At regular intervals during the implementation of your activity you should review what has been achieved.  This will support accountability and reporting, as well as learning and improvement.  This will require you to analyse and interpret the evidence gathered.

The way you make sense of the evidence you collect will depend on the kind of information you have collected (facts, figures, answers, feelings, events, experiences and so on).  For example, you will need to use a different approach for analysing the number and types of attendees at awareness-raising events than making sense of the feedback from a series of one-to-one discussions.

Different types of data

Quantitative data can be organised through statistical analysis, which can range from using basic calculations - total numbers, averages, percentages and so on - to advanced mathematical techniques.

Qualitative data needs to be organised and analysed thematically; there are different ways to describe the process of identifying key themes or patterns.

How you approach your data and what you do with it depends on whether it is quantitative or qualitative.  You will need to know the following:

  • How to convert raw data into useful and applicable information
  • What techniques can be used to analyse quantitative data
  •  What techniques can be used to analyse qualitative data

Raw Data

The information you collect will be in a raw format until you do something with it. Raw data is not particularly useful until you organise and ‘clean’ it. This is simply a filtering process. Regardless of the type of data you have collected, you will need to organise it into a logical and understandable format.

Quantitative data: Microsoft Excel (or equivalent software) can be used to enter and tabulate your data into an electronic format (completed hardcopy surveys, your own records etc.). There are many free online survey tools such as SurveyMonkey that can take the hard work out of using data. Once completed, results can be downloaded in a variety of formats (Excel, PDF, PowerPoint) and analysed.

Qualitative data: Microsoft Word (or equivalent software) can be used to transcribe the data (field notes, audio recordings of interviews and/or focus groups, video recordings of observations).

You must also develop a filing system for logging and organising your data.

As you are entering or typing up data, you can sense-check it looking out for recording mistakes and typos. In research and evaluation, this is known asdata cleaning. This is a vital process because incorrect (or ‘dirty’) data can hugely skew your evaluation results.

It is important to remember that all these stages (data entry, transcribing, cleaning) can be unexpectedly time consuming so ensure you allow enough time to turn data into useful evidence.

Analysis

Once your raw data has been transcribed, cleaned, and organised, look for patterns and messages from the evidence.  There is no single best way or software package to do this analysis.  The next two sections give a little more general guidance on the analysis of quantitative and qualitative data.  There are also many useful resources, including research methods textbooks, which can provide further support.

When carrying out the analysis you will also want to ensure that the quality of the information will stand up to scrutiny. Also, make sure your methods of collecting or monitoring information give you and others what is needed. With the benefit of hindsight, there are always changes that you would make to monitoring arrangements, so ensure you make any necessary adjustments. Any mistakes will always provide useful learning and support future improvement.

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Resources

One of the key challenges is finding a system such as a database that can help to collect, store, retrieve and analyse the monitoring information for your project.

  • The Inspiring Impact Hub has brought together a searchable directory of Database & Case Management Systems  that are typically used by the community and voluntary sector.
  • Further information on buying and using Customer Relationship Management Systems can be found here.