Your assessment question drives the methodology you choose for data collection, and conducting a literature review is one of the best ways to gain insight into the benefits and drawbacks of different methods used to answer similar questions.
Selecting a tool that is valid and reliable
If you intend to use or create a tool for data collection (survey, rubric, etc.) as part of your assessment project, you must consider whether that tool is valid and reliable. A tool is considered valid when it measures what it purports to measure. A reliable tool gives the same value when the same thing is measured multiple times, including by different users.
Conducting an assessment with a tool that is not valid and reliable may produce misleading results. Tools created internally need to be tested for their validity and reliability. It is often preferable to find a tool that has already been shown to be valid and reliable, and conducting a literature review is probably the best way to find such a tool.
Sampling considerations
Unless you plan to include the entire population of interest in your study (e.g., every student at your university or every book in your collection), you will need to select a sample. Some common types of sample selection are listed here.
Your sample size may be dictated by practical considerations such as time and money, but keep in mind that for quantitative assessment, your sample size may restrict the statistical analyses you can conduct with your results. For more information about sample sizes, see (link).
Human Subjects
If your assessment involves human subjects, you should consult the institutional review board at your campus to determine whether you must submit a human subjects application for review.
While the specifics of analyzing qualitative and quantitative data are beyond the scope of this toolkit, your campus may have some valuable resources as well. It is recommended that you contact your campus office of institutional research when designing your assessment to develop your plan for data analysis before you begin collecting data and ensure that the data you collect fits your intentions for analysis.
Questions to consider: