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Recent Posts
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Category Archives: 16. Modelling – mixedeffects
Modelling – mixed effects – concepts
I am going to take another shot at considering mixed effects modelling. This time from a perspective closer to my starting point. I first learnt about mixedeffects modelling through reading about it in, I think, some paper or chapter by … Continue reading
Posted in 16. Modelling  mixedeffects
Tagged anova(), F', lmer(), minF', mixed effects, mixede, regression
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Mixed effects – continuing the extended example
As noted in a previous post, our mixed effects analysis of the ML lexical decision data suggested that RTs were influenced by significant effects due to: cAge + cART + cTOWRENW + cLength + cOLD + cBG_Mean + cLgSUBTLCD + … Continue reading
Modelling – mixed effects – concepts
In the previous post, we ran through an extended example of a mixedeffects modelling analysis of the ML lexical decision data. We ended the post by getting pvalues for the effects of the predictors included in our model. That was … Continue reading
Posted in 16. Modelling  mixedeffects
Tagged hierarchical, mixed effects, multilevel
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Modelling – extended example mixedeffects analysis
In the previous post, we ran through an example of a mixedeffects analysis completed using the lmer() function from the lme4 package (Bates, 2005; Bates, Maelchler & Bolker, 2013). We will not, yet, really fulfill the promise to develop our … Continue reading
Posted in 16. Modelling  mixedeffects
Tagged AIC, anova(), BIC, chisq, fixed effects, lmer(), MCMC, random effects
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Modelling – example mixedeffects analysis
In the last post, I showed how to collate the various data bases we constructed or collated from the data collection achieved in a study of lexical decision. We ended the post by producing a .csv file of the output … Continue reading
Modelling – look ahead to mixedeffects modelling
So far, we have been looking at the participant attributes or the item attributes for our lexical decision study. It is time to move on to consider a mixedeffects analysis examining whether or how lexical decision responses were influenced by: … Continue reading
Posted in 16. Modelling  mixedeffects
Tagged ddply(), length(), levels(), merge, summary
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