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PhD Thesis: Addressing the Inferential Limitations of Regression Models by…
PhD Thesis: Addressing the Inferential Limitations of Regression Models by using Path Analytic Models with Mediated Associations
Write three papers exploring and demonstrating that using regression for inference where even weak/moderate collinearity is present is flawed.
Demonstrate and discuss how we can make more accurate and robust estimates of model parameters by addressing problems associated with collinearity between variables by taking into account bidirectional effects of associated predictor variables using path analytic models with mediated associations.
Paper 1
Key Messages:
Using regression models for inference in the presence of even weak or moderate collinearity is flawed.
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As collinearity between predictors increases, the accuracy of Parameter Estimates diverge from true values
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Moderation vs Mediation
Moderation is well known and is often referred to as interaction effects. Moderation implies fitting additional terms to the model. Mediation does not require fitting additional terms.
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Addressing the Inferential Limitations of Regression Models by using Path Analytic Models with Mediated Associations
A Critical Examination of the Inferential Limitations of Regression Models and using Path Analytic Models with Mediated Associations to Address Them
I feel like the word "COLLINEARITY" needs to be in here, but not sure where
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Paper 3
Multilevel SEM
What are the effects of collinearity when both numerical and categorical variables are included in a path model?
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