## Process and Analyse

Once data collection is complete, the next step is to prepare your data for analysis. This stage involves cleaning your data and selecting appropriate tools to explore your findings. The tools and methods you use will depend on the nature of your research and the type of data you've collected.

#### Data cleaning: Preparing your data for analysis.

Before analysis can begin, your data must be clean and suitable for use. Data cleaning is the process of transforming raw data into a usable format. Since raw data is rarely ready for immediate analysis, this step is essential.

Depending on your data type, cleaning may involve:

- formatting and restructuring your data,
- removing duplicates,
- handling missing values,
- removing unnecessary or irrelevant data,
- validating data for consistency and accuracy.

You may also use software tools to assist with data cleaning and preparation, such as transcription software. Always ensure the tools you use are secure and supported by the VU. If you're unsure, contact the Research Data Support Desk for guidance.

#### Data Analysis: Exploring your findings

With your data cleaned and structured, you can begin the data analysis phase. This involves examining your data to answer your research questions and identify patterns, trends, or relationships.

Depending on your methodology and data type, you may use:

- Quantitative tools: Programming languages like R or Python, or statistical software such as SPSS
- Qualitative tools: Text analysis software like ATLAS.ti for coding and interpreting open-ended responses or transcripts

When working with code or analysis scripts:

- Write clear, well-documented code,
- Use comments to explain your logic and decisions,
- Create a codebook that defines your variables, values, and data structures.

This promotes transparency, reproducibility, and easier collaboration.

Version control is an essential aspect of the documentation process and should be established at this stage, if not already implemented.