![]() ![]() What these numbers and these histograms show us is that participants in the sample varied broadly (from 20 years to 60+ years) in age, though most of the middle-aged people in the sample were male, also that measures of reading ability – TOWRE word and non-word naming accuracy – tended to vary around the top end of the range. using the ggplot code discussed previously to get some histograms using the function call: describe(subjects) to get the mean and standard deviation etc. In previous posts, we have seen how to use R to get a sense of the average for and spread of values on these measures for our sample, and we have seen how to show the distribution of values using histograms: What does the observation that participants varied mean? We know that the participants tested in the ML study of reading varied on measures of gender, age, reading skill (we used the TOWRE test of ability, Torgesen, Wagner & Rashotte, 1999) and print exposure, a proxy for reading history (measured using the ART, Masterson & Hayes, 2007 Stanovich & West, 1989). Variation in the values of subject scores ![]() While doing these things – mostly achieving the presentation of relationships between variables – we should also consider what statistical insights the plots teach us.ġ. ![]() modifying the appearance of the scatterplots for presentation. examining the relationship between pairs of variables using scatterplots 2. This post assumes that you have installed and are able to load the ggplot2 package, that you have been able to download the ML subject scores database and can read it in to have it available as a dataframe in the workspace, and that you have already tried out some plotting using ggplot2. ![]()
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