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Mean Centering Data in Linear Mixed Models
5 messages · AvianResearchDivision, Joshua Wiley, Chris Howden
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1 day later
As others have said which centring you use, if any, is based on how you want to interpret the parameters and the results you are after. A good example of when to do respondent centring is when you do segmentations on survey data. Some people naturally rate high while others rate low. If we don't do resp centring we almost always get a segmentation with high vs low rates. Which is of little interest. So in your example I suppose the question is what you want the grand mean ie intercept to represent if the predictor is 1) zero (no centring) 2) at its average (overall centring) 3) at that resp's average (resp centring) 4) at the group average (group averaging),... Although I'm a little unsure exactly how this would work and if there may be some unforeseen consequences. Chris Howden Founding Partner Tricky Solutions Tricky Solutions 4 Tricky Problems Evidence Based Strategic Development, IP Commercialisation and Innovation, Data Analysis, Modelling and Training (mobile) 0410 689 945 (fax / office) chris at trickysolutions.com.au Disclaimer: The information in this email and any attachments to it are confidential and may contain legally privileged information. If you are not the named or intended recipient, please delete this communication and contact us immediately. Please note you are not authorised to copy, use or disclose this communication or any attachments without our consent. Although this email has been checked by anti-virus software, there is a risk that email messages may be corrupted or infected by viruses or other interferences. No responsibility is accepted for such interference. Unless expressly stated, the views of the writer are not those of the company. Tricky Solutions always does our best to provide accurate forecasts and analyses based on the data supplied, however it is possible that some important predictors were not included in the data sent to us. Information provided by us should not be solely relied upon when making decisions and clients should use their own judgement.
On 24/08/2013, at 12:12, Joshua Wiley <jwiley.psych at gmail.com> wrote:
Hi, Mean centering only changes the interpretation of the intercept; group mean centering changes the interpretation of the intercept and the slope coefficient. Which is appropriate, if either, is more of a substantive and interpretational issue. To discuss issues like these, I would suggest seeking the advice of a local statistician or a statistical consultant. Cheers, Josh On Fri, Aug 23, 2013 at 6:43 PM, AvianResearchDivision <segerfan83 at gmail.com
wrote:
Hi, Thank you for the response. I guess my question still is unanswered to a certain degree. I can see now that mean centering changes the interpretation of the intercept and doing this is probably up to whomever is performing the analysis. In my situation, interpreting an intercept at 0 dB ambient noise doesn't make much sense because that value probably doesn't exist anywhere. However, with my particular situation where I have a fixed effect that has 12 levels with all 59 individuals divided up unevenly within those levels, is grand mean centering the way to go or is group mean centering? I suppose I can't decide because I can't imagine a scenario where group mean centering would be warranted. With either group mean or grand mean, all that is being done is subtracting a mean from a value, correct? I've read some papers (Kontiainen et al. 2009) where they did this and then divided the result by the overall standard deviation of that variable. Thank you! On Fri, Aug 23, 2013 at 9:13 PM, Joshua Wiley <jwiley.psych at gmail.com>wrote:
Hi,
Mean centering will only change the interpretation of the intercept, and
could be done easily using (for example):
scale(mtcars$mpg, scale=FALSE)
Group mean centering, so that each individual has mean 0 changes things
as this is no longer just adding a constant. A higher score then is
relative to each individuals' mean, not to an absolute value.
This can be accomplished using:
mtcars <- within(mtcars, {
gmmpg <- ave(mpg, cyl, FUN = function(x) x - mean(x))
})
Cheers,
Joshua
On Fri, Aug 23, 2013 at 5:43 PM, AvianResearchDivision <
segerfan83 at gmail.com> wrote:
Hi all,
I have a data set that includes 1 predictor variable (ambient noise) that
is continuous and 2 predictor variables that are factors. One of the
predictor variables has 12 levels, that has 59 individuals (random
effect)
grouped within (unevenly). The question is, should I consider mean
centering my continuous predictor variable? I know that interpretation
of
the model results means that the intercept and slope are based on ambient
noise of 0 dB. If I do want to mean center my data, is there a specific
way I should do this because of the predictor variable that has
individuals
grouped within? Thank you for your help.
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