Step 6 – analyse the findings

Now you have gathered a rich array of data at step 5 of the Research Cycle, it is time to set to the task of analysing the findings. It is likely that you will have collected a wealth of raw data using your chosen research methodology. If you have used a range of research methodologies, your data may include transcripts from discussions or interviews, responses to questionnaires, recordings from class groups, diamond 9 pictures and accompanying notes. You now need to think carefully about deciding which data to analyse. You need to seek out the
data that is helpful in answering your research question. The analysis of findings helps you search for patterns within the data gathered. Patterns within the data may be presented as similarities or differences, common occurrences, abnormalities or points of interest.

There are a range of strategies to help you analyse your data. Each strategy depends on your ability to eyeball the data. Eyeballing is a technique that, with practice, helps you to seek meaning from the data that is presented whether in numerical, graphical, pictorial, auditory or written form.

In order to strengthen your ability to eyeball data, you can ask the following critical
questions as you examine the data and charts:
Stepping back from your data, what jumps out at you?
• What are the prominent similarities in the data presented and why
might this be the case?
• What are the prominent differences in the data presented and why
might this be the case?
• What didn’t you expect to see in this data?
• Does anything support your assumptions relating to your
research question?
• Does anything challenge your assumptions relating to your
research question?
• Is there anything in this chart that stands out as unusual or surprising?
• Does your wider evidence base support or challenge what you see in
this data?
• Can you trust this data? Why?
• Are there any other ways you can represent your data? Would a
different chart help you see something new?
• How does X affect Y?
• Is there anything that you would have changed in your methodology
now you have analysed this data

Here are my key points about analysis of data:

Analysis is the art of unpacking or breaking down the data for analysis in order to bring clarity to what the data is saying.
Synthesis is the art of drawing together the many elements of data in order to form a conclusion.
• Seek out data that is helpful in answering your research question.
• Help define cause and effect within research through the use of a flow map.
• Compare and contrast data using a double bubble map.
• The tree map helps you sort data into categories.
• While not essential in the analysis of data, words and images (qualitative data) can be turned into numbers (quantitative data) to broaden the analysis of the data.
Eyeballing is the process of reading and rereading data to seek for relevant patterns or anomalies in the data set.
Bar charts are helpful when comparing the frequency of a measurement
across a range of variables.
• The distribution chart uses a range rather than a single number in a data set, helping to group larger data sets into bite sized chunks of data that is easier to interpret.
Pie charts are helpful when presenting data relating to the frequency of one numerical variable against a categorical variable.
Percentage component bar charts compare percentage responses across multiple sets of data.
• A spider or radar chart presents data visually showing the relative scale of response on a common scale for a range of variables.
Line charts are helpful when interpreting data at a range of points in time.
Scatter charts group data to present patterns when looking at two scaled variables.
• When analysing the data, you must hold your assumptions lightly in order to allow the data to tell the story of research.
• Be alert to your own conscious or unconscious bias in your analysis and when answering your research question.

I explore these points in more detail in my book, Irresistible Learning – embedding a culture of research in schools.

Back to the Research Cycle.

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