After selecting a method, you must carefully plan how you will manage and analyze the data you collect. This involves considering and addressing questions such as:
If your study involves human participants, you will need to seek approval from the Research Ethics Board by submitting a detailed plan that includes how you will properly manage, storage, and use the data you collect. If your project plan changes at some point in any way, you might also need to submit an amendment or seek re-approval.
Research data is the information that you will collect and analyze as primary source(s) throughout the course of a project.
According to the Government of Canada, research data are the source(s):
...that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. What is considered relevant research data is often highly contextual, and determining what counts as such should be guided by disciplinary norms. (n.d., “What is research data?)
Research data may be comprised of numbers, but are not synonymous with numbers; it can also come in the form of interview transcripts, words extracted from documents, and many other types of text-based sources.
According to the Digital Research Alliance of Canada, research data management (RDM) refers to "the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data, and allows researchers to find and access data" (n.d., "What is RDM?). It intersects with open and collaborative research practices, as it emphasizes sharing your research data whenever possible.
Current best practices in RDM centre follow the principles that research data should be made Findable, Accessible, Interoperable, and Reusable (FAIR) (note this is not equivalent to making data open by default; the phrase "as open as possible, as closed as necessary" is often used to describe this distinction):
RDM supports researchers to work effectively and ethically with the data they collect. More broadly, it's important for ensuring that data is contributing to the wider community and enhancing the transparency and reliability of research findings.
Below are selected resources and organizations that provide training and tools for RDM. The Research & Instruction Librarian is also available to assist you in understanding the FAIR principles and engaging in effective RDM practices.
Data analysis refers to the way in which you will extract meaning from the data you have collected and reach conclusions that answer your initial research question or problem statement.
How you analyze and interpret the data will depend broadly on the type of methodology you selected (i.e. qualitative, quantitative, or mixed methods) and, in particular, how you designed the project. It may also depend on the theoretical paradigm or conceptual position you are working within.
At this stage, it is often helpful to use an online tool or software application to assist in ensuring your findings are accurate and alleviating some of the burden of manipulating your data through statistical tests and other forms of analysis.
Examples of statistical analysis software include SPSS, Excel, SAS, and R. Examples of qualitative software include NVivo and ATLAS.ti. Most of these are subscription-based and offer trials to help you determine if they suit your needs; below are lists of options that are free or open source:
Doing Qualitative Research in a Digital World by 
An Introduction to Statistics and Data Analysis Using Stata® by 
Qualitative Data Analysis: A Methods Sourcebook by 
SPSS survival manual: a step by step guide to data analysis using IBM SPSS by 