NCA-package: Necessary Condition Analysis

In NCA: Necessary Condition Analysis

Necessary Condition Analysis

Description

The NCA package implements Necessary Condition Analysis (NCA) as developed by Dul (2016). For running the NCA package a data file (e.g., mydata.csv, which contains the input data) must be available. An example data file (presented in above article) is included in the package. The user must load the data and call the nca function.

Details

Package:	NCA
Type:	Package
Version:	5.0.0
Date:	2026-03-20
License:	GPL (>= 3)

Author(s)

Author: Jan Dul [e-mail]
Maintainer: Govert Buijs [e-mail]

References

Dul, J. 2016. Necessary Condition Analysis (NCA).Logic and methodology of 'necessary but not sufficient' causality. Organizational Research Methods 19(1), 10-52. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1094428115584005")}
Dul, J. (2020). Conducting Necessary Condition Analysis. Sage publishers. ISBN: 9781526460141. https://uk.sagepub.com/en-gb/eur/conducting-necessary-condition-analysis-for-business-and-management-students/book262898
Dul, J., van der Laan, E., & Kuik, R. (2020). A statistical significance test for Necessary Condition Analysis. Organizational Research Methods, 23(2), 385-395. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1094428118795272")}

See Also

nca_analysis, nca_output

Examples

# A more detailed guide can be found here : https://repub.eur.nl/pub/78323/
# or https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981

# Load data from a CSV file with header and row names:
data <- read.csv('mydata.csv', row.names=1)
# Or load and rename the example dataset
data(nca.example)
data <- nca.example

# Run NCA with the dataset and name the analysis 'model'.
# Specify the conditions and outcomes by column index or name
# More than 1 condition can be specified with a vector
model <- nca_analysis(data, c(1, 2), 3)

# A quick summary of the analysis can be displayed by 'model'
model

# A full summary of the analysis is shown by nca_output (see documentation for more options)
nca_output(model)

# The results of the analysis is a list of 6 items :
# - plots (1 for each condition)
# - summaries (1 for each condition)
# - bottleneck tables (1 for each ceiling technique)
# - peers (1 dataframe for each condition)
# - tests (1 list for each condition)
# - test.time (total time to run all tests)
names(model)

# The first item contains the graphical outputs for each condition
# This is not really useful to humans
model$plots[[1]]

# The second item contains a list with the summaries for the conditions
# Extract individual elements with nca_extract
model$summaries[[1]]

# The third item contains a list with the bottleneck tables, one for each ceiling technique
model$bottlenecks$cr_fdh

# The fourth item shows the peers, for each condition
model$peers$Individualism

# For the fifth and sixth item, the test.rep needs to be larger than 0
# for performing the statistical test
# Optionally the p_confidence (default 0.95) and the p_threshold (default 0) can be set
model <- nca_analysis(data, c(1, 2), 3, test.rep=100)

# The fifth item shows the tests for each condition
# This is not really useful to humans
model$tests$Individualism

# The last item shows the total time needed to perform the analysis.
# For large values of test.rep the test may take long.
model$test.time

NCA documentation built on March 20, 2026, 5:08 p.m.