Ulf Jakobsson1. 1. Department of Nursing, Faculty of Medicine, Lund University, Lund, Sweden. ulf.jakobsson@omv.lu.se
Abstract
OBJECTIVES: The aim of this study was to review the presentation and analysis of ordinal data in three international nursing journals in 2003. METHOD: In total, 166 full-length articles from the 2003 editions of Cancer Nursing, Scandinavian Journal of Caring Sciences and Nursing Research were reviewed for their use of ordinal data. RESULTS: This review showed that ordinal scales were used in about a third of the articles. However, only about half of the articles that used ordinal data had appropriate data presentation and only about half of the analyses of the ordinal data were performed properly. CONCLUSIONS: Ordinal data are rather common in nursing research, but a large share of the studies do not present/analyse the result properly. Incorrect presentation and analysis of the data may lead to bias and reduced ability to detect statistical differences or effects, resulting in misleading information. This highlights the importance of knowledge about data level, and underlying assumptions for the statistical tests must be considered to ensure correct presentation and analyses of data.
OBJECTIVES: The aim of this study was to review the presentation and analysis of ordinal data in three international nursing journals in 2003. METHOD: In total, 166 full-length articles from the 2003 editions of Cancer Nursing, Scandinavian Journal of Caring Sciences and Nursing Research were reviewed for their use of ordinal data. RESULTS: This review showed that ordinal scales were used in about a third of the articles. However, only about half of the articles that used ordinal data had appropriate data presentation and only about half of the analyses of the ordinal data were performed properly. CONCLUSIONS: Ordinal data are rather common in nursing research, but a large share of the studies do not present/analyse the result properly. Incorrect presentation and analysis of the data may lead to bias and reduced ability to detect statistical differences or effects, resulting in misleading information. This highlights the importance of knowledge about data level, and underlying assumptions for the statistical tests must be considered to ensure correct presentation and analyses of data.
Authors: Eunice N Chomi; Phares G M Mujinja; Kristian Hansen; Angwara D Kiwara; Ulrika Enemark Journal: BMC Health Serv Res Date: 2015-03-15 Impact factor: 2.655
Authors: Cynthia Sau Ting Wu; Cathrine Fowler; Winsome Yuk Yin Lam; Ho Ting Wong; Charmaine Hei Man Wong; Alice Yuen Loke Journal: Ital J Pediatr Date: 2014-05-07 Impact factor: 2.638
Authors: Harriet Koorts; Jo Salmon; Anna Timperio; Kylie Ball; Susie Macfarlane; Samuel K Lai; Helen Brown; Stephanie E Chappel; Marina Lewis; Nicola D Ridgers Journal: J Med Internet Res Date: 2020-08-07 Impact factor: 5.428