d.volatile

This dataset is from Wollebaek (2010), who analyzes the causes of disbandings of grassroots associations in Norway.

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.volatile function

The data frame contains 22 rows (cases) and the following 9 columns featuring Boolean factors taking values 1 and 0 only:
<table class="table">
<tr><td>[ , 1]</td><td>PG</td><td>high population growth</td></tr>
<tr><td>[ , 2]</td><td>RB</td><td>high rurbanization (i.e. people moving to previously sparsely populated areas that are</td></tr>
<tr><td></td><td></td><td>not adjacent to a larger city)</td></tr>
<tr><td>[ , 3]</td><td>EL</td><td>high increase in education levels</td></tr>
<tr><td>[ , 4]</td><td>SE</td><td>high degree of secularization</td></tr>
<tr><td>[ , 5]</td><td>CS</td><td>existence of Christian strongholds</td></tr>
<tr><td>[ , 6]</td><td>OD</td><td>high organizational density</td></tr>
<tr><td>[ , 7]</td><td>PC</td><td>existence of polycephality (i.e. municipalities with multiple centers)</td></tr>
<tr><td>[ , 8]</td><td>UP</td><td>urban proximity</td></tr>
<tr><td>[ , 9]</td><td>VO2</td><td>very high volatility of grassroots associations</td></tr>
</table>

Format

Data on the volatility of grassroots associations in Norway between 1980 and 2000 — d.volatile

The data frame contains 22 rows (cases) and the following 9 columns featuring Boolean factors taking values 1 and 0 only:
<table class='table'>
<tr><td>[ , 1]</td><td>PG</td><td>high population growth</td></tr>
<tr><td>[ , 2]</td><td>RB</td><td>high rurbanization (i.e. people moving to previously sparsely populated areas that are</td></tr>
<tr><td></td><td></td><td>not adjacent to a larger city)</td></tr>
<tr><td>[ , 3]</td><td>EL</td><td>high increase in education levels</td></tr>
<tr><td>[ , 4]</td><td>SE</td><td>high degree of secularization</td></tr>
<tr><td>[ , 5]</td><td>CS</td><td>existence of Christian strongholds</td></tr>
<tr><td>[ , 6]</td><td>OD</td><td>high organizational density</td></tr>
<tr><td>[ , 7]</td><td>PC</td><td>existence of polycephality (i.e. municipalities with multiple centers)</td></tr>
<tr><td>[ , 8]</td><td>UP</td><td>urban proximity</td></tr>
<tr><td>[ , 9]</td><td>VO2</td><td>very high volatility of grassroots associations</td></tr>
</table>

d.volatile: Data on the volatility of grassroots associations in Norway between 1980 and 2000

Description

Usage

Arguments

Format

[ , 1]	PG	high population growth
[ , 2]	RB	high rurbanization (i.e. people moving to previously sparsely populated areas that are
		not adjacent to a larger city)
[ , 3]	EL	high increase in education levels
[ , 4]	SE	high degree of secularization
[ , 5]	CS	existence of Christian strongholds
[ , 6]	OD	high organizational density
[ , 7]	PC	existence of polycephality (i.e. municipalities with multiple centers)
[ , 8]	UP	urban proximity
[ , 9]	VO2	very high volatility of grassroots associations