Quantitativedata analysis is very useful in an evaluation. It can be analysed in a variety of ways and be grouped, categorised, understood, and displayed to great effect.

Before you can analyse your data, you must organise it by type of information.  Below is a table summarising the types of information you are likely to want to record in your evaluation, and some of the more technical names for it.

Once you have identified these types of data and recorded them it will allow your data to start telling a story.  You can then interpret and expand on this narrative when reporting and writing up.  Recording is best done in a spreadsheet software package that allows you to do calculations and produce charts or graphs.  Various packages are available, the most common being Excel by Microsoft but other open source and web based options are available. 

For the purposes of evaluation most organisations will only need a fairly simple analysis of quantitative data. These usually includeaverages,frequenciesandpercentages. You could put your data into a table which would allow you to explore how often something occurred, how many participants reported what types of outcomes and in what order, etc. The table below is a small example.

This type of analysis would allow you to confidently report the number and percentage of participants of different types and experiencing different types of outcomes.

It is important to remember that the data collected in open text/comment boxes in a survey can be treated as qualitative data, which is discussed elsewhere in this guide.  Equally important to note is the ability to pull quantitative data out from qualitative sources.  For example, you could code and count the number of times a particular phrase came up in interviews.

Evaluation Support Scotland have produced a helpful guide to analysing information: ESS Support Guide 3.1 - Analysing information for evaluation.  The guide also contains some useful terminology and descriptions you may want to familiarise yourself with.  They can be useful if you need to analyse, explain and present numbers in reports or presentations and are included below:

Absolutes: the numbers stand as they are - this works best if your numbers are small.  For example, 9 out of 10 participants got jobs.

Sums: adding and subtracting numbers.  For example, five people who took part on the project said they increased their ability to “say no to drugs and alcohol” by two points on a scale.

Average: for example, the average weight loss for men on a health and fitness programme was 4.7 kg. Watch out for figures that don’t make sense such as 23.6 people.  You should also be aware how extremes can skew the average.  If some people lose a lot of weight but most do not, then an average may be misleading.

Mode: the most frequent occurrences, for example, out of 30 people, one person got an ECDL but 27 people learnt how to make personalised cards using a digital camera.

Range: for example, at the start of the course people got between 14 and 20 answers out of 40 correct and at the end they got between 22 and 37.  Ranges can be useful when you need to illustrate diversity.

Median: the mid-point of a range of numbers.  For example in the list of numbers: 22, 28, 34, 35, 37, the median is 34.

Percentages: for example, 58% of participants increased the amount of exercise they do. But don’t use percentages with small numbers.  For example 80% of trainees got jobs may just mean four out of five.