Qualitative Comparative Analysis (QCA) is said to be a method that can be used to uncover social complexity. However, this complexity is often ‘missing in action’ in actual empirical applications of the method. We aim to rearticulate the properties of social and causal complexity in their relationship to QCA. We first discuss the reasons why this relationship is not fully articulated in the current research. Rooted in a realist understanding of social emergence, we identify four possible strategies to bring social complexity back on focus when performing QCA: the use of thick case descriptions; the opening of the black box of conditions, by stacking and/or by developing them in a grounded manner; the integration of time in the method itself; and the combination of QCA with other, more time-sensitive methods.

Governance dynamics and spatial planning regulations are significant factors in the occurrence (or containment) of urban sprawl. However, qualitative investigations of the planning regulatory systems and practices, and governance arrangements that cumulatively stimulate suburbanisation, typically remain detached from land-change analyses. Based on the concept of institutional frames of spatial planning systems, this article elucidates how governance dynamics and spatial planning practices, at different scales, can partially explain suburban land-use patterns. The territorial transformations of two Southern European metropolitan regions, Barcelona and Milan, are examined through land-use data (1990–2012) at different territorial scales. Demographic (1991–2011) and administrative (2011) data are also analysed. In-depth interviews about individual and collective land management practices have been carried out, as well as document analysis concerning spatial planning laws and regulations. This research shows that the metropolitan character of urban sprawl originates from local planning practices mainly performed by municipal authorities through land-use micro-transformations. Further, it highlights the decisive role that higher-level institutions can play in land containment. Urban sprawl is hence not necessarily an unplanned phenomenon, but rather a ‘differently planned’ local and regional land-use strategy.

Qualitative comparative analysis was initially time-agnostic, but efforts to make the method more time-sensitive have been made since the mid-2000s. These attempts mainly focus on cross-case differences, accounting for change over time at the level of attributes or conditions. While useful, they cannot account for the fact that individual cases also develop over time. As such, strategies regarding ‘within-case’ development have remained under-theorized in qualitative comparative analysis (QCA). To address this gap, we propose trajectory-based qualitative comparative analysis (TJ-QCA) building on the logic of the diversity-oriented approach: meaningful within-case change is carefully defined in terms of development stages that capture qualitative case-based change patterns. We conceptualize configurations dynamically so that they express different development stages. Theoretically, our method is rooted in a complexity-informed understanding of cases describing trajectories through the property space. Trajectory-based qualitative comparative analysis works with both numerical and qualitative data. We will illustrate the method empirically by re-elaborating a well-known published time-sensitive qualitative comparative analysis study.

Qualitative Comparative Analysis (QCA) includes two main components: QCA “as a research approach” and QCA “as a method”. In this study, we focus on the former and, by means of the “interpretive spiral”, we critically look at the research process of QCA. We show how QCA as a research approach is composed of (1) an “analytical move”, where cases, conditions and outcome(s) are conceptualised in terms of sets, and (2) a “membership move”, where set membership values are qualitatively assigned by the researcher (i.e. calibration). Moreover, we show that QCA scholars have not sufficiently acknowledged the data generation process as a constituent research phase (or “move”) for the performance of QCA. This is particularly relevant when qualitative data–e.g. interviews, focus groups, documents–are used for subsequent analysis and calibration (i.e. analytical and membership moves). We call the qualitative data collection process “relational move” because, for data gathering, researchers establish the social relation “interview” with the study participants. By using examples from our own research, we show how a dialogical interviewing style can help researchers gain the in-depth knowledge necessary to meaningfully represent qualitative data into set membership values for QCA, hence improving our ability to account for the “qualitative” in QCA.