| Title |
Why simulations are appropriate for evaluating Qualitative Comparative Analysis
|
|---|---|
| Published in |
Quality & Quantity, July 2015
|
| DOI | 10.1007/s11135-015-0251-8 |
| Pubmed ID | |
| Authors |
Ingo Rohlfing |
| Abstract |
QCA has recently been subject to massive criticism and although the substance of that criticism is not completely new, it differs from earlier critiques by invoking simulations for the evaluation of QCA. In addition to debates about the meaning of the simulation results, there is a more fundamental discussion about whether simulations promise any relevant insights in principle. Some voices in the QCA community reject simulations per se because they necessarily lack case knowledge. As a consequence, the debate is at an impasse on a metalevel because critics of QCA rely on simulations, the results of which some QCA proponents refuse to consider as insightful. This article addresses this impasse and presents six reasons why simulations must be considered appropriate for evaluating QCA. I show that if taken to its conclusion, the central argument against simulations undermines the need for running a truth table analysis in the first place. The way forward in this debate should not be about whether simulations are useful, but how to configure meaningful simulations evaluating QCA. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 38 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 36% |
| Student > Doctoral Student | 5 | 13% |
| Researcher | 5 | 13% |
| Professor | 5 | 13% |
| Student > Master | 3 | 8% |
| Other | 2 | 5% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 19 | 49% |
| Business, Management and Accounting | 4 | 10% |
| Economics, Econometrics and Finance | 3 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 6 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2016.
All research outputs
#4,428,156
of 24,749,767 outputs
Outputs from Quality & Quantity
#93
of 685 outputs
Outputs of similar age
#51,529
of 268,457 outputs
Outputs of similar age from Quality & Quantity
#3
of 10 outputs
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So far Altmetric has tracked 685 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 86% of its peers.
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