Factor structure refers to the underlying relationships between variables in a dataset, particularly in the context of factor analysis. Factor analysis is a statistical method used to identify latent factors or underlying dimensions that explain patterns of variation among observed variables. By analyzing the factor structure of a dataset, researchers can uncover the underlying constructs that drive the relationships between different variables. This can help in simplifying data, identifying key factors influencing a phenomenon, and informing the development of theories or models. Understanding factor structure is crucial for making sense of complex data and drawing meaningful conclusions from research findings.