Multilevel Analysis of Repeated Measures Data

Hierarchically structured data are common in many areas of scientific research. Such data are characterized by nested membership relations among the units of observation. Multilevel analysis is a class of methods that explicitly takes the hierarchical structure into account. Repeated measures data can be considered as having a hierarchical structure as well: measurements are nested within, for instance, individuals. In this paper, an overview is given of the multilevel analysis approach to repeated measures data. A simple application to growth curves is provided as an illustration. It is argued that multilevel analysis of repeated measures data is a powerful and attractive approach for several reasons, such as flexibility, and the emphasis on individual development.     

Discrete Response Multilevel Models for Repeated Measures: An Application to Voting Intentions Data

Repeated measures data can be modelled as a two-levelmodel where occasions (level one units) are grouped byindividuals (level two units). Goldstein et al. (1994)proposed a multilevel time series model when theresponse variable follows a Normal distribution andthe measurements are taken with unequal timeintervals. This paper extends the methodology todiscrete response variables. The models are applied toBritish Election Study data consisting of repeatedmeasures of voting intention.