score_codes {novelqualcodes}

Score novel and duplicates codes across interviews

Description

'Novel' and 'duplicate' codes are scored once per interview; the number of times they are spoken in an interview does not matter.

The definition of whether a code is novel or duplicated is entirely chronological:

• A novel code is a topic/idea/concept that, for example, is mentioned in Interview 17, but was not mentioned in Interviews 1 through 16.

• A duplicate code is one that has been talked about in other interviews previously.

The cumulative sum of novel codes is used to visualise a stopping point for qualitative interviews.

Usage

score_codes(interviews)

Arguments

interviews

(List) A list of dataframes, as generated by import_coding_matrices().

Value

A dataframe, with one row per interview and these columns:

1. itvw_seq, the chronological order of interviews.

2. n_codes, the number of unique codes mentioned in this interview.

3. n_duplicate, how many of those codes are duplicates mentioned in previous interviews).

4. n_novel, how many of those codes are novel (mentioned for the first time in this interview).

5. prop_duplicate, the proportion of this interview's codes that are duplicates.

6. prop_novel, the proportion of this interview's codes that are novel.

7. cumsum_novel, the cumulative sum of novel codes over time (i.e. across interviews).

See Also

plot_novelty(), plot_richness()

Examples

# A folder of example coding matrices included with the package
path_to_matrices <- system.file("insect_study/matrices/", package = "novelqualcodes")
print(path_to_matrices)

# A list of files in that folder
list.files(path_to_matrices)

# Import them all at once
my_matrices <- import_coding_matrices(path_to_matrices)

# Score them for novel and duplicate codes
my_scores <- score_codes(my_matrices)

# Look inside the result; novel and duplicate codes are scored across
# all interviews.
print(my_scores)