surreyj wrote

Now all the other demand literature reports the %/proportion of variance accounted for (or R squared) as well as the parameter values and standard error.

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I had a chat to a supervisor and he suggested I post to here and see if someone can give me a reference/references backing up why I shouldn't use r-squared.

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For references to the printed literature , see

http://markmail.org/message/qoup5oerbxchejmyI sympathize with the point you mention in a more general context. There many "against-all-reviewers" wisdoms in this community

-- F-test in ANOVA (early years)

-- Exegesis/Venables

-- "Nesting" (most helpful sentence by D Bates: "I never understood this")

-- p-values (most lately brought up by lmer's refusal to produce these)

-- r-squared for nonlinear

that will prevail in the long run, but we (oldies) make students or colleagues in applied fields suffer by not producing these. It is easy for FoxBatsRipley to tell statistical reviewers that they are wrong, but what about surreyi's master thesis against the sheer mass of papers with nonlinear R^2 and "more-p-better-paper" reviewers? Or Frank Harrell against a Mayo Clinics Medical professor? (Sorry, Frank, I made up the example)

Surprisingly, it's mostly the not-so-top papers that cause problems here. When Lancet, New English or BMJ reject some statistical argument, they have good reasons.

Dieter