First- Versus Second-Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory

Geiser, Christian; Keller, Brian T.; Lockhart, Ginger

doi:10.1080/10705511.2013.797832

Cited by 90 publications

(128 citation statements)

References 42 publications

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“…Methodologists have favored SO-LCM over first-order LCM because it separates measurement error and occasion-or state-specific variability (as first-order latent constructs) from true, trait-based change in a second-order latent construct, and it has greater power to detect mean-level changes because it accounts for the unreliability of manifest variables (Bollen & Curran, 2006;Geiser et al, 2013).…”

Section: Data Analytic Approachmentioning

confidence: 99%

“…SO-LCM captures the development of a construct with two second-order latent random factors: intercept (i.e., initial status) and slope (i.e., change over time; Bollen & Curran, 2006;Geiser et al, 2013). Specifically, we tested: (a) a strict stability model assuming no meanlevel change in sympathy/overt aggression from T1-T3 in which only the intercept was estimated (intercept factor loadings fixed at 1), (b) a linear change model in which a latent slope represented a linear change in sympathy/overt aggression over time (slope factor loadings fixed at 0, 1, and 2, respectively), and (c) a non-linear change model in which change was not specified a priori (first and last slope factor loadings fixed at 0 and 1, respectively, and second factor loading freely estimated; see Bollen & Curran, 2006).…”

Section: Developmental Trajectories Of Sympathy and Overt Aggressionmentioning

confidence: 99%

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The codevelopment of sympathy and overt aggression from middle childhood to early adolescence.

Zuffianò

Colasante

Buchmann

et al. 2018

Developmental Psychology

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We assessed the extent to which feelings of sympathy and aggressive behaviors co-developed from 6 to 12 years of age in a representative sample of Swiss children (N = 1,273). Caregivers and teachers reported children's sympathy and overt aggression in three-year intervals. Secondorder latent curve models indicated general mean-level declines in sympathy and overt aggression over time, although the decline in sympathy was relatively small. Importantly, both trajectories were characterized by significant inter-individual variability. A bivariate secondorder latent curve model revealed a small-moderate negative correlation between the latent slopes of sympathy and overt aggression, suggesting an inverse co-developmental relationship between the constructs from middle childhood to early adolescence. In terms of predictive effects, an autoregressive cross-lagged model indicated a lack of bidirectional relations between sympathy and overt aggression, underscoring the primacy of the variables' rank-order stability.We discuss the co-development and developmental relations of sympathy and aggression, their potential conjoint social-emotional mechanisms, and the practical implications thereof.Keywords: sympathy, overt aggression, co-development, childhood, adolescence, secondorder latent curve model CO-DEVELOPMENT OF SYMPATHY AND AGGRESSION 3The Co-Development of Sympathy and Overt Aggression from Middle Childhood to Early Adolescence The role of sympathy in children's aggressive behavior has received considerable attention from developmental scientists over the past three decades (Joliffe & Farrington, 2006;MacEvoy & Leff, 2012;Miller & Eisenberg, 1988). The ability to sympathize, which often includes encoding and experiencing others' emotional states, is thought to help children anticipate and recognize the negative consequences of aggressive acts (for a review, see Eisenberg, Spinrad, & Morris, 2014). Children with high levels of sympathy engage in less frequent and less severe aggression (Lovett & Sheffield, 2007;van Noorden, Haselager, Cillessen, & Bukowski, 2014). Such findings yield some support for social-emotional interventions to prevent and reduce aggressive behavior, which operate under the conceptual premise that fostering sympathy and related capacities in children will result in corresponding decreases in their aggression (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011;Malti, Chaparro, Zuffianò, & Colasante, 2016). However, the extent to which a child's mean-level growth in sympathy is related to a parallel, mean-level decrease in his or her aggression has not been empirically detailed within a longitudinal framework. We aimed to bridge this theoryevidence gap by investigating the co-development of sympathy and aggression from age 6 to 12 in a large, representative sample of Swiss children. In addition, given the scarcity of past longitudinal studies analyzing predictive relations between sympathy and aggression, we aimed to investigate their possible reciprocal effects and thereby offer a...

