Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data.

By J. Mcardle, Fumiaki Hamagami. 2001

535 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/936fdd8b2874430efe1f1f264d18b628ca785e99

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In Computer Networks and Communications

In Management Science and Operations Research

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Citations in the corpus

Bayesian analysis of longitudinal data using growth curve models (2007, methodology): “BAUW can be used to generate WinBUGS programs for many kinds of models, such as growth curve models, latent difference score models (McArdle & Hamagami, 2001), and IRT models (Embretson & Reise, 2000).”
The bidirectional relation between positive mental health and psychopathology in a longitudinal representative panel study (2015, methodology): “The statistical analyses were done using a combination of latent growth modeling (see McArdle & Hamagami, 2001; Skrondal & Rabe-Hesketh, 2004) with an IRT model.”
Evaluating Latent Growth Curve Models Using Individual Fit Statistics (2006, methodology): “…confirmatory factor analysis model in a seminal article by Meredith and Tisak (1990) and to the development of techniques for handling missing data (McArdle & Hamagami, 2001), in particular computational algorithms such as EM (Dempster, Laird, & Rubin, 1977) and estimation techniques such as…”
Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement (2007, methodology): “Bivariate latent difference score growth model Latent difference score (LDS) growth models (Hamagami & McArdle, 2001; McArdle, 2001; McArdle & Hamagami, 2001) are an extension of the latent growth curve and autoregressive time-series models.”
The health and relationship dynamics of late-life couples: a systematic review of the literature (2009, background): “The recent use of dynamic dual change score models to reveal the dynamics for both the partners and the couple (McArdle and Hamagami 2001), and within or across domains of health will eventually clarify causal links (e.”
Relating Latent Change Score and Continuous Time Models (2015, methodology): “Both classes of models are extremely powerful because they allow us to address key objectives in longitudinal research (Baltes & Nesselroade, 1979; McArdle & Hamagami, 2001) including questions regarding the dynamic development and interplay between psychological constructs over time.”
The Effects of Prisoner Attachment to Family on Re-entry Outcomes: A Longitudinal Assessment (2016, methodology): “…on both the types of visits received (distinguishing between visits from parents, partners and siblings) and the frequency of visits, we estimate a latentchange structural equation model (Raykov 1993; McArdle and Hamagami 2001) to data from a longitudinal survey of prisoners in England and Wales.”
Alternative Structural Models for Multivariate Longitudinal Data Analysis (2003, methodology): “This specification, labeled dual change score model, is not the only possibility (see McArdle, 2001; McArdle & Hamagami, 2001).”
Using residualized change versus difference scores for longitudinal research (2018, methodology): “For example, one of the best-known LCS models introduced by McArdle and colleagues (McArdle, 2001; McArdle & Hamagami, 2001) is referred to as the dual LCS model.”
Mediation Analysis in a Latent Growth Curve Modeling Framework (2011, unknown): “…longitudinal autoregressive models (Cole & Maxwell, 2003; MacKinnon, 2008), latent difference score models (Ferrer & McArdle, 2003; MacKinnon, 2008; McArdle & Hamagami, 2001), and growth curve models (Cheong, MacKinnon, & Khoo, 2001, 2003; Kenny, Korchmaros, & Bolger, 2003; Maxwell & Cole, 2007).”
On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice (2020, background): “Some of the longitudinal models that include cross-lagged parameters (e.g., the stable trait autoregressive trait and state (STARTS) model; Kenny and Zautra (1995, 2001); and latent change score (LCS) model; Hamagami and McArdle (2001); McArdle and Hamagami (2001) assume unique factors.”
Fluid Intelligence as a Predictor of Learning: A Longitudinal Multilevel Approach Applied to Math. (2010, background): “Recent applications of multilevel modeling (Bryk & Raudenbush, 1987; Plewis, 2005) and latent growth curve analysis (McArdle & Hamagami, 2001) have been proposed as a way of overcoming problems in this regard.”
A review of 25 years of research in bidirectionality in parent–child relationships (2016, background): “The second are latent difference score structural models, which measure different types of change across time in order to more accurately capture developmental change and bidirectionality (McArdle & Hamagami, 2001).”