Data extraction tables can be used to organize what information needs to be collected from the refined body of literature. This is done by noting what data from each individual study is necessary for analysis.
Boland, Cherry, & Dickson (2017, p. 97) offer examples of important information to include in these tables.
Build the table according to information relevant to the research question. If there is information missing, it doesn't hurt to contact the researchers to ask if they can provide the missing information.
Depending on the reference management software used, some of the data extraction process can be automated. This is especially true for users of Covidence.
Covidence is a useful tool in managing references, collaborating with the research team, and also helps in data extraction. Refer below to resources on Covidence.
Note: Duquesne students, staff, and faculty get free access to all of Covidence's software. For those that are not affiliated with Duquesne, Covidence does have a free version for researchers. Some limitations apply, including a cap at 500 references and a limit to 1 systematic review per free account.
This guide highlights many options that are available to Duquesne-affiliated researchers, especially Covidence. While Covidence is highly recommended for use in reference management, collaboration, and data extraction, there are additional resources available to researchers. Some of these options are listed below.