Zickmund SL (VA Pittsburgh, Center for Health Equity Research and Promotion (CHERP)), Rodriguez KL
(VA Pittsburgh, CHERP)
The Veterans Administration has taken a leading role in supporting the use of mixed-method evaluation approaches within the area of health services research. To effectively carry out projects collecting both quantitative and qualitative data, the use of digital technology to manage the qualitative data is essential—especially those with multiple investigators/sites and large datasets. The overarching objective of this workshop is to learn how to transform qualitative texts into a data format that can be easily used in mixed-methods studies and analyses. More specifically, techniques on how to score and organize textual data, as well as the hands-on training on how to use a specific computer software program, Atlas.ti, will be provided. Further, the workshop will focus on the steps needed in the generation and management of “mixed” data.
The proposed workshop will largely introduce methods needed for the translation of qualitative textual findings into numeric codes by way of a software package. We will use Atlas.ti, as it is the dominant software package employed, has an active technical support system, and translates qualitative data numerically into both a spreadsheet form and a statistical package form. This workshop will also provide participants with detailed hands-on instruction and informational handouts on how to use the Atlas.ti software program for entering, managing, analyzing, and summarizing qualitative data files. Included will be a demonstration for the creation and documentation of coding files, mechanisms for retrieving qualitative codes, as well as output functions for table and spreadsheet construction. Discussions of how to set up and calculate intercoder reliability will also be discussed. Participants will be encouraged prior to course attendance to download a demo version of the program from the Internet (www.atlas.ti.com) and to bring their laptops with them as a way of participating in the demonstration.
Investigators and staff who are interested in learning to manage and analyze mixed methods data.
Assumed Audience Familiarity with Topic: