Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

Autor/in	Osborne, Jason W.
Titel	Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results
Quelle	In: Practical Assessment, Research & Evaluation, 17 (2012) 11, (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1531-7714
Schlagwörter	Regression (Statistics); Prediction; Mathematics; Probability; Mathematical Concepts; Graphs; Dropouts; Longitudinal Studies + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Vorhersage; Mathematik; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Grafische Darstellung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These outcomes represent important social science lines of research: retention in, or dropout from school, using illicit drugs, underage alcohol consumption, antisocial behavior, purchasing decisions, voting patterns, risky behavior, and so on. The goal of this paper is to briefly lead the reader through the surprisingly simple mathematics that underpins logistic regression: probabilities, odds, odds ratios, and logits. Anyone with spreadsheet software or a scientific calculator can follow along, and in turn, this knowledge can be used to make much more interesting, clear, and accurate presentations of results (especially to non-technical audiences). In particular, I will share an example of an interaction in logistic regression, how it was originally graphed, and how the graph was made substantially more user-friendly by converting the original metric (logits) to a more readily interpretable metric (probability) through three simple steps. (Contains 4 figures, 5 tables and 6 footnotes.) (As Provided).
Anmerkungen	Dr. Lawrence M. Rudner. e-mail: editor@pareonline.net; Web site: http://pareonline.net
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10