Background: When a construct such as patients’ “transition to self-management” of chronic illness is studied by researchers across multiple disciplines, the meaning of key terms can become confused. This results from inherent problems in language where a term can have multiple meanings (polysemy) and different words can mean the same thing (synonymy).
Objectives: The aim of this study was to test a novel quantitative method for clarifying the meaning of constructs by examining the similarity of published contexts in which they are used.
Methods: Published terms related to the concept transition to self-management of chronic illness were analyzed using the internomological network (INN), a type of latent semantic analysis performed to calculate the mathematical relationships between constructs based on the contexts in which researchers use each term. This novel approach was tested by comparing results with those from concept analysis, a best-practice qualitative approach to clarifying meanings of terms. By comparing results of the 2 methods, the best synonyms of transition to self-management, as well as key antecedent, attribute, and consequence terms, were identified.
Results: Results from INN analysis were consistent with those from concept analysis. The potential synonyms self-management, transition, and adaptation had the greatest utility. Adaptation was the clearest overall synonym but had lower cross-disciplinary use. The terms coping and readiness had more circumscribed meanings. The INN analysis confirmed key features of transition to self-management and suggested related concepts not found by the previous review.
Discussion: The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries and may help to integrate the diverse literature on self-management of chronic illness.
Paul F. Cook, PhD, Assistant Professor, College of Nursing, University of Colorado, Aurora.
Kai R. Larsen, PhD, Associate Professor, Leeds School of Business, University of Colorado Boulder.
Teresa J. Sakraida, PhD, RN, Assistant Professor; and Leli Pedro, DNSc, RN, OCN, CNE, Assistant Professor, College of Nursing, University of Colorado, Aurora.
Editor’s note This article is part of the focus on statistics in nursing research.
Accepted for publication January 13, 2012.
Thank you to Julia I. Lane and Randy Ross for their support of this work. Thank you to Dr. Karen Peifer, who collaborated on the Clinical and Translational Science Institute (CTSI) novel methods grant and subsequent grant submissions and contributed to the development of the INN analysis strategy; Dr. Jintae Lee, who collaborated on the CTSI novel methods grant and is a coprincipal investigator on the National Science Foundation grant; and Zoya A. Voronovich, who collaborated on the CTSI novel methods grant.
This methodological research was supported by a Novel Methods pilot grant from the Colorado Clinical and Translational Science Institute National Institutes of Health Grant 1UL1RR025780-01 and by National Science Foundation Grant 0965338.
Dr. Cook is receiving a grant from Merck & Co. and has received other support in the past 36 months from various public-sector funders. Dr. Larsen has no conflicts to declare. Dr. Sakraida is receiving other grant support from the Robert Wood Johnson Foundation. Dr. Pedro is receiving other grant support from the National Institute of Nursing Research.
Corresponding author: Paul F. Cook, PhD, College of Nursing, University of Colorado, 13120 E 19th Ave, Campus Box C288-04, Aurora, CO 80045 (e-mail: firstname.lastname@example.org).