Category Archives: 16. Modelling – mixed-effects

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 mixed-effects modelling through reading about it in, I think, some paper or chapter by … Continue reading

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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

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Modelling – mixed effects – concepts

In the previous post, we ran through an extended example of a mixed-effects modelling analysis of the ML lexical decision data. We ended the post by getting p-values for the effects of the predictors included in our model. That was … Continue reading

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Modelling – extended example mixed-effects analysis

In the previous post, we ran through an example of a mixed-effects 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

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Modelling – example mixed-effects 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

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Modelling – look ahead to mixed-effects 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 mixed-effects analysis examining whether or how lexical decision responses were influenced by: … Continue reading

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