Abstract
In this rcscarch wc describe longitudinal structural equaiion modeis useful for testing dynamic hypothesis. The Statistical modeis described here come from recent research on latent variable siruclural equaiion modeling (SEM) for longiiudinal data. The initial set of analyses arc hased on considerations about measurement modeis with changing scales over timc following modcls used by McArdle & Woodcock (1997). A second set of analyses are based directly on the latent growth curvc model of Meredith & Tisak (1990). A third set of analyses are based on latent difference score modcls of McArdle & Nesselroade (1994) and McArdle (2001). In a fourth and final set of analyses we present some new bivariate dynamic model across different variables at different ages from McArdle & Hamagami (2001). These SEM analyses permit a dynamk Interpretation of the developmental influences of onc variable upon another over timc and can bc used with many form of repeated mcasures longiiudinal data. This rcscarch paper emphasi/cs practica! aspects of testing dynamic hypolheses with SEM, but implications for turther experimcntal and developmental rcscarch arc also discussed.
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McArdle, J., Hamagami, F. (2004). Methods for Dynamic Change Hypotheses. In: van Montfort, K., Oud, J., Satorra, A. (eds) Recent Developments on Structural Equation Models. Mathematical Modelling: Theory and Applications, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-1958-6_15
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