Six Approaches to Calculating Standardized Logistic Regression Coefficients

By S. Menard. 2004

290 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a6c89d546d7c56814d65322da04f9f63c9d7d8c3

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In Statistics and Probability

Citations in the corpus

Taking Stock of the Relationship Between Gang Membership and Offending (2016, methodology): “…coded by converting a t ratio to an r using the equation r t t n= + −2 2 , z values were converted by r z z n= +2 (Rosenthal, 1994; Vartanian, Schwartz, & Brownell, 2007), and methods for standardizing unstandardized coefficients were used when t or z values could not be computed (Menard, 2004).”
Bentham or Aristotle in the Development Process? An Empirical Investigation of Capabilities and Subjective Well-Being (2015, background): “Standardized coefficient estimates measure the relative influence in the statistical sense while variance decompositions allow us to discern the relative importance of a variable based on its share of the variance (Menard, 2004).”
Standards for Standardized Logistic Regression Coefficients (2011, methodology): “For a numerical comparison among different approaches to standardizing logistic regression coefficients, see Menard (2004).”
Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weight Analysis (2010, methodology): “…in OLS regression that incorporate the observed variability of the criterion when standardizing: b OLS ¼ ðbÞðsxÞ ðsyÞ: ð13Þ To overcome this limitation, Menard (2004) proposed a fully standardized logistic regression coefficient: b M ¼ ðbÞðsxÞðROÞ ðslogitðŷÞÞ; ð14Þ where b is the…”
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020, background): “If one is only interested in the rank order of the magnitude of the eﬀects of the independent variables on the utility, however, standardizing the independent variables (i.e., X -standardization) is suﬃcient (Menard, 2004).”
Meta‐analysis reveals the importance of matrix composition for animals in fragmented habitat (2011, methodology): “We calculated fully standardized logistic regression coefficients (Menard, 2004) because beta varies according to the scale at which an effect is measured (Menard, 2004), and predictors were measured on different scales (e.”
Competing Explanations of Undergraduate Noncompletion (2011, unknown): “We report standardized coefficients using the ‘‘variance-based’’ method (Menard, 2004).”