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Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approaches

Overview of attention for article published in BMC Research Notes, March 2016
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Title
Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approaches
Published in
BMC Research Notes, March 2016
DOI 10.1186/s13104-016-1954-1
Pubmed ID
Authors

Daniel Grossoehme, Ellen Lipstein

Abstract

Longitudinal qualitative research methods can add depth and understanding to health care research, especially on topics such as chronic conditions, adherence and changing health policies. In this manuscript we describe when and how to undertake two different applied approaches to analyzing longitudinal qualitative data: a recurrent cross-sectional approach and a trajectory approach. A recurrent cross-sectional approach is most appropriate when the primary interest is comparing two time points, such as before and after a policy change, or when a cohort cannot be maintained, such as a study in which some participants are expected to die. In contrast, a trajectory approach is most appropriate when the purpose of the research is to understand individuals' experiences over time or to understand longitudinal healthcare processes. Longitudinal qualitative research has the potential to be a powerful approach to understanding the complexities of health care: from relationships between providers and patients, to the experience of chronic disease, to the impact of health policy. Such research will be strengthened by careful consideration of the research question at hand, followed by application of the appropriate analytic approach.

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Mendeley readers

The data shown below were compiled from readership statistics for 250 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Unknown 249 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 25%
Student > Master 36 14%
Researcher 33 13%
Student > Doctoral Student 15 6%
Student > Bachelor 9 4%
Other 38 15%
Unknown 56 22%
Readers by discipline Count As %
Social Sciences 45 18%
Psychology 38 15%
Medicine and Dentistry 25 10%
Nursing and Health Professions 20 8%
Computer Science 8 3%
Other 43 17%
Unknown 71 28%