Tag Archive for: What is quantitative data?

What are the Indices of Multiple Deprivation?

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What are the Indices of Multiple Deprivation?

If you have ever read one of our reports, you’ll likely see reference to something called the Indices of Multiple Deprivation, or ‘IMD’.

We use the IMD as a powerful tool to analyse and understand the barriers and socio-economic status of audiences, participants, and volunteers. We use postcodes to measure the IMD status, and this means that people find it easy to answer. This does mean that we are looking at the household status too.
The results give our clients an idea of which people they are attracting to their projects and what barriers they may be facing in life.

Statistically, people who live within areas of greater deprivation have more barriers in their lives and are less likely to engage with the arts and nature and are more likely to have greater issues with their wellbeing. That’s one of the reasons funders do like to understand if projects and organisations are reaching people from the most deprived areas.
How are the indices of multiple deprivation worked out?
The IMD is worked out using a range of factors which are assessed across the UK and given a score. The factors are listed below:

  • Income deprivation: how many people in the area are experiencing low-income levels.
  • Employment deprivation: what is the rate of unemployment in the area?
  • Education deprivation: what are the average qualifications within the area, and how easy is it to access quality education?
  • Health deprivation and disability: what is the average life expectancy and disability prevalence in the area?
  • Crime: how much crime occurs within the area
  • Housing deprivation: how affordable is housing within the area, and are there other barriers to people accessing housing
  • Living environment: what is the air quality like in the area, and are there green spaces?

These scores are compiled to give a ranking, which are then categorised into ten deciles. The entirety of England falls equally into one of ten deciles, with decile 1 indicating the most deprivation, and area 10 having the least deprivation.
If you were targeting deprived areas, you might want to reach 50% of people living in IMD1 and 2 for example. Or if you wanted a perfect section of the UK represented in your project, you might want to see 10% of your audience from each decile.

We think the IMD is a useful tool and aim to use it in every evaluation where relevant.

Learning about data; Working with quantitative data

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In a previous blog, we described quantitative data as data that can be counted or measured in numerical values. A spreadsheet is a good basis for organising data ready for analysis. Now let us imagine you have some data to hand, this is how you might want to try and use it.

Ways to analyse quantitative data.

Example 1: Looking at what the data tells you itself.

We generally start with calculating averages to consider the range of data (the highest score and the lowest score collected).  If we consider 2 sets of data (Data set A and Data set B):

From Data Set A, it is apparent that a similar experience was shared by the large majority of the audience because the average score is 40, but the range is from 36-44. That means the lowest answer was 36, which is quite close to 40 and the highest was 44 which is still quite close to 40.

However, in Data Set B, there was a much wider range of experience.  This should be questioned as to why experiences are so varied, illustrated by the wide range in the data.  Why did some individuals have such a higher score than others? Did some people have a different tutor, or venue, or have more time? There are many reasons why the range could be so high.

Example 2: Comparing data to other national data available.

Another possible way to compare data could be as in the worked example below where the Warwick Edinburgh Scale of Wellbeing was used to measure participant wellbeing.

Participants scores were collected over a period of time, firstly at the start of their engagement and then, at the end.  This graph represents a comparison in:

  • Each individual’s wellbeing from the start versus at the end. This is often a reflection of the impact of their engagement.
  • The individuals’ wellbeing who took part versus the national average
  • The individuals’ wellbeing score versus the NHS score of 40 which is believed to be indicative of poor wellbeing.
  • The individuals’ wellbeing versus one another.

Using quantitative data to help with forecasting & future decision making

Using percentages makes making comparisons easier to relate to and understand.

We use an example of a cinema and record audience attendance numbers of time.  Attendance could be affected by the popularity of the content of the film, seasonal trends or weather.  The trend line shows the overall audience is growing and then this line can be used to forecast on at the same trajectory.  This will help you to identify if you are likely to achieve your audience targets.

This information can also help you to make decisions about your capacity too and streamline your resources. For example, if you were thinking about moving a cinema location to a larger space, it would be worth looking at the trend line to think about venue size. If you were looking at two venues and one had a capacity of 100, and another 200, you might want to look at what your predicted audience size would be in six months’ time and negotiate the lease or hire accordingly. This is an example of making a data-driven-decision, something we are passionate about at The Evaluator.

