rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Longitudinal data analysis using multilevel linear modeling (mlm)
    sub title: fitting an optimal variance-covariance structure
  name:
    name part: Lee, Yuan-Hsuan.
    role:
      role term: creator
  name:
    name part: Kwok, Oi-man
    role:
      role term: thesis advisor.
  name:
    name part: Willson, Victor L.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: This dissertation focuses on issues related to fitting an optimal variance-covariance structure in multilevel linear modeling framework with two Monte Carlo simulation studies. In the first study, the author evaluated the performance of common fit statistics such as Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) and a new proposed method, standardized root mean square residual (SRMR), for selecting the correct within-subject covariance structure. Results from the simulated data suggested SRMR had the best performance in selecting the optimal covariance structure. A pharmaceutical example was also used to evaluate the performance of these fit statistics empirically. The LRT failed to decide which is a better model because LRT can only be used for nested models. SRMR, on the other hand, had congruent result as AIC and BIC and chose ARMA(1,1) as the optimal variance-covariance structure. In the second study, the author adopted a first-order autoregressive structure as the true within-subject V-C structure with variability in the intercept and slope (estimating [tau]₀₀ and [tau]₁₁ only) and investigated the consequence of misspecifying different levels/types of the V-C matrices simultaneously on the estimation and test of significance for the growth/fixed-effect and random-effect parameters, considering the size of the autoregressive parameter, magnitude of the fixed effect parameters, number of cases, and number of waves. The result of the simulation study showed that the commonly-used identity within-subject structure with unstructured between-subject matrix performed equally well as the true model in the evaluation of the criterion variables. On the other hand, other misspecified conditions, such as Under G & Over R conditions and Generally misspecified G & R conditions had biased standard error estimates for the fixed effect and lead to inflated Type I error rate or lowered statistical power. The two studies…
  note: by Yuan-Hsuan Lee.
  note: "Major Subject: Educational Psychology"
  note: Title from author supplied metadata (automated record created 2011-11-01 09:04:20).
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2010 http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8464
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major educational psychology
  subject:
    topic: Fit statistics
  subject:
    topic: Repeated measurements
  subject:
    topic: Multilevel modeling
  subject:
    topic: Variance covariance structure
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8464
  record info:
    record content source: TXA
    record creation date: 111114
    record change date: 20151202144225.0
    record identifier: in00002732977