Introduction to Path Analysis

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One way to “think about” path analysis is as a way of “sorting out” the colinearity patterns amongst the predictors – asking yourself what may be the “structure” -- temporal &/or causal relationships - - among these predictors that produces the pattern of colinearity. “Structure” of a MR model – with hypotheses about which predictors will contribute 1 A proposed structure for the colinearity among the predictors and how they relate to the criterion – with hypotheses about which paths will contribute 2 3 1 3 4 5 Crit earlier 5 2 4 Crit More recent distal cause proximal cause
Where do the path coefficients come from? One way is to run a series of multiple regressions… for each analysis: a variable with arrows pointing at it will be the criterion variable and each of the variables having arrows pointing to it will be the predictors 1. Crit = 3 Pred = 5 2. Crit = 1 Preds = 3 & 5 3. Crit = 4 Pred = 5 3 5 Crit 4 2 1 4. Crit = Crit Preds = 1, 2, 3 & 4 The path coefficients are the β weights from the respective regression analyses (remember that β = r for bivariate models)
Page 1: Introduction to Path Analysis • W
Page 5 and 6: Alternative ways to “think about
Page 7 and 8: …to distinguish “direct” and
Page 9 and 10: … to investigate “mediation eff
Page 11 and 12: Some caveats about the “when” o
Page 13 and 14: Path Analysis - allows us to look a
Page 15 and 16: The things to remember are that: 1.
Page 17 and 18: About non-recursive (bi-directional
Page 19 and 20: Mediation Analyses The basic mediat
Page 21 and 22: Testing Causal Models Theory Trimmi
Page 23 and 24: Testing Over-identified models Test

Introduction to Path Analysis

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