Physics

Perspective (graphical)

Sociology

Inference

Mathematics

Statistics

Machine learning

Cross-sectional study

Econometrics

Artificial intelligence

Psychology

Causal inference

Cross-cultural

Anthropology

Data science

Computer science

Quantum mechanics

Causality (physics)

Spurious relationship

Journal of Business and Psychology

The cross-sectional research design, especially when used with self-report surveys, is held in low esteem despite its widespread use. It is generally accepted that the longitudinal design offers considerable advantages and should be preferred due to its ability to shed light on causal connections. In this paper, I will argue that the ability of the longitudinal design to reflect causality has been overstated and that it offers limited advantages over the cross-sectional design in most cases in which it is used. The nature of causal inference from a philosophy of science perspective is used to illustrate how cross-sectional designs can provide evidence for relationships among variables and can be used to rule out many potential alternative explanations for those relationships. Strategies for optimizing the use of cross-sectional designs are noted, including the inclusion of control variables to rule out spurious relationships, the addition of alternative sources of data, and the incorporation of experimental methods. Best practice advice is offered for the use of both cross-sectional and longitudinal designs, as well as for authors writing and for reviewers evaluating papers that report results of cross-sectional studies.

Do Not Cross Me: Optimizing the Use of Cross-Sectional Designs