Exemplars of mixed methods data combination and analysis

Nurs Res. 2006 Mar-Apr;55(2 Suppl):S43-9. doi: 10.1097/00006199-200603001-00008.


Background: Mixed methods research approaches can be applied to nursing and healthcare. Multiple perspectives and different types of data (e.g., social-behavioral data, numerical outcome measures, or clinical variables) often are needed to examine complex clinical problems and health behaviors fully. Although qualitative and quantitative methods are recognized widely as complementary for studying and explaining human phenomena, methodological techniques for combining and analyzing mixed methods data have received less attention.

Objectives: To describe techniques for mixed methods data combination and analyses using three different design approaches.

Methods: Data combination and analysis techniques are presented using the following approaches: (a) mixed methods event analysis, (b) concurrent-mixed analysis for complementarity and completeness, and (c) concurrent nested analysis to provide a broader understanding of phenomena and enrich the description of participants.

Results: Research exemplars from topical areas such as weaning from long-term mechanical ventilation, medication-taking among community-dwelling persons with dementia, health control beliefs after lung transplantation, and recovery from subarachnoid hemorrhage are presented. Simple and complex matrix construction and a variety of graphical displays are used to illustrate data combination and analysis techniques for mixed methods research.

Discussion: The techniques for mixed methods data combination and analysis presented have the potential to advance the use and refinement of mixed methods research, thereby expanding the repertoire of methodologies to study complex phenomena of interest to nurses.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Data Interpretation, Statistical*
  • Humans
  • Nursing Research / statistics & numerical data*
  • Qualitative Research