<div dir="ltr">Hi Claire,<br>
<br>a couple of comments on top of what Roger and Klinton already said:<br><br><br><div class="gmail_quote"><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
* I think it's more standard to calculate residual RTs by constructing<br>
subject-specific linear regressions rather than a mixed-effect linear<br>
regression pooling all the data. Also, you usually want to use *all*<br>
the regions (not just the critical region/regions) in constructing this<br>
regression; maybe throw out the first and last regions of the sentence.<br>
I can't tell whether you're doing this.</blockquote><div><br><ul><li>Claire was following a suggestion I made on my blog - see <a href="http://hlplab.wordpress.com/2008/01/23/modeling-self-paced-reading-data-effects-of-word-length-word-position-spill-over-etc/">http://hlplab.wordpress.com/2008/01/23/modeling-self-paced-reading-data-effects-of-word-length-word-position-spill-over-etc/</a> and linked posts, though I have changed things around a bit since then [update to come soon) (and I am using this in a paper that's almost finished). A mixed-effect regression with participants as random effect should be better than by-subject linear regression for the same reason why subject-differences generally are nicely accounted for by mixed-effect regressions. in a balanced design with about equally much data for each subject and, crucially, random slopes for all predictors (unlike what Claire currently has) the two approaches won't differ much anyway. for less balanced data, they will differ, and the mixed effect model should be better as it recognizes that the group-internal mean and SE are less reliable for small groups. Gelman & Hill 2007 have a nice discussion of this.<br>
</li></ul></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
<br>
* The problem with HPDinterval might be specific to the current state of<br>
lme4. What error do you get? Can you replicate it with a tiny toy<br>
dataset that you could post to the list?<br></blockquote><div><ul><li>HPDinterval is part of two packages, coda and lme4. i think
languageR loads coda, too, and in any case i ran into similar problems.
You have to specific which HPDinterval() you mean. i assume you want
lme4::HPDinterval().</li></ul></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">* This may incite controversy, but I personally would suggest being<br>
careful about residualizing and analyzing transformed RTs. The reason<br>
for this is that the transform changes the interpretation of the linear<br>
regression (used to calculate residuals) and of any interactions in your<br>
analysis.<br>
<br>
* Is this a designed & balanced experiment? If so, there shouldn't be<br>
problems with collinearity.</blockquote><div><br><ul><li>It's the covariate that introduces collinearity. but between the balanced variables there should be not collinearity <i>after centering</i> (which you seem to do). </li>
<li>Klinton is right though: centering (and other steps to reduce collinearity) should be done on the the data set that only contains exactly those cases that will go into the analysis!<br></li><li>In answer to your question, Claire, I would be worried about a fixed effect correlation of .5. I may be overly cautious, but so far i've always checked whether my results hold if all fixed effect correlations are reduced to < 0.3, often even < 0.1 (via centering, residualization or principal component analysis). Most people are <i>less </i>conservative, but i found cases, where even correlations of .3 can screw things up (especially in larger models with many small correlations). <br>
</li></ul></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">* You might consider having more random effects than intercepts in your<br>
mixed-effects regression. I believe this is an open issue.</blockquote><div><ul><li>I think Baayen et al in press describe pretty exactly how to make that decision. it's a matter of model comparison, just as for fixed effects. I suggest following their suggestion.</li>
</ul></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
* I'm not sure what criteria you want to use to exclude deviant<br>
participants. Could you explain in greater detail?</blockquote><div><ul><li>I've seen exclusion based on more than 2 to 3 absolute subject-internal SEs away from the subject's mean. I find it worrysome that there is so much variance between papers. personally, i think 2 SEs is often too tight. also, after having looked at lots of transforms for several data sets, I use log RTs from the beginning (and I exclude based on deviations in the log-transformed space).</li>
</ul><br><ul><li>finally, the last point: regression with residuals: looks good to me. I assume you're removing the collinearity between the interaction and the main effects and that's indeed how it's done =)<br>
</li></ul>Florian<br></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
<br>
Hope this helps.<br>
<br>
Best<br>
<br>
Roger<br>
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