Markov Network Analysis: suggestions for innovations in covariance structure analysis.

Published

Journal Article

Studies of aging offer special methodological challenges to the researcher in that he must often examine the change of multiple correlated variables over time. We present a set of procedures that are specifically designed to model change in such multivariate situations. These procedures, which we will call Markov Network Analysis, are directly applicable to modeling change from longitudinal or serial data. In such cases, the parameters of the model have dynamic interpretations, e.g., as coefficients in positive or negative feedback loops. In cross-sectional data, one cannot directly estimate the dynamic coefficients but the model does show how certain dynamic interpretations can be made. Statistically, maximum likelihood estimation procedures are developed and presented. In the development of the statistical model, it is shown how the bias of sequential hypothesis testing, a frequent occurrence in the estimation of complex covariance structure models, may be reduced.

Full Text

Duke Authors

Cited Authors

  • Woodbury, MA; Manton, KG; Siegler, IC

Published In

Volume / Issue

  • 8 / 3-4

Start / End Page

  • 135 - 140

PubMed ID

  • 6984834

Pubmed Central ID

  • 6984834

International Standard Serial Number (ISSN)

  • 0361-073X

Digital Object Identifier (DOI)

  • 10.1080/03610738208260270

Language

  • eng

Conference Location

  • United States