Whatever type of information you have collected, analysis and discussion of your findings will be time-consuming. This
can be daunting for those new to inquiry, but once begun can reveal fascinating discoveries.
Data needs analysing – it does not 'speak for itself'. Different people interpret evidence differently. Your analysis
will support your interpretation of your data.
Analysis is about organising the information you have collected, drawing out themes and trends, and interpreting its significance
within the context of the study.
How you analyse your data will be determined by the type of information you have collected and should be part of your research
design. The format of the data (questionnaire replies, test scores, audio or video recordings, written accounts, etc.) will
determine the resources required.
There are many long-established techniques that can be applied although sometimes you will need to invent or adapt the analysis
to fit your research question.
Although free software packages designed for specific data analysis are available, high-specification applications can be
expensive. Most practitioner researchers rely on the traditional methods unless their project is part of a course of study
and they have access to college or university computer facilities.
The process of preparing data for analysis should begin as it is collected. Raw data needs to be sorted, coded and indexed
in a systemised way.
In some strategies (eg grounded theory), preliminary data analysis informs continuing data collection.
Analysis should result in a balanced interpretation of what the data reveals, including acknowledgement of any limitations
that will impact on your conclusions.
Coding is the process of packaging information. It can be built into the data collection strategy. For example, a question
might read: 'Is the young person aged 5-7, 8-11, 12-14, 15-16, 16+?'. The coding for these groups may then be 5-7 = 1, 8-11=
2, and so on. This approach is known as deductive coding and can really save analysis time. Despite this
advantage, it requires thorough planning to cover all possible responses.
Inductive coding supports exploratory inquiry, since responses do not need to fit pre-determined constraints.
The coding is derived from categories suggested by the data. For example, answers to: 'Where do you go on holiday?' may be
best categorised by town, region, country, etc., depending on the range of responses given.
Computer applications save time and allow for more complex analysis. However, the availability of such hardware and software
should not sway your method of analysis. The primary consideration should always be to use the method that is most appropriate
for the research question and data type.
Many experienced researchers, particularly in the quantitative field, argue that though undertaking the process yourself adds
time to the analysis phase, it can have a beneficial impact through the interpretations you form and modify as you work with
your data.
This example illustrates that it is how the data is understood that influences the conclusions.
This is a prime example of a situation where analysing observational data alongside other sources of data (triangulation)
may produce a more rounded interpretation.
It also makes a case for involving other people at key points during your data analysis.
How will others interpret a sample of your material? It may be that they offer a different or better perspective from your
own. If several people interpret the information one way, this may be the more valid interpretation. This is known as interrater
reliability.
Blaxter, J, Hughes, C. and Tight, M. (2001) How to Research, Buckingham: Open University Press.
Cohen, L., Manion, L. and Morrison K. (2011) Research Methods in Education. 7th edition. London Routledge.