Gifted Child Quarterly 57(3) 197–204 © 2013 National Association for Gifted Children Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0016986213490022 gcq.sagepub.com Methodological Brief In gifted education research, logistic regression analysis (also known as logit analysis) has been used to analyze binary outcomes. 1 For example, logistic regression has been used to identify factors (e.g., family, school, environment, and individual) associated with high-achieving and under- achieving gifted students (Baker, Bridger, & Evans, 1998; McCoach & Siegle, 2003), investigate variables related with gifted students dropping out (Renzulli & Park, 2000), and describe the phenomenon of bullying among gifted students (Peterson & Ray, 2006). Binary outcome variables (also known as Bernoulli variables) represent two categories indi- cating that an event has occurred (e.g., 1 = student dropped out, 0 = student did not drop out) or that a characteristic is present (e.g., 1 = high achieving, 0 = underachieving). Predicting giftedness (1 = yes, 0 = no), though we realize that giftedness can be measured on a continuum of abilities, has been modeled using logistic regression as well (Konstantopoulos, Modi, & Hedges, 2001). However, unlike linear regression results, interpreting and presenting logistic regression coefficients may be more challenging for applied researchers (Liberman, 2005; Long, 1997). The issues with using ordinary least squares (OLS) regres- sion with binary outcomes have been well researched (Long, 1997). While decades ago, using OLS to model the relation- ship of predictors with binary outcome variables was accept- able, it is considered unacceptable today by most researchers (Allison, 1999). Using traditional linear regression to model binary outcomes affects the model parameter estimates and standard errors as a result of linear regression model viola- tions (Long, 1997). Using traditional OLS regression may also result in impossible or nonsensical predicted values that are greater than one or less than zero. This Methodological Brief presents an overview of logistic regression (LR), pro- vides an example of interpreting logistic regression results, and closes by discussing multilevel extensions. Data Source and Measures Our data came from the National Education Longitudinal Study of 1988 (NELS:88; Institute of Education Sciences, U.S. Department of Education, http://nces.ed.gov/surveys/ nels88/). We limited our analysis to public school 10th-grade students (n = 4,832) with nonmissing variables of interest. As our analysis is for pedagogical purposes only, we did not use weights (see Hahs-Vaughn, 2005, for a primer on using weights with national datasets) or account for the nested data structure (i.e., students within schools). Our dependent vari- able (DV) was if a student had ever been in an Advanced Placement (AP) course (F1S34E; 1 = yes, 0 = no). Our inde- pendent variables (IVs) of interest were the following: if a student had been enrolled in a gifted and talented class in the eighth grade (BYS68A), gender (1 = female, 0 = male), and socioeconomic status (SES). SES is an NELS developed composite (M = -0.09, SD = 0.75, min = -2.23, max = 1.91) created using parents’ level of education, occupation, and household income. The specific research question of interest 490022GCQ XX X 10.1177/0016986213490022Gifted Child QuarterlyHuang and Moon research-article 2013 1 University of Virginia, Charlottesville, VA, USA Corresponding Author: Francis L. Huang, Curry School of Education, University of Virginia, PO Box 800785, Charlottesville, VA 22908-8785, USA. Email: flhuang2000@yahoo.com What Are the Odds of That? A Primer on Understanding Logistic Regression Francis L. Huang 1 and Tonya R. Moon 1 Abstract The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a gifted class, gender, and socioeconomic status) that were associated with taking an Advanced Placement course in the 10th grade. Through the use of the NELS:88 data, the authors provide an example of how to interpret both categorical and continuous independent variables and illustrate how model fit can be assessed. Keywords quantitative methodologies, policy/policy analysis, assessment by guest on April 21, 2016 gcq.sagepub.com Downloaded from