Replication Data for: Modeling Configurational Explanations (doi:10.7910/DVN/FORHNF)

View:

Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

Replication Data for: Modeling Configurational Explanations

Identification Number:

doi:10.7910/DVN/FORHNF

Distributor:

Harvard Dataverse

Date of Distribution:

2021-02-09

Version:

1

Bibliographic Citation:

Damonte, Alessia, 2021, "Replication Data for: Modeling Configurational Explanations", https://doi.org/10.7910/DVN/FORHNF, Harvard Dataverse, V1, UNF:6:MUFgM1MkSiqWIdG73d/g1A== [fileUNF]

Study Description

Citation

Title:

Replication Data for: Modeling Configurational Explanations

Identification Number:

doi:10.7910/DVN/FORHNF

Authoring Entity:

Damonte, Alessia (University of Milan)

Distributor:

Harvard Dataverse

Access Authority:

Damonte, Alessia

Depositor:

Damonte, Alessia

Date of Deposit:

2020-12-24

Study Scope

Keywords:

Computer and Information Science, Social Sciences, Explanation, inus causation, mediation, pruning, Qualitative Comparative Analysis, quasi-experimental designs, Structural Causal Model framework

Abstract:

How can Qualitative Comparative Analysis contribute to causal knowledge? The article’s answer builds on the shift from design to models that the Structural Causal Model framework has compelled in the probabilistic analysis of causation. From this viewpoint, models refine the claim that a ‘treatment’ has causal relevance as they specify the ‘covariates’ that make some units responsive. The article shows how QCA can establish configurational models of plausible ‘covariates’. It explicates the rationale, operations, and criteria that confer explanatory import to configurational models, then illustrates how the basic structures of the SCM can widen the interpretability of configurational solutions and deepen the dialogue among techniques.

Notes:

Raw, fuzzy data, thresholds for replication of the QCA, then of the operations to identify the shape of the relationships between core and peripheral conditions in its solutions along the lines of the SCM.

Methodology and Processing

Sources Statement

Data Access

Notes:

CC0 Waiver

Other Study Description Materials

Related Publications

Citation

Bibliographic Citation:

Damonte, Alessia (2021), 'Modeling configurational explanations', Italian Political Science Review/Rivista Italiana di Scienza Politica

File Description--f4275004

File: IPSR20_MCE_fuzzy.tab

  • Number of cases: 26

  • No. of variables per record: 7

  • Type of File: text/tab-separated-values

Notes:

UNF:6:k5hFS2HfTXSIOGTCEUopsQ==

File Description--f4275005

File: IPSR20_MCE_raw.tab

  • Number of cases: 26

  • No. of variables per record: 7

  • Type of File: text/tab-separated-values

Notes:

UNF:6:Es5aZKG8fbTkctmhvqWwcQ==

Variable Description

List of Variables:

Variables

id

f4275004 Location:

Variable Format: character

Notes: UNF:6:pr3iTnGiXHYNTC2eDpiSPQ==

clean

f4275004 Location:

Summary Statistics: StDev 0.4274251279464042; Min. 0.005; Mean 0.571; Valid 26.0; Max. 0.994

Variable Format: numeric

Notes: UNF:6:gssjzCieluiliym4HuYf4g==

atec

f4275004 Location:

Summary Statistics: Mean 0.6442692307692308; Min. 0.0; Valid 26.0; StDev 0.422253910124447; Max. 1.0

Variable Format: numeric

Notes: UNF:6:Kr3kfNnp90Wp3dPSj24OIg==

asoc

f4275004 Location:

Summary Statistics: Valid 26.0; Mean 0.585076923076923; Min. 0.0; Max. 1.0; StDev 0.44476276130781656;

Variable Format: numeric

Notes: UNF:6:1iYNuFjTT/R89IyG636QHg==

apub

f4275004 Location:

Summary Statistics: Min. 0.002; Valid 26.0; StDev 0.4406762024260164; Mean 0.5103461538461539; Max. 0.998

Variable Format: numeric

Notes: UNF:6:h62nC/bRDrfATjQvBMgrGw==

rta

f4275004 Location:

Summary Statistics: Valid 26.0; Max. 1.0; Min. 0.0; Mean 0.45369230769230773; StDev 0.38981952431665284

Variable Format: numeric

Notes: UNF:6:GgQNxWQF7aQG2VbP2alE3A==

enfor

f4275004 Location:

Summary Statistics: Max. 0.997; Mean 0.5394230769230769; Valid 26.0; Min. 0.0; StDev 0.41417775633917603;

Variable Format: numeric

Notes: UNF:6:I8FWkGHLnJ5D7/IT45/JWw==

id

f4275005 Location:

Variable Format: character

Notes: UNF:6:pr3iTnGiXHYNTC2eDpiSPQ==

clean.r

f4275005 Location:

Summary Statistics: StDev 0.15079073629983428; Valid 26.0; Max. 0.89; Mean 0.6853846153846154; Min. 0.43;

Variable Format: numeric

Notes: UNF:6:DKtkssvsouj1Qc/J4Hn89g==

atec.r

f4275005 Location:

Summary Statistics: Max. 0.942595; Mean 0.7554868076923077; Min. 0.41269; Valid 26.0; StDev 0.14901314451819855

Variable Format: numeric

Notes: UNF:6:KVr/pok5DO1E2Ws8Gv2s9w==

asoc.r

f4275005 Location:

Summary Statistics: Mean 0.7818676538461539; Max. 0.973083; Valid 26.0; StDev 0.10971816865367094; Min. 0.490233;

Variable Format: numeric

Notes: UNF:6:Rkt0WxNxWgTeSrP0dZxheQ==

apub.r

f4275005 Location:

Summary Statistics: Valid 26.0; Mean 0.7341856538461539; Max. 0.910391; StDev 0.1259681396261586; Min. 0.541897

Variable Format: numeric

Notes: UNF:6:YlDyTEe7TRTzVDHgcgL6gg==

rta.r

f4275005 Location:

Summary Statistics: Valid 26.0; Min. 0.451707; Max. 0.947463; StDev 0.10746862548942437; Mean 0.6922468846153846

Variable Format: numeric

Notes: UNF:6:TlSA2qMZpEnv5wRqsH4P8w==

enfor.r

f4275005 Location:

Summary Statistics: Min. 0.367565; StDev 0.16322227898606434; Max. 0.927448; Mean 0.6982242692307692; Valid 26.0

Variable Format: numeric

Notes: UNF:6:yhZLqn4sMPC/wdA8V5j1gg==

Other Study-Related Materials

Label:

IPSR20_MCE_OLA.pdf

Text:

Data with keys and sources; descriptives; thresholds used for calibration

Notes:

application/pdf