and gaps exist where the data have not yet been published.

 

Ministry of Justice Proven Reoffending Quarterly Statistics

Source: Ministry of Justice Proven Reoffending Quarterly Statistics

Definition: Months after commencement is the minimum number of months after commencement for an offender in the October 2015 to December 2015 cohort. However in the figures published, the follow up period varies depending on when within those 3 months the offender started.

 

The results are similar across the cohorts. After 8 months, 33% of offenders will have reoffended, and then 39% after 11 months, 42% after 14 months and 43% after 17 months. Most offending will occur within the first 8 months and the subsequent increases are smaller each time.

Predicting the final October to December 2015 rate

In order to predict the final reoffending rate at 18 months I need to estimate the trend. There are different options for doing this, and I will explain in a forthcoming blog how I selected the appropriate method. However, the trend I calculated for the October to December 2015 cohort is presented in the figure below.

 

Predicting the final October to December 2015 rate

Source: Ministry of Justice Proven Reoffending Quarterly Statistics

Definition: Months after commencement is the minimum number of months after commencement for an offender in the October 2015 to December 2015 cohort. However in the figures published, the follow up period varies depending on when within those 3 months the offender started.

 

The blue squares describe the published England and Wales reoffending rates, the red line is the fitted trend, and the red dot describes the predicted reoffending rate after 18 months. The predicted reoffending rate was just under 45% using this method. The selected reoffending trend is a curve to represent the reduction in the rate of increase as time progresses. Even from the limited data released, the curve shows an expected rapid increase in the first months after the start of an order or licence, but over time this increase is not sustained.  This trend is consistent with other research we have conducted using more detailed data.

October to December 2015 CRC results

I extended the same analysis to individual CRCs. When the final results are published, a CRC’s reoffending rate will be compared to a 2011 baseline rate. Since individual baseline rates for each CRC’s baseline have not been published, I cannot anticipate that analysis. However, the baseline OGRS scores (a measure of how likely someone is to reoffend) have been  published and so I was able to compare a CRC’s predicted reoffending rate to the baseline OGRS rate, adjusting for differences in the likelihood of reoffending between the baseline and the October to December 2015 cohorts. The spread in differences between the baseline OGRS rate and the predicted reoffending rate for each CRC are presented in the figure below. I have anonymised each CRC because I believed highlighting relative performance in the public domain with important information missing would not be ethical. If, however, you want to know how your CRC or area is performing please contact me.

 

October to December 2015 CRC results

Source: Ministry of Justice Proven Reoffending Quarterly Statistics

Eight of the 21 CRCS were predicted to beat the baseline OGRS score. The largest difference is 5.1%, followed by two CRCs expected to beat the baseline OGRS score by  4.4%. Twelve of the CRCs were predicted to perform worse that the baseline OGRS. For five of these the difference is less 1%, but three were expected to exceed the baseline OGRS rate by more than 3%.

Next steps

The analysis presented here is based upon a description of the data. The analysis therefore does not allow for the uncertainty in the predicted reoffending rate. A more realistic analysis would present the range of outcomes that are likely to happen. The presented analysis also assumes that the data are independent. In fact a CRC’s current reoffending rate will be dependent upon the rate 3 months ago and the current rate cannot be lower than the previous measure. A statistical model can allow for these issues and I will present that approach in a subsequent blog. This does not mean the presented results are wrong. Instead, it means greater insight and use is possible as we expand the analytical approach.

Delivering Innovative Social Impact Analytics to Sodexo Justice

We are delighted to announce a new contract to deliver our ground breaking social impact analytics to Sodexo Justice, a leading provider of justice services in the UK.

The purpose of our social impact analytics is to provide definitive evidence of an organisation’s impact on society by delivering predictive analyses and impact evaluation.  Under the newly signed contract, we will measure the effectiveness of Sodexo’s six Community Rehabilitation Companies in managing the risk associated with the offenders and delivering interventions that reduce their reoffending.

By understanding “what works?” in changing lives and delivering safer communities, our social impact analytics will also be used by Sodexo Justice to measure the impact of its services.  Sodexo Justice will be paid through a payment by results mechanism that measures its success in reducing reoffending.

Our founding director, Jack Cattell said, “We very much look forward to providing our social impact analytics to Sodexo Justice Services.  Our SIAs will provide offender managers with the information they require to manage resources and deliver high quality interventions to reduce reoffending.”