Results 1  10
of
232
Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency.
 Child Development,
, 1999
"... A semiparametric mixture model was used with a sample of 1,037 boys assessed repeatedly from 6 to 15 years of age to approximate a continuous distribution of developmental trajectories for three externalizing behaviors. Regression models were then used to determine which trajectories best predicte ..."
Abstract

Cited by 176 (22 self)
 Add to MetaCart
(Show Context)
A semiparametric mixture model was used with a sample of 1,037 boys assessed repeatedly from 6 to 15 years of age to approximate a continuous distribution of developmental trajectories for three externalizing behaviors. Regression models were then used to determine which trajectories best predicted physically violent and nonviolent juvenile delinquency up to 17 years of age. Four developmental trajectories were identified for the physical aggression, opposition, and hyperactivity externalizing behavior dimensions: a chronic problem trajectory, a high level neardesister trajectory, a moderate level desister trajectory, and a no problem trajectory. Boys who followed a given trajectory for one type of externalizing problem behavior did not necessarily follow the same trajectory for the two other types of behavior problem. The different developmental trajectories of problem behavior also led to different types of juvenile delinquency. A chronic oppositional trajectory, with the physical aggression and hyperactivity trajectories being held constant, led to covert delinquency (theft) only, while a chronic physical aggression trajectory, with the oppositional and hyperactivity trajectories being held constant, led to overt delinquency (physical violence) and to the most serious delinquent acts.
Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data
, 2004
"... This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. ..."
Abstract

Cited by 160 (16 self)
 Add to MetaCart
(Show Context)
This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables.
Developmental Trajectories of Childhood Disruptive Behaviors and Adolescent Delinquency: A SixSite
 CrossNational Study.” Developmental Psychology
, 2003
"... This study used data from 6 sites and 3 countries to examine the developmental course of physical aggression in childhood and to analyze its linkage to violent and nonviolent offending outcomes in adolescence. The results indicate that among boys there is continuity in problem behavior from childhoo ..."
Abstract

Cited by 131 (9 self)
 Add to MetaCart
This study used data from 6 sites and 3 countries to examine the developmental course of physical aggression in childhood and to analyze its linkage to violent and nonviolent offending outcomes in adolescence. The results indicate that among boys there is continuity in problem behavior from childhood to adolescence and that such continuity is especially acute when early problem behavior takes the form of physical aggression. Chronic physical aggression during the elementary school years specifically increases the risk for continued physical violence as well as other nonviolent forms of delinquency during adolescence. However, this conclusion is reserved primarily for boys, because the results indicate no clear linkage between childhood physical aggression and adolescent offending among female samples despite notable similarities across male and female samples in the developmental course of physical
Beyond SEM: General latent variable modeling
 Behaviormetrika
, 2002
"... This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random e&ects, missing data, sources of variatio ..."
Abstract

Cited by 116 (9 self)
 Add to MetaCart
This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random e&ects, missing data, sources of variation in hierarchical data, hnite mixtures, latent classes, and clusters. These latent variable applications go beyond the traditional latent variable useage in psychometrics with its focus on measurement error and hypothetical constructs measured by multiple indicators. The article argues for the value of integrating statistical and psychometric modeling ideas. Di&erent applications are discussed in a unifying framework that brings together in one general model such di&erent analysis types as factor models, growth curve models, multilevel models, latent class models and discretetime survival models. Several possible combinations and extensions of these models are made clear due to the unifying framework. 1.
General growth mixture modeling for randomized preventive interventions
, 2002
"... This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finitemixture random effects model. The methodology allows one to examine the impact of an interventio ..."
Abstract

Cited by 96 (21 self)
 Add to MetaCart
(Show Context)
This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finitemixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as nonnormative growth. The analysis identifies subgroup membership and allows theorybased modeling of intervention effects in the different subgroups. An example is presented concerning a randomized
Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes
 Psychological Methods
, 2003
"... Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absenc ..."
Abstract

