Using Latent Class Analysis to Model Temperament Types

Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks was used for model selection. Results show at least three types of infant temperament, with patterns consistent with those identified by previous researchers who classified the infants using a theoretically based system. Multiple imputation of group memberships is proposed as an alternative to assigning subjects to the latent class with maximum posterior probability in order to reflect variance due to uncertainty in the parameter estimation. Latent class membership at four months of age predicted longitudinal outcomes at four years of age. The example illustrates issues relevant to all mixture models, including estimation, multi-modality, model selection, and comparisons based on the latent group indicators.     

Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span.

Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N approximately 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion "accelerated" data (e.g., J. J. McArdle & R. Q. Bell, 2000; J. J. McArdle & R. W. Woodcock, 1997). Nonlinear mixed-effects growth models based on a dual exponential rate yield a reasonable fit to all life span cognitive data. These results suggest that most broad cognitive functions fit a generalized curve that rises and falls. Novel multilevel models directly comparing growth curves show that broad fluid reasoning (Gf) and acculturated crystallized knowledge (Gc) have different growth patterns. In all comparisons, any model of cognitive age changes with only a single g factor yields an overly simplistic view of growth and change over age.     

Statistical power of latent growth curve models to detect quadratic growth

Latent curve models (LCMs) have been used extensively to analyze longitudinal data. However, little is known about the power of LCMs to detect nonlinear trends when they are present in the data. For this study, we utilized simulated data to investigate the power of LCMs to detect the mean of the quadratic slope, Type I error rates, and rates of nonconvergence during the estimation of quadratic LCMs. Five factors were examined: the number of time points, growth magnitude, interindividual variability, sample size, and the R ²s of the measured variables. The results showed that the empirical Type I error rates were close to the nominal value of 5 %. The empirical power to detect the mean of the quadratic slope was affected by the simulation factors. Finally, a substantial proportion of samples failed to converge under conditions of no to small variation in the quadratic factor, small sample sizes, and small R ² of the repeated measures. In general, we recommended that quadratic LCMs be based on samples of (a) at least 250 but ideally 400, when four measurement points are available; (b) at least 100 but ideally 150, when six measurement points are available; (c) at least 50 but ideally 100, when ten measurement points are available.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.     

Cross-sectional and longitudinal confirmatory factor models for the german test anxiety inventory: A construct validation

Construct validity of the German Test Anxiety Inventory (TAI-G) was tested in two respects. Firstly, the purported four-dimensional structure of the TAI-G (comprising subscales Emotionality, Worry, Interference, and Lack of Confidence) as well as relations of the test anxiety dimensions to self-efficacy were tested. Secondly, the trait conception of the TAI-G was tested within the framework of Latent State-Trait theory. The TAI-G was given to a student sample (N=302) on three occasions with a time interval of 2 weeks along with a study-specific self-efficacy scale on occasion 1. Dimensionality assumptions as well as relations with self-efficacy were tested using cross-sectional second-order confirmatory factor analysis. The trait conception was tested separately for TAI-G subscales by specifying longitudinal confirmatory factor models (Latent State-Trait models) and by calculating variance proportions of manifest variables (Latent State-Trait coefficients) referring to different sources of systematic variance (person, situation, and method) based on parameter estimates of the models. Results were supportive of both the purported four-dimensional structure and hypothesized relationships to self-efficacy (i.e., acceptable model fit) as well as of the trait conception of test anxiety (i.e., acceptable model fit and high proportion of variance due to person component). Implications for further validation studies were discussed.     

The Canberra Longitudinal Study: Design,Aims, Methodology,Outcomes and Recent Empirical Investigations

The first section of this paper describes methodology and major cognitive outcomes of the Canberra Longitudinal Study (CLS). This community study of 1045 Australians aged 70 years or over commenced in 1990. Participants were reassessed on three subsequent occasions. Its major themes were investigations of prevalence of dementia and depression, risk factors, inter-individual variability and instrument development. Over 60 papers arising from the study have been published.

The second section of this paper describes the use of a Factor of Curves (FOC) latent growth model to examine the existence of a common factor responsible for age-related deterioration in cognitive and non-cognitive processes. This analysis is a logical progression in a series of investigations using the Canberra Longitudinal Study into risk factors and correlates of cognitive change using structural equation modeling techniques. The FOC model is described and is used to explore the nature of an hypothesized common factor and to determine its relationship with age, gender, education, pre-morbid intelligence and to the ApoE genotype. Latent growth models were developed for each of reaction time, Symbol Letter Modalities Test (SLMT), Grip strength, self-reported Sensory disability and memory from three waves of data. Second-order latent level slope factors were established based on the individual factor growth curve models. Although a common factor model could be fitted to the data, there is little support that it represents a single common cause.     

