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Data creation

Dataset requirements

The data you create should have:

-    A continuous outcome variable

-    Two predictor variables: one categorical and the other either categorical or continuous

-    Any other variables that you would record with the experiment and methods you propose

For example, let’s say you proposed a lexical decision task to determine whether listeners respond faster to words and nonwords with a simple onset (a consonant followed by a vowel) versus a complex onset (two or more consonants followed by a vowel). You recorded participant responses and response times. You controlled for the number of syllables in the word. You had 48 trials and ran 20 participants. You might have the following variables:

-     participant – a number from 1 to 20

-    trial – a number from 1 to 48

-     stimulus – the word that listeners heard on that trial

-     real.word –a categorical variable with the levels “yes” and “no”

-    onset – a categorical variable with the levels “simple” and “complex”

-     num.syl – the number of syllables from 1 to 3

-     response – a categorical variable with the levels “yes” and “no”

-     RT – the reaction time of the response (measured in seconds)

With that example, the first few rows of the data might look like this:

Two datasets

You need to create two different datasets. It should be like you ran the same experiment twice and got two different results. Continuing the example above, one dataset might show that simple onsets are processed faster across the board, and the other dataset shows that simple onsets are faster for real words but complex onsets are faster for nonwords.

Save the datasets as separate .csv files. I recommend fully creating one dataset first. It will likely be easiest to use that complete dataset as a starting point for your second dataset.

You’ll also turn in the R file that creates your data. It should include relevant count() and summarise() queries on your two datasets to show the difference that you created.