Abstract
Discuss how to extend the MDS model for longitudinal data analysis in the context of growth mixture modeling. Scale values can be interpreted in terms of growth or change parameters. Posteriori profile probability is introduced to classify individuals into different growth/change profile types. An example of real data is provided to illustrate the idea.
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- 1.
In MDS, dimensions are defined as a set of m directed axes that are orthogonal to each other in a geometric space. In the applied context, dimensions may be viewed as underlying representations of how the points may form certain groupings, which would meaningfully explain the data. This concept is similar to latent classes or factors in mixture modeling. Distance is defined as distribution of points along k dimension among pairs of objects (e.g., time points) in a plane that shows changes.
- 2.
The issue of setting the origin for each dimension in the PAMS model corresponds to the “centering” issue in multiple regression. That is, just as the interpretation of the intercept parameter in multiple regression changes depending on how the predictor variables are centered, the interpretation of the intercept parameter in latent growth curve models changes depending on placement of the zero point along each growth dimension.
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Ding, C.S. (2018). Longitudinal Analysis Using MDS. In: Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research. Springer, Cham. https://doi.org/10.1007/978-3-319-78172-3_11
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DOI: https://doi.org/10.1007/978-3-319-78172-3_11
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