An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain.
10.4040/jkan.2013.43.2.154
- Author:
Hyeoun Ae PARK
1
Author Information
1. College of Nursing and System Biomedical Informatics National Core Research Center, Seoul National University, Seoul, Korea. hapark@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Logit function;
Maximum likelihood estimation;
Odds;
Odds ratio;
Wald test
- MeSH:
Humans;
Logistic Models;
*Models, Statistical;
Odds Ratio;
Publishing/standards;
*Research
- From:Journal of Korean Academy of Nursing
2013;43(2):154-164
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. METHODS: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. RESULTS: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. CONCLUSION: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.
