An Illustration of Second-Order Latent Growth Models

2001 ◽  
Vol 8 (3) ◽  
pp. 470-489 ◽  
Author(s):  
Gregory Hancock ◽  
Wen-Ling Kuo ◽  
Frank Lawrence
Keyword(s):  
Growth Models ◽  
Second Order ◽  
Latent Growth ◽  
Latent Growth Models

Second-order latent growth models.

2001 ◽  
pp. 179-200 ◽  
Author(s):  
Aline G. Sayer ◽  
Patricio E. Cumsille
Keyword(s):  
Growth Models ◽  
Second Order ◽  
Latent Growth ◽  
Latent Growth Models

scholarly journals Factorial Invariance and the Specification of Second-Order Latent Growth Models

Methodology ◽  
2008 ◽  
Vol 4 (1) ◽  
pp. 22-36 ◽  
Author(s):  
Emilio Ferrer ◽  
Nekane Balluerka ◽  
Keith F. Widaman
Keyword(s):  
Latent Variable ◽  
Growth Models ◽  
Second Order ◽  
Factorial Invariance ◽  
Future Research ◽  
Latent Growth ◽  
Latent Growth Models ◽  
Multiple Indicators ◽  
Confirmatory Factor ◽  
Trait Level

Abstract. Latent growth modeling has been a topic of intense interest during the past two decades. Most theoretical and applied work has employed first-order growth models, in which a single manifest variable serves as indicator of trait level at each time of measurement. In the current paper, we concentrate on issues regarding second-order growth models, which have multiple indicators at each time of measurement. With multiple indicators, tests of factorial invariance of parameters across times of measurement can be tested. We conduct such tests using two sets of data, which differ in the extent to which factorial invariance holds, and evaluate longitudinal confirmatory factor, latent growth curve, and latent difference score models. We demonstrate that, if factorial invariance fails to hold, choice of indicator used to identify the latent variable can have substantial influences on the characterization of patterns of growth, strong enough to alter conclusions about growth. We also discuss matters related to the scaling of growth factors and conclude with recommendations for practice and for future research.

Nuances in Alzheimer’s Genetic Risk Reveal Differential Predictions of Non-demented Memory Aging Trajectories: Selective Patterns by APOE Genotype and Sex

2019 ◽  
Vol 16 (4) ◽  
pp. 302-315 ◽  
Author(s):  
G. Peggy McFall ◽  
Lars Bäckman ◽  
Roger A. Dixon
Keyword(s):  
Genetic Risk ◽  
Growth Models ◽  
Memory Performance ◽  
Apoe Genotype ◽  
Cognitively Impaired ◽  
Latent Growth ◽  
Latent Growth Models ◽  
Selective Protection ◽  
Underlying Mechanisms ◽  
Apoe Genotypes

Background: Apolipoprotein E (APOE) is a prominent genetic risk factor for Alzheimer’s disease (AD) and a frequent target for associations with non-demented and cognitively impaired aging. APOE offers a unique opportunity to evaluate two dichotomous comparisons and selected gradations of APOE risk. Some evidence suggests that APOE effects may differ by sex and emerge especially in interaction with other AD-related biomarkers (e.g., vascular health). Methods: Longitudinal trajectories of non-demented adults (n = 632, 67% female, Mage = 68.9) populated a 40-year band of aging. Focusing on memory performance and individualized memory trajectories, a sequence of latent growth models was tested for predictions of (moderation between) APOE and pulse pressure (PP) as stratified by sex. The analyses (1) established robust benchmark PP effects on memory trajectories, (2) compared predictions of alternative dichotomous groupings (ε4- vs ε4+, ε2- vs ε2+), and (3) examined precision-based predictions by disaggregated APOE genotypes. Results: Healthier (lower) PP was associated with better memory performance and less decline. Therefore, all subsequent analyses were conducted in the interactive context of PP effects and sex stratification. The ε4-based dichotomization produced no differential genetic predictions. The ε2-based analyses showed sex differences, including selective protection for ε2-positive females. Exploratory follow-up disaggregated APOE genotype analyses suggested selective ε2 protection effects for both homozygotic and heterozygotic females. Conclusion: Precision analyses of AD genetic risk will advance the understanding of underlying mechanisms and improve personalized implementation of interventions.

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