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Section: Data Analytic Approachmentioning

confidence: 99%

Section: Developmental Trajectories Of Sympathy and Overt Aggressionmentioning

confidence: 99%

The codevelopment of sympathy and overt aggression from middle childhood to early adolescence.

Zuffianò

Colasante

Buchmann

et al. 2018

Developmental Psychology

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“…-LST, Latent state-trait theory: this approach is based on a second order factorial model (Geiser et al 2013). The first order factorial variables are represented by state-latent variables, the second order ones are the latent growth components.…”

Section: Methodsmentioning

confidence: 99%

A micro approach to cognitive skills’ growth in a university context

Simonetto

Zavarrone

2014

Qual Quant

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This paper focuses on the measurement of human capital, specifically on the growth of cognitive skills (CS) during the higher education at university. CS have already been evaluated in a macroeconomic perspective inside the neoclassical growth models, but not still in a micro perspective. However the measure of educational quality and learning process is still an issue not fully addressed. The micro approach allows the researcher to focus on the evaluation of CS acquisition process. Based on these measurements, different types of CS accumulation can be identified. We will investigate CS through nonlinear latent growth modeling, and we will apply this methodology to administrative data of an Italian university

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“…The methodological advantages of the CUFFS model over the 1LGM have been previously documented (Ferrer, Balluerka, & Widaman, 2008;Geiser et al, 2013; Leite, 2007;Murphy, Beretvas, & Pituch, 2011;Sayer & Cumsille, 2001;von Oerzen, Hertzog, Lindenberger, & Ghisletta, 2010;Widaman, Ferrer, & Conger, 2010). We summarize the most important strengths of the CUFFS model.…”

Section: Modeling Growth Trajectoriesmentioning

confidence: 99%

“…An extension of the 1LGM, the CUFFS model characterizes the relation between the multiple items and their underlying construct at each time point, as well as the construct's growth trajectory. Although the CUFFS model was introduced almost 30 years ago and despite the analytical advantages it offers over the 1LGM, its application in educational research and the social sciences has been limited (Geiser, Keller, & Lockhart, 2013). Consequently, the benefits of the CUFFS model remain unfamiliar to many educational researchers working with longitudinal data.…”

mentioning

confidence: 99%

Curve of Factors Model: A Latent Growth Modeling Approach for Educational Research

Isiordia

Ferrer

2016

Educational and Psychological Measurement

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A first-order latent growth model assesses change in an unobserved construct from a single score and is commonly used across different domains of educational research. However, examining change using a set of multiple response scores (e.g., scale items) affords researchers several methodological benefits not possible when using a single score. A curve of factors (CUFFS) model assesses change in a construct from multiple response scores but its use in the social sciences has been limited. In this article, we advocate the CUFFS for analyzing a construct's latent trajectory over time, with an emphasis on applying this model to educational research. First, we present a review of longitudinal factorial invariance, a condition necessary for ensuring that the measured construct is the same across time points. Next, we introduce the CUFFS model, followed by an illustration of testing factorial invariance and specifying a univariate and a bivariate CUFFS model to longitudinal data. To facilitate implementation, we include syntax for specifying these statistical methods using the free statistical software R. Keywords curve of factors model, latent growth models, longitudinal data analysisAssessing change in an unobservable construct (e.g., motivation, academic interest, peer relations) over time is essential for understanding developmental processes in education. Under a structural equation modeling (SEM) framework, latent growth modeling (LGM) has become a prominent method among educational researchers for

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First- Versus Second-Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory

Cited by 90 publications

References 42 publications

The codevelopment of sympathy and overt aggression from middle childhood to early adolescence.

The codevelopment of sympathy and overt aggression from middle childhood to early adolescence.

A micro approach to cognitive skills’ growth in a university context

Curve of Factors Model: A Latent Growth Modeling Approach for Educational Research

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