The Comparative Advantages of fsQCA and Regression Analysis for Moderately Large-N Analyses

Abstract

This article contributes to the literature on comparative methods in the social sciences by assessing the strengths and weaknesses of regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) for studies with a moderately large-n (between approximately 50 and 100). Moderately large-n studies are interesting in this respect since they allow for regression analysis as well as fsQCA analysis. These two approaches have a different epistemological foundation and thereby answer different, yet related, research questions. To illustrate the comparison of fsQCA and regression analysis empirically, I use a recent data set (n = 53) that includes data on the conditions under which governments in Western democracies increase their spending on active labor market policies (ALMPs). This comparison demonstrates that while each approach has merits and demerits, fsQCA leads to a fuller understanding of the conditions under which the outcome occurs.

Risk Disclosure Analysis in the Corporate Governance Annual Report Using Fuzzy-Set Qualitative Comparative Analysis

“…The fsQCA tests whether a condition or combination of conditions is necessary and/or sufficient for a certain outcome. It also aims to reveal the minimal (combinations of) conditions bringing about a particular outcome (Vis, 2012), therefore, it explores complex pathways fsQCA essentially investigates set relations, and it changes variables within and across them to find the combinations of causal sets that better match the outcome. Sets are "fuzzy" when the criterion for membership allows objects to possess the required common property in varying degrees (Stevenson, 2013).…”

“…In addition, thinking in terms of alternative mechanisms indicates that several causal recipes can relate to the outcome or response variable (Woodside, 2013). Besides, for studies with a moderately large-n, fsQCA has typically most to offer (Vis, 2012). that no statements are being made about the situations that did not occur empirically.…”

“…Traditional regressions follow an effects-of-causes approach in which the goal is to estimate if an individual variable has a significant (positive or negative) effect, net of other variables' effects, on the dependent variable in a population of cases. We used fsQCA instead, which mostly fits the causes-of-effects approach, to explore more complex pathways, i.e., combinations of necessary, sufficient and minimal conditions (Vis, 2012) related with good CG mechanisms under which high risk disclosure is provided in the CGAR. Hence, we found several causal recipes related with the outcome or response variable (Woodside, 2013).…”

Strategy, operations, and profitability: the role of resource orchestration

“…MRA is used to test if an independent variable, within a set of independent variables, has a positive or negative effect on a dependent variable, net of the other variables (Vis, 2012). First, then, the study examines whether or not objective data on acquisitions; the cost of goods sold; employees; property, plant and equipment; advertising expense; pension and retirement expense; R&D expense; and selling, general and administrative expense (minus advertising and R&D expense to prevent double-counting), relates to firm profitability.…”

“…Since configurations may be numerous, it is not specified in advance which configurations will be most strongly associated with high firm profitability. As noted by Vis (2012), fsQCA enables the possibility of addressing multiple causations when it is likely that there are several resource configurations leading to performance (i.e., resource orchestration recipes for performance).…”