Taking notice of negative space in data

It’s important to take note of the 70% who are agreeing but also to take note of the 30% who are disagreeing and find out why this is the case.  It is worth delving a bit deeper into on the minority and finding out what was the cause of their response.

At The Evaluator, we tend to represent 3 answers ‘yes’, ‘no’ and ‘prefer not to say’ in our reporting. If there’s a high number who indicate that they would ‘prefer not to say’ then it would be suggested they might insecure about completing the survey.  Often their indecision is explained in their qualitative answers and this is worth taking note of when creating future surveys.

Dealing with ‘satisficing’ survey responses.

This is the term we use when people respond with the answers that they think you are looking for and may not read all the questions.  An indication of satisficing is when respondents repeatedly choose 3 when presented with a 1-5 scale. We don’t come across these very often, as we spend a lot of time making sure our surveys are easy to complete, and varied, but if we do spot them, we will try and remove these answers from our data analysis.  It’s important to encourage honesty in answering survey questions.

A final tip

All data can be segmented but you do need to think about the time you have to spend on this as it is time consuming.  In segmentation ideally you are looking for what is more than 10% different to the average. In the example of the graph below, it is worth looking at segmenting to see the results demographics of the areas that are 10% above and 10% below the average line.

 

 

Learning about data; What is quantitative data?

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What is Quantitative Data?

Quantitative data is data that can be counted or measured in numerical values.  As with qualitative data, there’s a good chance that you already have some collected for your organisation.

You might have collected some of the following:

  • Sign-in sheets
  • Feedback forms
  • Surveys
  • Polls
  • Social media statistics
  • Reports

We often find clients already have quite a bit of data they didn’t know they had collected!

The differences between primary and secondary quantitative data.

There is a distinction between primary and secondary quantitative data.  Primary data is the data that your organisation has collected directly, such as footfall counts or feedback forms. Secondary data is data someone else has collected, for example a national age profile, or a partner shares their footfall data. It can helpful for you to draw comparisons between your collected data and national averages to see how your organisation compares.

Overcoming the challenges of working with quantitative data

There are some challenges to working with quantitative data.  Often the biggest challenge is that it’s not collected in a format that makes it easy to compare to other collected data, or to secondary sources. The best solution for this is to plan in advance and use standardised questions at every opportunity to collect data. The answer format is also important so choosing a standard answer format will make it easier to compare data.

Tracking codes can be useful in identifying if you know the same person will be answering a survey multiple times so you can monitor their progress.  A tracking code can be created within a survey using data such as: a combination of a person’s date of birth and their initials.

It’s always important to date the data particularly for paper copies of surveys which makes identifying the event possible and the data relatable to that event. If one of your feedback forms reveals a problem with the venue or experience, you need to know the date on which that particular event happened to make sure you can address the problem. Don’t forget, feedback forms may be input or analysed as a batch of forms after a few months of collection so it may prove difficult to find out which venue the problem occurred at if you don’t have a way to check.

Managing personal data from surveys

GDPR (General Data Protection Regulation) are regulations which relate to how we retain and use personal data.  Within these regulations it is important to:

It’s important to maintain confidentiality and anonymity with personal information.  Recording date of birth and full name poses a risk to personal identity, however, recording only a date of birth is not identifiable. There are also additional regulations regarding collection data from under 16-year-olds. It is possible to collect identifiable information, but if you do so you need to ensure that the data is obtained with consent, is properly secured and then destroyed once no longer needed.

Thinking about when to collect data

Recording information in the moment is valuable so it can help to set up processes to ensure you don’t miss out! One tactic that works is to have a standard question you ask at the end of every event.  This, and the size of audience questioned can be collected for contextual purposes to see if the responses were representative of the larger audience.

Top tip – you don’t need to collect data from everyone!

Deciding how much data to collect

It’s important to consider whether sample sizes are large enough to provide you with sufficient data to base a decision on.  10% to 20% of the audience is usually a reliable sample size to base a decision on. If you hold many smaller events, it would be advisable to collect evidence from each event and consider it accumulatively to make decisions.