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<DIV dir=ltr align=left><FONT size=2 face=Arial><SPAN
class=436193109-30072009>My thanks to Andy, James, and Florian for their
responses to my question. The replies were, as always, prompt, helpful, and
lucid. I have a couple of quick further questions about model comparison: I
think all three replies included suggestions of doing likelihood ratio
tests to assess the significance of a single fixed factor in the model. How
reliable is this? As far as I can recall, Baayen in his book and in the JML
paper only uses this to evaluate random factors, and the paper by Bolker et al
that Andy cited recommends against it in the case of fixed factors. Are there
good alternatives? </SPAN></FONT></DIV>
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<DIV dir=ltr align=left><FONT size=2 face=Arial><SPAN
class=436193109-30072009>Finally, a quick follow up question regarding Florian's
six-step procedure, reproduced below. In step 5 you suggest I interpret the
coefficients in the full _or_ the reduced model. So is it acceptable to look at
the coefficients of a factor or an interaction even if the factor or interaction
does not "survive" a likelihood ratio test, i.e. does not significantly
contribute to the fit of the model?</SPAN></FONT></DIV>
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<DIV dir=ltr align=left><FONT size=2 face=Arial><SPAN class=436193109-30072009>I
hope that makes sense, thank you again for all the help!</SPAN></FONT></DIV>
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<DIV dir=ltr align=left><FONT size=2 face=Arial><SPAN
class=436193109-30072009>Jakke</SPAN></FONT></DIV><BR>
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<DIV class=gmail_quote><FONT size=2 face=Arial></FONT>
<DIV><BR>1) l <- lmer(logRT~A*B+(1+A*B|Subject)+(1+A*B| Item), data) <BR>2)
follow the procedure outline on our lab blog to figure out which random
effects you need: <A
href="http://hlplab.wordpress.com/2009/05/14/random-effect-should-i-stay-or-should-i-go/">http://hlplab.wordpress.com/2009/05/14/random-effect-should-i-stay-or-should-i-go/</A><BR>3)
take the resulting model and compare it against a model without the
interaction, using anova(l, l.woInteraction).<BR>4) <I>if removal of the
interaction is not significant</I>, you could further compare the model
against a model with only A (see above).<BR>5) Interpret coefficients in the
full model or in the reduced model (I would do the former unless I don't have
much data or cannot reduce collinearity, but you may prefer the latter).<BR>6)
If you find any of the scripts of references given above useful, cite/refer to
them, so that others can find them ;)<SPAN class=436193109-30072009><FONT
size=2 face=Arial> </FONT></SPAN><SPAN
class=436193109-30072009> </SPAN><SPAN
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