Author Archives: robayedavies

Getting started — loading data, writing and saving scripts

[16/1/14 edit — as the procedure for finding data in Mac OS were detailed in class, I am going to leave off editing this post to show Mac OS steps in detail because: steps are mostly the same; and we’ve … Continue reading

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Getting started – getting helped

As we get started, we will need help, let’s work on how and where to find that help, and also how to act on it. We will need help in various ways when using R You need help most of … Continue reading

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Getting started — Installation of RStudio and some packages + using ggplot() to make a simple plot

Install R Revise how to install R, as previously discussed here and here. Installation How do you download and install R? Google “CRAN” and click on the download link, then follow the instructions (e.g. at “install R for the first time”). … Continue reading

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Postgraduate data analysis and interpretation

I am revising my previous posts on the whole process, from getting started with R to using it to do linear modelling and linear mixed-effects modelling. Sunset over the coast, on the road from Ravenglass to Broughton What this revision … Continue reading

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Mixed-effects modeling — four hour workshop — part IV: LMEs

For an example analysis, we collated a .csv file of data from a repeated measures lexical decision experiment, which can be downloaded here. The code I have used in running the analysis described in this post can be found here. … Continue reading

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Mixed-effects modeling — four hour workshop — part III: Regression modeling

We completed a study on the factors that influence visual word recognition performance in lexical decision. We then put together a dataset for analysis here. We examined the distribution of inter-relation of critical variables in that dataset, acting to transform … Continue reading

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Mixed-effects modeling — four hour workshop — part II: Data Exploration

We got our data together in Part I of this workshop, eventually arriving at the collation of a single dataframe in ‘long format’ holding all our data on subject attributes, item attributes and behavioural responses to words in a lexical … Continue reading

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