What is Data Analysis?
Data Analysis is the process of examining, cleaning, transforming, and modeling data to extract useful information, identify patterns, and support decision-making. It’s a critical step in research that turns raw data into meaningful insights.
Purpose of Data Analysis
The main goal is to discover trends and relationships that help answer research questions or test hypotheses. It involves both numerical and non-numerical data, depending on the research design.
Types of Data Analysis
- Descriptive Analysis: Summarises basic data features using averages, percentages, and charts.
- Inferential Analysis: Uses statistical tests to make predictions or generalisations.
- Qualitative Analysis: Focuses on identifying themes, meanings, and patterns in textual or visual data.
Steps in Data Analysis
- Collect and prepare data (remove errors and inconsistencies).
- Organise data using tables or coding schemes.
- Apply appropriate analysis methods (statistical or thematic).
- Interpret findings in context of research questions.
- Present results through charts, graphs, or narratives.
Common Tools
- SPSS, R, Python for statistical analysis.
- NVivo, Atlas.ti for qualitative analysis.
- Excel and Google Sheets for basic computation and visualisation.
Challenges
- Ensuring data accuracy and reliability.
- Handling missing or incomplete data.
- Interpreting results without bias.
Example Applications
- Analysing survey responses to identify customer satisfaction drivers.
- Examining test scores to evaluate educational outcomes.
- Studying health data to track disease patterns.