In npm27/lrd: A Package for Processing Lexical Response Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Libraries and Data

Please see manuscript for a long description of the following data. We will load the example data, and you can use the ? with the dataset name to learn more about the data.

library(lrd)
data("sentence_data")
head(sentence_data)
#?sentence_data

Data Cleanup

Scoring in lrd is case sensitive, so we will use tolower() to lower case all correct answers and participant answers.

sentence_data$Sentence <- tolower(sentence_data$Sentence)
sentence_data$Response <- tolower(sentence_data$Response)

Score the Data

You should define the following:

• data = dataframe of participant responses
• responses = column name of the participant answers
• key = column name of the answer key
• key.trial = column name of the trial id code
• id = column name of the participant id number
• id.trial = column name of the trial id within the participant data
• cutoff = the Levenshtein distance value you want to use for scoring (0 no changes exactly the same, higher numbers allow more variance in the word)
• flag = calculate z scores for outliers (TRUE/FALSE)
• group.by = column name(s) for grouping variables
• token.split = a value to split the tokens (i.e., words) in the sentence

Note that the answer key can be in a separate dataframe, use something like answer_key$answer for the key argument and answer_key$id_num for the trial number. Fill in answer_key with your dataframe name and the column name for those columns after the $.

sentence_ouptut <- 
  prop_correct_sentence(data = sentence_data,
                        responses = "Response",
                        key = "Sentence",
                        key.trial = "Trial.ID",
                        id = "Sub.ID",
                        id.trial = "Trial.ID",
                        cutoff = 1,
                        flag = TRUE,
                        group.by = "Condition",
                        token.split = " ")

str(sentence_ouptut)

Output

We can use DF_Scored to see the original dataframe with our new scoring columns - also to check if our answer key and participant answers matched up correctly! First, each sentence is stripped of punctuation and extra white space within the function. The total number of tokens, as split by token.split are tallied for calculating Proportion.Match. Then, the tokens are matched using the Levenshtein distance indicated in cutoff, as with the cued and free recall functions. The key difference in this function is how each type of token is handled. The Shared.Items column includes all the items that were matched completely with the original answer (i.e., a cutoff of 0). The tokens not matched in the participant answer are then compared to the tokens not matched from the answer key to create the Corrected.Items column. This column indicates answers that were misspelled but within the cutoff score and were matched to the answer key (i.e., "th" for the, "ths" for this). The non-matched items are then separated into Omitted.Items (i.e., items in the answer key not found in the participant answer), and Extra.Items (i.e., items found in the participant answer that were not found in the answer key). The Proportion.Match is calculated by summing the number of tokens matched in Shared.Items and Corrected.Items and dividing by the total number of tokens in the answer key. The DF_Participant can be used to view a participant level summary of the data. Last, if a grouping variable is used, we can use DF_Group to see that output.

#Overall
sentence_ouptut$DF_Scored

#Participant
sentence_ouptut$DF_Participant

#Groups
sentence_ouptut$DF_Group

npm27/lrd documentation built on Dec. 11, 2021, 9:26 p.m.