The purpose of this article is to provide researchers, editors, and readers with a set of guidelines for what to expect in an article using logistic regression techniques. Tables, figures, and charts that should be included to comprehensively assess the results and assumptions to be verified are discussed. This article demonstrates the preferred pattern for the application of logistic methods with an illustration of logistic regression applied to a data set in testing a research hypothesis. Recommendations are also offered for appropriate reporting formats of logistic regression results and the minimum observation-to-predictor ratio. The authors evaluated the use and interpretation of logistic regression presented in 8 articles published in The Journal of Educational Research between 1990 and 2000. They found that all 8 studies met or exceeded recommended criteria.

An Introduction to Logistic Regression Analysis and Reporting

Relations between normative beliefs and initiation intentions toward cigarette, alcohol and marijuana.

Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area

/pdf/using-multinomial-logistic-models-to-predict-adolescent-1ao5dhq1jm.pdf

Using Multinomial Logistic Models To Predict Adolescent Behavioral Risk

Although the algorithm for generating a best-subsets variable-selection routine in logistic regression was presented more than a decade ago, a search of the literature reveals that the technique is seldom applied. A possible explanation is its omission from popular statistical computing packages. Newer versions of the Statistical Analysis System (SAS) provide limited capabilities, and the Statistical Package for the Social Sciences (SPSS) does not allow for its calculation. Using a sample data set, this article briefly describes in nonmathematical terms how to implement and interpret results from a best-subsets logistic regression using SAS.

Running a Best-Subsets Logistic Regression: An Alternative to Stepwise Methods.

OBJECTIVE: To demonstrate the use of logistic regression in health care research METHOD: Forward and backward stepwise logistic regression algorithms were systematically applied to a real-world data set comprising 301 cancer patients and a set of explanatory variables RESULTS: Four variables were identified as effective predictors of pain reporting by cancer patients during chemotherapy: fatigue, depression, severity of colds or viral infections, and insomnia The 4-predictor model was validated by (a) significance tests of regression coefficients at p<005, (b) significant improvement of this model over competing models, and (c) goodness of fit indices CONCLUSIONS: Logistic regression is useful for health-related research in which outcomes of interest are often categorical

Barbara D. Manz

Papers

Modeling categorical variables by logistic regression.