DATA ANALYSIS GUIDE
How to analyse, interpret and present research data.
Learn how researchers turn collected data into meaningful evidence, select appropriate analytical methods, interpret findings and connect the results back to research questions and objectives.
UNDERSTANDING DATA ANALYSIS
Turning research data into meaningful evidence
Data analysis is the process of examining, organising and interpreting information collected during a research project. The purpose is not simply to produce tables, charts or themes, but to identify patterns and evidence that help answer the research questions.
The appropriate approach depends on the research design and the type of data collected. Quantitative studies commonly work with numerical data and statistical techniques, while qualitative studies often examine words, experiences, meanings and themes. Mixed-method research may combine both approaches.
THE ANALYSIS PROCESS
A structured approach to analysing research data
Good analysis follows a logical process. Researchers first prepare and organise their data before applying analytical techniques that are appropriate for their research questions, objectives and methodology.
STEP 01
Prepare the data
Begin by organising the information collected during the research. Depending on the study, this may involve checking survey responses, transcribing interviews, organising documents, coding variables or removing unusable entries.
STEP 02
Check data quality
Researchers need to consider missing values, inconsistent responses, duplicate observations, errors and other issues that could affect the analysis. The checks used should be appropriate to the type of data and research design.
STEP 03
Select the analytical method
The analytical technique should follow from the research questions, objectives, methodology and type of evidence collected. The method should not be selected simply because it is familiar or easy to use.
STEP 04
Analyse the evidence
Apply the selected procedures systematically. Quantitative research may involve descriptive or inferential statistics, while qualitative research may involve coding, categorisation and thematic analysis.
STEP 05
Interpret the findings
Analysis produces evidence, but researchers must explain what that evidence means in relation to the research questions and objectives. Interpretation should remain grounded in the results rather than going beyond what the data supports.
STEP 06
Report the results
Present the findings clearly using appropriate tables, charts, statistical results, themes, quotations or other forms of evidence. The presentation should make it clear how the findings answer the research questions.
CHOOSING AN APPROACH
Quantitative, qualitative and mixed-method analysis
Different types of research data require different analytical approaches. The research methodology should provide the foundation for deciding how the evidence will be examined.
Quantitative Analysis
Quantitative analysis works with numerical data. Researchers may use descriptive statistics to summarise the dataset and, where appropriate, inferential techniques to examine relationships, differences or other patterns.
Common outputs can include frequencies, percentages, means, measures of variation, tables, charts and statistical test results.
Qualitative Analysis
Qualitative analysis examines non-numerical information such as interview transcripts, observations, documents and open-ended responses.
Researchers may code the data, identify recurring patterns, develop categories and construct themes that help explain participants' experiences, perspectives or meanings.
Mixed-Method Analysis
Mixed-method research combines quantitative and qualitative evidence within the same research project.
Analysis may therefore involve separate quantitative and qualitative procedures followed by an appropriate process for bringing the findings together.
ALIGNMENT
Analysis should answer the research questions
A strong research project maintains alignment between its research problem, questions, objectives, methodology, data and analysis. The analytical process should therefore be capable of producing evidence that addresses the questions the study set out to investigate.
This means researchers should avoid collecting large amounts of information without a clear analytical purpose. Each major variable, question, interview theme or dataset should have a meaningful relationship with the objectives of the study.
INTERPRETATION
Analysis is more than describing the results
Reporting that a particular result occurred is only one part of research analysis. Researchers also need to consider what the result indicates in the context of the research question, theoretical framework and existing evidence.
Interpretation should be careful and evidence-based. Findings should not be presented as proving more than the research design and data can reasonably support.
Questions to ask when interpreting findings
- • What patterns or relationships are visible in the data?
- • How do the findings relate to each research question?
- • What do the findings suggest in relation to the research objectives?
- • Are the findings consistent with previous research?
- • Are there alternative explanations that should be considered?
- • What limitations affect how the findings should be interpreted?
COMMON PROBLEMS
Common mistakes in research data analysis
Choosing a method without considering the research question
An analytical technique should be justified by the research design, question and type of data rather than selected simply because it is available.
Treating description as interpretation
Listing numbers, percentages or themes does not by itself explain what the findings mean for the research problem.
Ignoring data quality
Missing, inconsistent or unreliable data can affect the credibility of the findings and should be considered during analysis.
Making claims beyond the evidence
Conclusions should remain proportionate to the data, methodology, sample and limitations of the research.
THE BIG PICTURE
From collected data to research findings
Data analysis sits between data collection and the final interpretation of research findings. A well-designed project connects these stages: the research questions guide the data collection, the methodology informs the analysis, and the analysis produces evidence that supports the findings and conclusions.
CONTINUE LEARNING
Explore the wider research process
Data analysis works as part of a wider research process. Explore the related guides to understand how research questions, objectives, methodology and data collection connect with analysis.
Research Questions →
Learn how focused research questions establish what a study needs to investigate.
Research Objectives →
Understand how research objectives translate the research problem into specific outcomes.
Research Methodology →
Explore how researchers select appropriate approaches and methods for a study.
Data Collection →
Understand how primary and secondary research data can be collected systematically.
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