agree_tab: Agreement for multiple items

In volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

Agreement for multiple items

Description

Two types of comparing categories are provided:

Usage

agree_tab(
  data,
  cols,
  coders,
  ids = NULL,
  category = NULL,
  method = "reliability",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

data	A tibble containing item measures, coders and case IDs.
cols	A tidy selection of item variables (e.g. starts_with...) with ratings.
coders	The column holding coders or methods to compare.
ids	The column with case IDs.
category	For classification performance indicators, if no category is provided, macro statistics are returned (along with the number of categories in the output). Provide a category to get the statistics for this category only. If values are boolean (TRUE / FALSE) and no category is provided, the category is always assumed to be "TRUE".
method	The output metrics, one of reliability or classification. You can abbreviate it, e.g. reli or class.
labels	If TRUE (default) extracts labels from the attributes, see codebook.
clean	Prepare data by data_clean.
...	Placeholder to allow calling the method with unused parameters from report_counts.

Details

• Reliability: Compare codings of two or more raters in content analysis. Common reliability measures are percent agreement (also known as Holsti), Fleiss' or Cohen's Kappa, Krippendorff's Alpha and Gwets AC.

• Classification: Compare true and predicted categories from classification methods. Common performance metrics include accuracy, precision, recall and F1.

Value

A volker tibble with one row for each item. The item name is returned in the first column. For the reliability method, the following columns are returned:

• n: Number of cases (each case id is only counted once).

• Coders: Number of coders.

• Categories: Number of categories.

• Holsti: Percent agreement (same as accuracy).

• Krippendorff' Alpha: Chance-corrected reliability score.

• Kappa: Depending on the number of coders either Cohen's Kappa (two coders) or Fleiss' Kappa (more coders).

• Gwet's AC1: Gwet's agreement coefficient.

For the classification method, the following columns are returned:

• n: Number of cases (each case id is only counted once)

• Categories: Number of categories

• Accuracy: Share of correct classifications.

• Precision: Share of true cases in all detected true cases.

• Recall: Share of true cases detected from all true cases.

• F1: Harmonic mean of precision and recall.

Examples

library(dplyr)
library(volker)

data <- volker::chatgpt

# Prepare example data.
# First, recode "x" to TRUE/FALSE for the first coder's sample.
data_coder1 <- data |>
  mutate(across(starts_with("cg_act_"), ~ ifelse(is.na(.), FALSE, TRUE))) %>%
  mutate(coder = "coder one")

# Second, recode using a dictionary approach for the second coder's sample.
data_coder2 <- data |>
  mutate(across(starts_with("cg_act_"), ~ ifelse(is.na(.), FALSE, TRUE))) %>%
  mutate(cg_act_write = grepl("write|text|translate", tolower(cg_activities))) %>%
  mutate(coder="coder two")

data_coded <- bind_rows(
  data_coder1,
  data_coder2
)

# Reliability coefficients are strictly only appropriate for manual codings
agree_tab(data_coded, cg_act_write,  coder, case, method = "reli")

# Better use classification performance indicators to compare the
# dictionary approach with human coding
agree_tab(data_coded, cg_act_write,  coder, case, method = "class")

volker documentation built on Nov. 5, 2025, 5:21 p.m.