The team has experience with SPSS, Stata, R (and R Studio), Python, HTML, UNIX, FORTRAN, QuickBasic, Java, SQL etc. We spend a lot of time compiling syntax and code for various stages of data processing, analysis, migration and transference. Syntax is compiled for all quantitative datasets, acting as the upgrade agent. The syntax is canada rcs data retained, serving as the archival record of all recoding, variable frequencies, variable labelling, value label and missing value label updates etc., for each data file. Macro programming for qualitative interview transcripts helps locate disclosive text and other transcription errors.
We adapt and run Python code to act on data files to produce data dictionaries for each quantitative data file, describing survey variables and response values. The UK Data Archive Data Dictionaries are very informative documents, giving a quick and searchable profile of each data file, thus enabling potential data users the opportunity to examine the data content of each dataset beforehand.
The Python code, once executed, creates alternative data formats for data users to choose from. Where data are deposited in SPSS, Stata and tab-delimited versions of the data files are created for those researchers that may wish to analyse the data in other software packages (for example R). Data users can also request alternative formats such as SAS. Qualitative data users can choose to investigate interview transcripts in Rich Text Format and Portable Document Format, and various image, audio and video formats are also made available, where possible.