This chapter reviews the range of hierarchical modelling. It argues that hierarchical models provide the stochastic framework within which to develop integrative process models. The chapter is organized as follows. Section 3.2 recalls the basics of hierarchical forms, including random effects and missing data. Section 3.3 offers some scope for the introduction of other sorts of latent variables. Section 3.4 considers mixture models while Section 3.5 returns to random effects, primarily in the context of structured dependence. Section 3.6 looks at dynamic models while Section 3.7 considers relatively recent ideas in data fusion. The chapter ends with a brief summary in Section 3.8.
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