Cited by 89 (10 self)
 Add to MetaCart
(Show Context)
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the withinclass parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested. Over the last decade, random coefficient growth modeling has become a centerpiece of longitudinal data analysis. These models have been adopted enthusiastically by applied psychological researchers in part because they provide a more dynamic analysis of repeated measures data than do many traditional techniques. However, these methods are not ideally suited for testing theories that posit the existence of qualitatively different developmental pathways, that is, theories in which distinct developmental pathways are thought to hold within subpopulations. One widely cited theory of this type is Moffitt’s (1993) distinction between “lifecourse persistent ” and “adolescentlimited ” antisocial behavior trajectories. Moffitt’s theory is prototypical of other developmental taxonomies that have been proposed in such diverse areas as developmental psychopathology (Schulenberg,
Investigating population heterogeneity with factor mixture models
 Psychological Methods
, 2005
"... Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed ..."
Abstract

Cited by 73 (4 self)
 Add to MetaCart
(Show Context)
Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A stepbystep analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point. The populations investigated in the behavioral sciences and related fields of research are often heterogeneous. A sample may consist of explicitly defined groups such as experimental and control groups, and the aim is to compare these groups. On the other hand, the sources of population heterogeneity may not be known beforehand. Test scores on a cognitive test may reflect two types of children in the sample: those who master the knowledge required to solve the items (masters) and those who lack this critical knowledge (nonmasters). The interest may be to decide to which of the subpopulations a given child most likely belongs. In addition, it may be of interest to characterize masters and nonmasters using background variables to develop specific
Physical aggression during early childhood: Trajectories and predictors
 Pediatrics
, 2004
"... Introduction: This study aimed to identify the trajectories of physical aggression during early childhood and antecedents of high levels of physical aggression early in life. Methods: 572 families with a 5monthold newborn were recruited. Assessments of physical aggression frequency were obtained f ..."
Abstract

Cited by 70 (9 self)
 Add to MetaCart
Introduction: This study aimed to identify the trajectories of physical aggression during early childhood and antecedents of high levels of physical aggression early in life. Methods: 572 families with a 5monthold newborn were recruited. Assessments of physical aggression frequency were obtained from mothers
Number Sense Growth in Kindergarten: A Longitudinal Investigation of Children at Risk for Mathematics Difficulties
 Child Development
, 2006
"... Number sense development of 411 middle and lowincome kindergartners (mean age 5.8 years) was examined over 4 time points while controlling for gender, age, and reading skill. Although lowincome children performed significantly worse than middleincome children at the end of kindergarten on all ta ..."
Abstract

Cited by 69 (6 self)
 Add to MetaCart
Number sense development of 411 middle and lowincome kindergartners (mean age 5.8 years) was examined over 4 time points while controlling for gender, age, and reading skill. Although lowincome children performed significantly worse than middleincome children at the end of kindergarten on all tasks, both groups progressed at about the same rate. An exception was story problems, on which the lowincome group achieved at a slower rate; both income groups made comparable progress when the same problems were presented nonverbally with visual referents. Holding other predictors constant, there were small but reliable gender effects favoring boys on overall number sense performance as well as on nonverbal calculation. Using growth mixture modeling, 3 classes of growth trajectories in number sense emerged. Mathematics difficulties are widespread in the United States as well as in other industrialized nations. The consequences of such difficulties are serious and can be felt into adulthood (Dougherty, 2003; Murnane, Willett, & Levy, 1995). Low math achievement is especially pronounced in students from lowincome households (National Assessment of Educational Progress, 2004). Children with weaknesses in basic arithmetic may not develop the conceptual structures required to support the learning of advanced mathematics. Although competence in highlevel math serves as a gateway to a myriad careers in science and technology (Geary, 1994), many students never reach this stage. Some children gradually learn to avoid all things involving math and even develop math anxieties or phobias (Ash