Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research.

Social Network Analysis and Qualitative Comparative Analysis: Their mutual benefit for the explanation of policy network structures

By switching the level of analysis and aggregating data from the micro-level of individual cases to the macro-level, quantitative data can be analysed within a more case-based approach. This paper presents such an approach in two steps: In a first step, it discusses the combination of Social Network Analysis (SNA) and Qualitative Comparative Analysis (QCA) in a sequential mixed-methods research design. In such a design, quantitative social network data on individual cases and their relations at the micro-level are used to describe the structure of the network that these cases constitute at the macro-level. Different network structures can then be compared by QCA. This strategy allows adding an element of potential causal explanation to SNA, while SNA-indicators allow for a systematic description of the cases to be compared by QCA. Because mixing methods can be a promising, but also a risky endeavour, the methodological part also discusses the possibility that underlying assumptions of both methods could clash. In a second step, the research design presented beforehand is applied to an empirical study of policy network structures in Swiss politics. Through a comparison of 11 policy networks, causal paths that lead to a conflictual or consensual policy network structure are identified and discussed. The analysis reveals that different theoretical factors matter and that multiple conjunctural causation is at work. Based on both the methodological discussion and the empirical application, it appears that a combination of SNA and QCA can represent a helpful methodological design for social science research and a possibility of using quantitative data with a more case-based approach.