6 Vignettes

These vignettes have been written to demonstrate in more detail how common problems can be solved with LexOPS. Click on a vignette to find out more.

1) Higher Level Controls (control_for_map): The control_for() function works well when your variable is 1-dimensional, with a single value for each word, as it is for variables like Length, Frequency or Concreteness. Things become slightly trickier, however, when controlling for distance or similarity values, which can be calculated for each unique combination of words (i.e. \(n^2\) values). One easy solution is to use control_for_map() to pass a function which should be used to calculate the value between any two words. Simple examples for controlling for orthographic and phonological similarity are available in the package bookdown site. This vignette demonstrates how to build your own function for control_for_map(), which in this example controls for semantic similarity.

2) Using Data from Custom Sources: The built-in variables of LexOPS are useful but not exhaustive. Thankfully, LexOPS can work with any suitable list of features. For this example, we will join the Lancaster Sensorimotor norms to Engelthaler and Hills’ humour ratings, and the in-built LexOPS dataset (LexOPS::lexops). We can then use this to generate stimuli with a visual rating by humour interaction, controlling for length and frequency.

3) Creating Custom Levels: By default, LexOPS will split by a single variable for each use of split_by(), and will create items for each factorial cell. For instance, splitting by arousal into 2 levels, and emotional valence into 3 levels, would result in 6 factorial cells. But what if we want to generate items for just 2 of these 6 factorial cells? We can do this by creating a factor/character vector column in our data which will represent suitability for each factorial cell. This vignette provides an example, where we want to compare high arousal, negative emotional words to low arousal neutral words.

4) Euclidean Distance: Euclidean distance can be a useful way of matching by or controlling for multiple variables with greater flexibility than dealing with the variables individually. This vignette explains how weighted and unweighted Euclidean distance is calculated in LexOPS, gives example usage with euc_dists() and match_item(), and introduces the control_for_euc() function. The latter allows you to generate stimuli controlling for Euclidean distance to a match null within a Generate pipeline.

5) Applications to Participant Selection: LexOPS has potential applications in designing between-subject studies that control for participant variables. In this example, a randomised control trial is imagined, where subjects need to be matched for some relevant variables such as age, sex, BMI, and IQ. Given the pool of possible participants, LexOPS can be used to match subjects in the intervention and control conditions.