Data Management
Presented by Barry DeCicco
Poor data management can destroy a research project, or cause long delays and a lot of headaches. Many researchers only discover their data problems when they conduct their analyses, when it’s too late to fix things. This leads to analyses being limited by preventable data issues. It also leads to long delays and extra work, sorting through the data and figuring out what is usable. Many researchers then suffer from long-term problems, when they want to go back and extend their project. This also means that sharing with colleagues is difficult, leading to nobody else being able to make use of the original project’s data.
In this workshop, you will learn how to plan data management *before* you collect data.
Aspects of data management involve:
- Planning data collection,
- Initial handling of your data,
- Spotting problems as soon as they emerge and fixing them while they are small,
- Keeping track of changes and version of your data, so that you always use the correct version,
- Data storage and back-up, so that you will never lose data,
- Initial data analysis, to test your data before running analyses,
- Archiving and long-term storage of data and metadata, so that you can easily access it later, and share with other researchers.
- What University resources are available to you, for experienced and professional help.
No previous knowledge of statistics or statistical software is required.
MSU graduate students can apply for a waiver of the $10 registration fee: https://msu.co1.qualtrics.com/jfe/form/SV_7ao9XSJkxZuXTLM