Visualization of Multidimensional Data in Nursing Science

Abstract

Nursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.

Uncovering Family Treatment Decision-Making Processes: The Value and Application of Case Study Methods to Family Research

“…Researchers have shared strategies for analyzing family-level data, such as conducting within- and across-case analysis in phenomenological family studies (Ayres et al, 2003), and case summaries, matrices, and other visual methods of analyzing large amounts of data from various categories of participants, including parents, physicians, nurses, and social workers (Docherty et al, 2017; K. A. Knafl & Ayres, 1996).…”

Positive psychological states and stress responses in caregivers of adults receiving an allogeneic bone marrow transplant: A study protocol

“…The multiple data types (surveys and biological indicators) will be plotted across time (days since transplant) and examined for trends. Plotting will be done by using a case‐oriented visual analysis to describe trends in the person reported data over the 8–12 weeks post‐transplant informed by qualitative themes by graphically assembling the multiple types of quantitative data along a timeline and then visually searching for patterns (Docherty et al., 2016). Themes from the interview data will provide contextual grounding for the cases and insights into factors influencing caregiver stress, psychological states and QOL.…”

Ways of knowing in precision health

“…Placement of the data in juxtaposition to each other allows data comparison, contrast, or extension in relation to each other (Sandelowski, 2014). In the conversion type of mix, sources of data are collected, but then are integrated prior to analysis through conversion or assimilated of the data sources into a new data-type (e.g., Britt & Evans, 2007;Caiola, Barroso, & Docherty, 2017;Docherty, Vorderstrasse, Brandon, & Johnson, 2017).…”