d.performance

This dataset is from Greckhammer et al. (2008), who analyze the causal conditions for superior (above average) business-unit performance of corporations in the manufacturing sector during the years 1995 to 1998.

datasets

Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.

Mathias Ambuehl

Causal Modeling with Coincidence Analysis

Michael Baumgartner

Ruedi Epple

Veli-Pekka Parkkinen

Alrik Thiem

d.performance function

The data frame contains 214 rows featuring configurations, one column reporting the frequencies of each configuration, and 8 columns listing the following Boolean factors:
<table class="table">
<tr><td>[ , 1]</td><td>MU</td><td>above average industry munificence</td></tr>
<tr><td>[ , 2]</td><td>DY</td><td>high industry dynamism</td></tr>
<tr><td>[ , 3]</td><td>CO</td><td>high industry competitiveness</td></tr>
<tr><td>[ , 4]</td><td>DIV</td><td>high corporate diversification</td></tr>
<tr><td>[ , 5]</td><td>CRA</td><td>above median corporate resource availability</td></tr>
<tr><td>[ , 6]</td><td>CI</td><td>above median corporate capital intensity</td></tr>
<tr><td>[ , 7]</td><td>BUS</td><td>large business-unit size</td></tr>
<tr><td>[ , 8]</td><td>SP</td><td>above average business-unit performance (in the manufacturing sector)</td></tr>
</table>

Format

Data on combinations of industry, corporate, and business-unit effects — d.performance

The data frame contains 214 rows featuring configurations, one column reporting the frequencies of each configuration, and 8 columns listing the following Boolean factors:
<table class='table'>
<tr><td>[ , 1]</td><td>MU</td><td>above average industry munificence</td></tr>
<tr><td>[ , 2]</td><td>DY</td><td>high industry dynamism</td></tr>
<tr><td>[ , 3]</td><td>CO</td><td>high industry competitiveness</td></tr>
<tr><td>[ , 4]</td><td>DIV</td><td>high corporate diversification</td></tr>
<tr><td>[ , 5]</td><td>CRA</td><td>above median corporate resource availability</td></tr>
<tr><td>[ , 6]</td><td>CI</td><td>above median corporate capital intensity</td></tr>
<tr><td>[ , 7]</td><td>BUS</td><td>large business-unit size</td></tr>
<tr><td>[ , 8]</td><td>SP</td><td>above average business-unit performance (in the manufacturing sector)</td></tr>
</table>

d.performance: Data on combinations of industry, corporate, and business-unit effects

Description

Usage

Arguments

Format

[ , 1]	MU	above average industry munificence
[ , 2]	DY	high industry dynamism
[ , 3]	CO	high industry competitiveness
[ , 4]	DIV	high corporate diversification
[ , 5]	CRA	above median corporate resource availability
[ , 6]	CI	above median corporate capital intensity
[ , 7]	BUS	large business-unit size
[ , 8]	SP	above average business-unit performance (in the manufacturing sector)