Understanding the Results of Multiple Linear Regression

By Kim F. Nimon, F. Oswald. 2013

358 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ab840b5470bda8d6cfdadb8643248f7d7410ea34

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Citations in the corpus

Who Is Impacted? Personality Predicts Individual Differences in Psychological Consequences of the COVID-19 Pandemic in Germany (2020, methodology): “Commonality analyses (e.g., Nimon & Oswald, 2013) would stringently address the partialization issue but are unfeasible with many predictors.”
The role of textural statistics vs. outer contours in deep CNN and neural responses to objects (2018, methodology): “…RDMs were then constructed by computing the correlation distance between activation vectors for each set, allowing direct comparison with neural RDMs. Shared variance analyses Following Bonner & Epstein (2018), we combined the standard RSA approach with commonality analysis (Nimon & Oswald, 2013).”
Illuminating the ‘Face’ of Justice: A Meta‐Analytic Examination of Leadership and Organizational Justice (2018, neutral): “(A variety of software packages – e.g., the ‘yhat’ package in R: https://cran.r-project.org/web/packages/yhat/ index.html – are available to conduct a DA; see Nimon and Oswald, 2013.)”
Psychological Ownership: A Meta-Analysis and Comparison of Multiple Forms of Attachment in the Workplace (2020, methodology): “…predictor’s incremental validity across all possible models that contain some subset of the other predictors and using the incremental validity coefficients to evaluate the following three levels of dominance: complete dominance, conditional dominance, and general dominance (Nimon & Oswald, 2013).”
Statistical control in correlational studies: 10 essential recommendations for organizational researchers. (2016, background): “These changes in meaning occur owing to partialling, the mathematical partitioning of variance explained by a set of predictor variables (Nimon & Oswald, 2013; Pedhazur, 1982).”