This could be software, code or scripts that you have written yourself - where possible, please make this software public, so your analysis is reproducible. For that reason, we recommend that you use open software as much as possible for your data analysis. Using open software increases the Accessiblity, Interoperability and Reusability of your data. If you want to read up on data analysis you should check out what journal articles and books the VU library has available on the subject: At the VU, most scripts are written in R, Python and SQL. In some cases researchers write their own scripts to analyse the data. Some of the software is available for download at: download.vu.nl. If multiple parties are involved in the analysis, data sharing may also be necessary.ĭata analysis often requires the use of specialised software.The software offered and licensed by the university currently includes: Stata, SPSS, and Atlas.TI. The process of cleaning and analysing data may require computing power not readily available or specific storage and protection options. Many steps may be required to gain useful information from raw data. Data analysis converts raw/processed data into information that is useful for understanding. To ensure that research is empirical and verifiable, it is crucial that researchers keep records ( data documentation) of every step made during the data analysis.Īnalysis essentially refers to breaking down a whole into its separate components for individual examination. Although data analysis is an ongoing process throughout the research project, this page focuses on the analysis of the data subsequent to its collection.
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