Abstract
PurposeTo identify the typical multilevel issues in social science, as well as illustrate the theoretical basis, hierarchical models and empirical exemplars of multilevel paradigm.Design/methodology/approachHierarchical and multilevel data are extremely common in social systems, but multilevel analysis is constrained by statistical techniques. With the development of social system theory and empirical methods such as hierarchical structure modeling and latent growth modeling, multilevel paradigm can be used to analyze multilevel data. So it is necessary to identify typical multilevel phenomena in social science and discuss multilevel modeling techniques.FindingsThis paper identifies four typical multilevel phenomena in social system study: hierarchical and clustered sampling, collective construct research, longitudinal repeated measures, and event history analysis. Hierarchical structure modeling and latent growth modeling are effective multilevel analysis techniques in social science because of their advantages in the integration of social system research.Research limitations/implicationsThe quality and availability of multilevel data are the main limitations regarding which model will be applied.Practical implicationsThe paper can aid the provision of effective multilevel models to social workers.Originality/valueThis paper provides information on application of multilevel modeling in social science.
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