ATLAS.ti

Analytic Activities

QDAS packages should not be confused with data analysis software such as SPSS, STATA, or SAS, which automatically analyze the data according to statistical formulas. Rather, ATLAS.ti is a platform, or workbench, in which researchers can choose how to organize, store, and structure their unstructured [Page 130]or semi-structured data in a systematic way that is aligned with their methodological approach. As described by the software manual, visualization, integration, serendipity, and exploration principles underlie its design. Visualization tools help researchers elicit meaning from the data; all project materials can be integrated within the software; browsing the data with the software encourages serendipitous findings; and the software supports an exploratory yet systematic approach to analysis.

Any qualitative methodological approach can be enacted in the software, be it thematic analysis, grounded theory analysis, discourse analysis, or ethnographic approaches to name a few. Christina Silver and Ann Lewins have suggested that there are five main categories of analytic activities that can be supported by ATLAS.ti and other QDAS packages: integrating data sources and analytic approaches; exploring the content and structure of the data; organizing materials and ideas; reflecting on data, interpretations, processes, and results; and retrieving, reviewing, and rethinking ideas about the data.

ATLAS.ti can be used to create what Zdeneˇk Konopásek called a textual laboratory to organize, store, and manage data sources alongside other project documents such as data collection instruments, ethics board approval forms, and even the research literature. Literature reviews require, in essence, a type of qualitative data analysis, and PDFs of articles can be uploaded into ATLAS.ti and the components used to analyze them in a systematic and visible way. With version 8, bibliographic data from reference management software such as Endnote, Zotero, and Mendeley can be imported into a project and triangulated with other data sources. Organizing the data in a project file makes the data portable, and annotating the data within the ATLAS.ti project file creates a visible audit trail. Both of these features support smooth collaboration across team members.

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