Hello,

I ran two lme analyses and got expected results. However, I saw

something suspicious regarding p-level for fixed effect. Models are the

same, only experimental designs differ and, of course, subjects. I am

aware that I could done nesting Subjects within Experiments, but it is

expected to have much slower RT (reaction time) in the second

experiment, since the task is more complex, so it would not make much

sense. That is why I kept analyses separated:

(A) lme(RT ~ F2 + MI, random =~ 1 | Subject, data = exp1)

ANOVA:

numDF denDF F-value p-value

(Intercept) 1 1379 243012.61 <.0001

F2 1 1379 47.55 <.0001

MI 1 1379 4.69 0.0305

Fixed effects: RT ~ F2 + MI

Value Std.Error DF t-value p-value

(Intercept) 6.430962 0.03843484 1379 167.32118 0.0000

F2 -0.028028 0.00445667 1379 -6.28896 0.0000

MI -0.004058 0.00187358 1379 -2.16612 0.0305

===========================================================

(B) lme(RT ~ F2 + MI, random =~ 1 | Subject, data = exp2)

ANOVA:

numDF denDF F-value p-value

(Intercept) 1 659 150170.71 <.0001

F2 1 659 17.28 <.0001

MI 1 659 13.43 3e-04

Fixed effects: RT ~ F2 + MI

Value Std.Error DF t-value p-value

(Intercept) 6.608252 0.05100954 659 129.54935 0.0000

F2 -0.008679 0.00616191 659 -1.40855 0.1594

MI 0.009476 0.00258605 659 3.66420 0.0003

As you can see, in exp1 p-levels for the model and for the fixed effects

are the same, as thay should be, as far as I know. Yet, in exp2 there is

significant p for F2 in the model, but insignificant regarding F2 as

fixed factor. How is it possible? I have ran many linear models and

those two values correspond (or are the same). Anyway, how can it be to

have insignificant effect that is significant in the model? Some strange

property of that factor, like distribution? Multicolinearity? Please,

help me on that.

Sincerely,

Petar

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