## Modelling – next steps

Having dwelt on the relationships between pairs of variables, both in terms of scatterplot depictions and, lately, in terms of correlations, we can move on to the real core of our focus in analyzing psycholinguistic data: linear regression. Creative commons … Continue reading

## Getting started – revision of dataframe input, new on looking at data type

This post assumes that you have read and worked through examples in previous posts concerning: 1. how to read in .csv files as dataframes; 2. data structure basics, including discussion of vectors, matrices, and dataframes. We will be working with … Continue reading

## Modelling – examining correlations among predictors

In a previous post, we used merge() to create a joint database holding a large set of data on the attributes of the 160 words presented in a lexical decision experiment whose completion I supervised a while ago. I would … Continue reading

## Modelling – examination correlations: advanced scatterplots

This post will focus exclusively on the relationships between pairs of variables. We will be looking at the item norms data, though you might want to practice the R procedures we deploy on the subjects data (see earlier posts here, … Continue reading

## Modelling – examination correlations: combining dataframes

This post will build on the previous Modelling posts. We are continuing to focus on the item norms data, previously discussed here and here (where I talked about were the data are from), here and here (where I talked about examining the … Continue reading

## Modelling – examining correlations: first, look at predictor distributions

This post follows on from the previous post, concerning the examination of variable distributions. It is always useful to plot the distribution of variables like item attributes e.g. item length in letters. One important benefit of doing so is identifying … Continue reading

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## Modelling – examining correlations: first, look at predictor distributions

This post will take a more in-depth look at the relationships between variables. A key property of experimental data is that we measure attributes – characteristics of people,  stimuli, and behaviour – and that those measurements (the numbers) vary. Numbers … Continue reading

## Modelling – examining the correlations between predictor variables

This post will consider how to explore the relationship between a set of variables: variables that we will ultimately use in a mixed-effects analysis of lexical decision data. Most of the analyses we will be doing will examine whether observed … Continue reading