Data Collection during delivery
An important consideration for any organisation when undertaking a piece of delivery is understanding what data to collect.
Organisations might want to consider:
- The different types of data they could collect
- At what stage in the process they will be collecting this data
- The scenario in which the delivery takes place
- What is proportionate for the organisation and the individuals attending
This guide will look to provide detail around these areas and ideas to support organisations with this decision-making process.
Different types of data organisations can collect
These will be more relevant for some delivery than others but examples of this could be:
- Participant / Attendee data - the demographic detail of your participants. It's important to know if you are reaching who you aim to reach.
- Examples of this could be the 'Gender' or 'Ethnicity' of individuals
- This could also include custom questions such as whether the attendee is in receipt of 'Free School Meals'
- Organisations can customise the data fields on their Upshot account.
- Attendance data - the attendance or engagement of your services and activities. It's important to know who is turning up, when, what they're attending, how many times they attend and in what capacity (e.g. participant, volunteering, mentoring or leading the session).
- Organisations could collect this through registers or head counts on their Upshot account.
- Feedback data - the quality and perception of the services and activities you are delivering. It's important to know what stakeholders, including participants think of your delivery.
- Outcomes data - the difference that is being made through your service and activities to participants' lives. It's important to know what changes are hopefully happening to participants because of your delivery.
- Organisations would link their outcomes to activities within Upshot.
- Organisations may also see individual attendee outcomes, such as the gaining of a qualification or the progression into employment as milestones they look to record on attendee profiles via the Timeline Event feature.
- Impact data - the difference that has been made in the long-term. It's important to know whether services have made a long-term difference to the participants, families or wider communities they are there to serve.
- Organisations might conduct surveys that look to measure change in an individual's attitudes or behaviour.
Stages of Data Collection
There are often four different stages for data collection when delivering. Not all of these stages may be available in every instance.
- Pre Session - Details about who is attending - i.e. participant data
This could be achieved through registration forms, booking systems or other sign-up processes. It's key to ensure you are asking for only the information you need to avoid barriers to entry.
On Upshot you could the Attendee sign-up form to allow people to register onto your database in advance, or if you have a rough idea of who is attending a future session, you can even take a 'draft register' to record those who you think will be in attendance. Some organisations also collect surveys at this stage, pre engagement, to collect a 'baseline' about the individual's feelings, thoughts, knowledge or behaviour.
- During Delivering - What they are doing - i.e. engagement data. Attendances, milestones achieved etc.
On Upshot you would be taking a register or head count here to record who attended, or how many people attended. Timeline Events could also be used to record any milestones achieved or developments during the session(s). Extra evidence could be collected such as photos or videos stored as Media on the system.
- Post Session - How they felt - i.e. feedback data. Their thoughts and feelings about the session and delivery.
The goal is to understand how people felt, to find out what worked and what didn't and to identify gaps.
This may be captured via a feedback survey after a session or even Timeline Events to log any anecdotal comments or thoughts post-session.
- Post Programme - So what? i.e. outcome and impact data. Changes in attitudes and behaviours.
Follow up surveys are a very common way to collect post-programme impact data. Some organisations also combine follow up surveys with baseline or pre-programme surveys to enable a comparison to be done to measure impact.
Following this it will be key to conduct some analysis and report on your data. Within Upshot the Reports Explained guide will help identify the relevant reports to use. Organisations may then look to visualise their data in external tools or use the tips presented here on how to turn their Upshot data into graphs and charts on Excel.
Delivery Scenarios
These can vary greatly between organisations, their projects and even individual sessions that take place. Examples of this might be:
- Types of Delivery
- In person or Online
- 1:1 or a Group session
- Open Access or Booked Sessions
- Sensitive contexts
These might be illustrated by either the Location or Activity Type on Upshot.
- Frequency of Delivery
- One-off
- Recurring
- Intensive
- Time-bound
This might be set up in advance of blocks of delivery by creating sessions.
- Purpose of Delivery
- To engage new people
- Retain members
- Tackle a social issue
This could be set up when creating your activities by mapping your Activity (what you're doing) to your Outcomes (what you're trying to achieve).
Proportionate Data Collection
The benefits of proportionate data collection would be felt by your data collection team as well as the audience that you serve.
Some of the main benefits of proportionate data collection include:
- Reduced participant burden:
- Decreased fatigue - asking participants to provide only essential information reduces survey fatigue, which can compromise the quality of the data if participants become tired or disinterested. E.g ensuring any registration form or survey questions that you include are necessary.
- Increased participation rates - a shorter, more relevant data collection process is more likely to encourage participation and reduce dropout rates in longitudinal studies.
- Enhanced data quality:
- Relevance and precision - focusing on essential data points increases the relevance and accuracy of the analysis, leading to more precise insights and conclusions.
- Reduced noise - limiting data to what is necessary helps to avoid the 'noise' that can obscure important trends and patterns, making it easier to draw meaningful conclusions.
- Ethical Considerations:
- Respect for participants - collecting only the data that is necessary shows respect for participants' time and privacy.
- Minimising risk - every piece of data collected can pose a risk to participants if mishandled. Proportionate data collection minimises these risks by limiting the amount of sensitive or identifying information gathered.
- Adaptability and Scalability:
- Scalability - a streamlined data collection framework is easier to scale across multiple sites or projects.
- Flexibility - projects often need to adapt to changing circumstances. A proportionate approach makes it easier to adjust data collection methods as projects evolve without overhauling the entire data system.