Contextual analyses with QCA-methods

Denk, Thomas; Lehtinen, Sarah

doi:10.1007/s11135-013-9968-4

Cited by 20 publications

(18 citation statements)

References 17 publications

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“…With such a methodological basis, it is difficult to capture complementarity between factors, consider the degree of necessity of a variable, or find multiple causal pathways leading to an outcome (Denk and Lehtinen 2013;Fiss 2007). Quantitative methods such as regression analysis and social network analysis measure the isolated, net effects of factors (or more formally, predictors) on an outcome.…”

Section: Methodological Limitationsmentioning

confidence: 99%

“…These methods allow for an in-depth, rich understanding of a particular context, but are generally non-systematic and non-comparative, and therefore have difficulties transcending contextuality (Denk and Lehtinen 2013). Qualitative approaches, including single or multiple case studies, can more easily uncover causal complexity and offer valuable knowledge on how outcomes are generated.…”

Section: Methodological Limitationsmentioning

confidence: 99%

“…This means that a predictor is held constant for all others, and its effect is assumed to be similar over all values of the other predictors. With such a methodological basis, it is difficult to capture complementarity between factors, consider the degree of necessity of a variable, or find multiple causal pathways leading to an outcome (Denk and Lehtinen 2013;Fiss 2007).…”

Section: Methodological Limitationsmentioning

confidence: 99%

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Reconciling Perspectives on Clusters: An Integrative Review and Research Agenda

Speldekamp

Saka‐Helmhout

Knoben

2019

Int J Management Reviews

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Although our understanding of clusters and their contribution to economic performance has improved over the last three decades, the literature has become host to a wide array of divergent empirical and theoretical claims. We systematically review cluster studies published in top journals, highlighting the lack of integration among prior work. We focus on the ways in which Porter's three cluster dimensions, namely geography, networks, and institutions, have been utilized. None of the studies reviewed fully captured their complex interrelationships, which we argue is an important cause of the key disagreements in the literature. Configurational theorizing and analysis are presented as means by which the different approaches to cluster studies could be reconciled. We discuss how the application of a configurational approach can help explore new scholarly directions that can deepen our understanding of clusters and their performance‐enhancing potential. In doing so, we can move beyond an understanding of independent effects to emphasizing combinations of attributes that can generate multiple pathways to cluster performance outcomes.

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Section: Methodological Limitationsmentioning

confidence: 99%

Section: Methodological Limitationsmentioning

confidence: 99%

Section: Methodological Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Reconciling Perspectives on Clusters: An Integrative Review and Research Agenda

Speldekamp

Saka‐Helmhout

Knoben

2019

Int J Management Reviews

View full text Add to dashboard Cite

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“…Depending on the research setting, it might therefore be advisable to compare more than just two contexts. Denk and Lehtinen (2013) do so, but they do not explain how to deal with inconclusive contextual effects.…”

Section: Esp Regions: Edu [Fam Rc T → R] (Formalization 1)mentioning

confidence: 96%

“…Simultaneously, our analysis identifies clear limitations of CMA in handling equifinality and limited diversity at the context level. One of CMA's main advantages is that it can easily be complemented with other techniques and methods (Denk & Lehtinen, 2013). This allows us to propose practical refinements of the CMA-approach.…”

Section: Introductionmentioning

confidence: 99%

Identifying context and cause in small-N settings: a comparative multilevel analysis

Thomann

Manatschal

2015

Policy Sci

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This is the accepted version of: Thomann, E. and A. Manatschal. 2016. Identifying context and cause in small-N settings: A Comparative Multilevel Analysis. Policy Sciences 49(3): 335-348. AbstractQualitative small-N comparisons face the challenge to detect context-bound causality under conditions of limited empirical diversity. Rather than treating context as a causal factor, we test the usefulness of the novel method of Comparative Multilevel Analysis (CMA) to identify and understand the role of context as a contingent necessary condition that enables a causal relationship to unfold. Combining CMA with pairwise comparisons, we assess how organ donation policies in Switzerland and Spain affect relatives' refusal rates in a small-N setting exhibiting multiple contextual levels. To tackle limited diversity systematically, we suggest to refine the CMA methodology by accounting for several contexts and referring to higher-order constructs. Applying CMA with these refinements, we find voluntary information measures only affect refusal rates in contexts of a credible state explicitly supporting organ donation.The fact that CMA can easily be combined with other analytical and conceptual approaches makes it an effective technique to identify contextual effects in small-N research.

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Analyzing multilevel data with QCA: yet another straightforward procedure

Thiem

2014

Qual Quant

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Contextual analyses with QCA-methods

Cited by 20 publications

References 17 publications

Reconciling Perspectives on Clusters: An Integrative Review and Research Agenda

Reconciling Perspectives on Clusters: An Integrative Review and Research Agenda

Identifying context and cause in small-N settings: a comparative multilevel analysis

Analyzing multilevel data with QCA: yet another straightforward procedure

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