Explanatory models for relating growth processes

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    For many purposes, longitudinal data arc a great advance over cross-sectional data. The opportunities for modelling are enhanced if data for several occasions are obtained for a response, .y, and at least one time-varying explanatory variable, x. The article describes, with examples, three modelling approaches when both y and x change over time. The first - a conditional approach - relates x toy in a regression framework. Earlier versions of these models were known as two-wave, two-variable (2W2V) 'causal' models. In the second, unconditional approach, growth or change parameters for x and y are themselves related in a second stage analysis. The third approach is based on structural equations modelling. All three approaches can be implemented in a multilevel framework. The article describes how multilevel models can extend the way we think about the analysis of longitudinal data, and hence how more interesting hypotheses about social processes can be modelled.
    Original languageEnglish
    Pages (from-to)207-225
    Number of pages18
    JournalMultivariate Behavioral Research
    Issue number2
    Publication statusPublished - 2001


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