An Introduction to Logistic Regression Analysis and Reporting

By C. Y. Peng, K. Lee, G. Ingersoll. 2002

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Citations in the corpus

Predicting Graduation (2015, methodology): “Predicting Persistence excluding First semester GPA Using the Statistical Package for the Social Sciences (SPSS), a binary hierarchical logistic regression (Peng et al., 2002) was conducted to test the hypothesis that mathematics/science self-efficacy would contribute a significant amount of varianc”
Using Multinomial Logistic Models To Predict Adolescent Behavioral Risk (2003, background): “According to Peng, Lee, and Ingersoll (2002), these indices are variations of the R2 concept defined for the ordinary least squares (OLS) regression model.”
Community College Online Course Retention and Final Grade: Predictability of Social Presence. (2009, methodology): “The pseudo R2 measured the success of the model in explaining the variations in the data (Peng et al., 2002).”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, background): “It is based on the central mathematical concept of the logit-natural logarithm (Peng et al., 2002).”
Wind‐throw mortality in the southern boreal forest: effects of species, diameter and stand age (2007, methodology): “We evaluated candidate models starting from the fully saturated model and considered all interactions using likelihood ratio tests (LRT) and Akaike’s information criterion (AIC) to remove terms (Agresti 1996; Peng et al. 2002; Quinn & Keogh 2002).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, background): “Moreover, there is no established rule to decide the minimum sample sizes that can be used for logistic regression (Peng et al. 2002).”
Mathematics performance and the role played by affective and background factors peter grootenboer and brian hemmings (2007, neutral): “According to Peng et al. (2002), an insignificant”