Chapter 3 The Two QCAs: From a Small-N to a Large-N Set Theoretic Approach
Abstract
Although QCA was originally developed specifically for small-N settings, recent studies have shown its potential for large-N organization studies. In this chapter, we provide guidance to prospective researchers with the goal of opening up QCA’s potential for widespread use in organization studies involving large-N settings, both as an alternative and as a complement to conventional regression analyses. We compare small-N and large-N QCA with respect to theoretical assumptions and objectives, processes and decisions involved in building the causal model, selecting the sample, as well as analyzing the data and interpreting the results. Finally, we discuss the prospects for large-N configurational analysis in organization studies and related fields going forward.
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Taking a set-theoretic approach to the study of large-N samples differs from a small-N approach in terms of researchers' goals, assumptions, and research processes.
“Taking a set-theoretic approach to the study of large-N samples differs from a small-N approach in terms of researchers' goals, assumptions, and research processes (Greckhamer, Misangyi, & Fiss, 2013).”
From Embracing Causal Complexity: The Emergence of a Neo-Configurational Perspective (2017) · cites this paper as Greckhamer, Misangyi, & Fiss, 2013
Hypotheses testing has been less common in QCA research, most likely because of the challenge of having to a priori explicate the causal complexity underlying theorizing.
“Hypotheses testing has been less common in QCA research most likely because of the challenge presented by having to a priori explicate the causal complexity that underlies theorizing (Fiss et al., 2013; Greckhamer et al., 2013).”
From Embracing Causal Complexity: The Emergence of a Neo-Configurational Perspective (2017) · cites this paper as Greckhamer et al., 2013
Larger sample sizes allow more conditions to be included in the analysis.
“Larger sample sizes allow more conditions to be included in the analysis [23, 26]”
From The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions (2021) · cites this paper as [23]
As a rule of thumb, once the number of conditions exceeds 6-8 (with up to 50 cases) or 10 (for larger samples), the credibility of solutions may be severely compromised.
“As a rule of thumb, once the number of conditions exceeds 6–8 (with up to 50 cases) or 10 (for larger samples), the credibility of solutions may be severely compromised [23].”
From The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions (2021) · cites this paper as [23]
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Embracing Causal Complexity: The Emergence of a Neo-Configurational Perspective
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- Authors
- Greckhamer, T., Misangyi, V. F., & Fiss, P. C.
- Venue
- Research in the Sociology of Organizations
- Publisher
- Emerald Group Publishing Limited
- Published
- 2013-01
- Pages
- 49-75
- ISSN
- 0733-558X (ISSN-L)
- OpenAlex
- W2506533400
- Cited by
- 435
- References
- 46
- Citations by year
- 2026: 59 · 2025: 62 · 2024: 50 · 2023: 53 · 2022: 34 · 2021: 46 · 2020: 40 · 2019: 27 · 2018: 22 · 2017: 19 · 2016: 8 · 2015: 9 · 2014: 4 · 2013: 2
- FWCI
- 48.54
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Research in the sociology of organizations (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Qualitative Comparative Analysis Research
- Social Sciences › Decision Sciences › Information Systems and Management › Business and Management Studies
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- qualitative comparative analysis, configurational analysis, sample selection, organization studies
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