Individual differences in the development of sensation seeking and impulsivity during adolescence: further evidence for a dual systems model

Consistent with social neuroscience perspectives on adolescent development, previous cross-sectional research has found diverging mean age-related trends for sensation seeking and impulsivity during adolescence. The present study uses longitudinal data on 7,640 youth from the National Longitudinal Study of Youth Children and Young Adults, a nationally representative sample assessed biennially from 1994 to 2006. Latent growth curve models were used to investigate mean age-related changes in self-reports of impulsivity and sensation seeking from ages 12 to 24 years, as well individual differences in these changes. Three novel findings are reported. First, impulsivity and sensation seeking showed diverging patterns of longitudinal change at the population level. Second, there was substantial person-to-person variation in the magnitudes of developmental change in both impulsivity and sensation seeking, with some teenagers showing rapid changes as they matured and others maintaining relatively constant levels with age. Finally, the correlation between age-related changes in impulsivity and sensation seeking was modest and not significant. Together, these results constitute the first support for the dual systems model of adolescent development to derive from longitudinal behavioral data.     

Only time will tell: cross-sectional studies offer no solution to the age-brain-cognition triangle: comment on Salthouse (2011)

Salthouse (2011) critically reviewed cross-sectional and longitudinal relations among adult age, brain structure, and cognition (ABC) and identified problems in interpretation of the extant literature. His review, however, missed several important points. First, there is enough disparity among the measures of brain structure and cognitive performance to question the uniformity of B and C vertices of the ABC triangle. Second, age differences and age changes in brain and cognition are often nonlinear. Third, variances and correlations among measures of brain and cognition frequently vary with age. Fourth, cross-sectional comparisons among competing models of ABC associations cannot disambiguate competing hypotheses about the structure and the range of directed and reciprocal relations between changes in brain and behavior. We offer the following conclusions, based on these observations. First, individual differences among younger adults are not useful for understanding the aging of brain and behavior. Second, only multivariate longitudinal studies, age-comparative experimental interventions, and a combination of the two will deliver us from the predicaments of the ABC triangle described by Salthouse. Mediation models of cross-sectional data represent age-related differences in target variables but fail to approximate time-dependent relations; thus, they do not elucidate the dimensions and dynamics of cognitive aging.     

Preschools reduce early academic-achievement gaps: a longitudinal twin approach

Preschools may reduce inequalities in early academic achievement by providing children from disadvantaged families with higher-quality learning environments than they would otherwise receive. In this study, longitudinal data from a nationally representative sample of more than 600 twin pairs were used to estimate the contributions of genes, the shared environment, and the nonshared environment to cognition and achievement scores in children enrolled versus not enrolled in preschool. Attending preschool at age 4 was associated with reductions in shared environmental influences on reading and math skills at age 5, but was not associated with the magnitude of shared environmental influences on cognition at age 2. These prospective effects were mediated by reductions in achievement gaps associated with minority status, socioeconomic status, and ratings of parental stimulation of cognitive development. Lower socioeconomic status was associated with lower rates of preschool enrollment, which suggests that the very children who would benefit most from preschools are the least likely to be enrolled in them.     

Longitudinal Patterns of Self-Regulation among Ethnic Minority Children Facing Poverty

Early poverty is associated with a cumulative load of family and community risk factors that can impact the development of self-regulatory abilities and result in socio-emotional and achievement gaps which begin early and persist across the lifespan. Ethnic minorities are disproportionately represented among children living in poverty. The longitudinal trajectories of self-regulation are important to understand in this population, in order to best inform prevention efforts. This study examines patterns of self-regulation over time among young, ethnic minority children living in low income, urban households. A stratified, random sample of 555 children, ages 2 to 4 years, (46% Black, 46% Hispanic; 47% female) were followed over three waves (including 1 and 5 year follow-ups). Internalizing and externalizing behaviors at approximately age nine were predicted by children’s early self-regulation. Latent class analyses revealed low, medium, and high levels of self-regulatory abilities at wave 1 (mean age: 2.99, SD?=?.81) and low and high levels, 1 year later (mean age: 4.39 (SD?=?.94). A gender effect was found whereby girls were more likely than boys to be in the high self-regulation class relative to the low at both waves. Using Latent Transition Analysis, distal outcomes were examined approximately 5 years after the initial assessment (mean age: 8.83, SD?=?.93). Children who sustained a higher level of self-regulation over time had the lowest internalizing and externalizing behaviors. Transition to low self-regulation at wave 2, regardless of initial self-regulation status, was related to greater severity of internalizing symptoms. Implications for prevention and future research are discussed.     

Modeling Intraindividual Cognitive Change in Aging Adults: Results from the Einstein Aging Studies

This paper reviews research from the longitudinal Einstein Aging Studies (EAS) that has focused on modeling intraindividual cognitive change in aging adults. We describe how to separate models of intraindividual change at the within-person level from models of individual differences in change at the between-person level. We illustrate this distinction by analyzing new data from the EAS to test the speed hypothesis at the intraindividual level of analyses. The present findings replicate those of Sliwinski and Buschke (1999) by showing that within-person changes in speed predict within-person changes in cognition, but that speed does not substantially attenuate estimates of within-person cognitive decline. We conclude that correct measurement and explanatory modeling of intraindividual change should be the primary focus of longitudinal aging research, and that the more common practice of modeling individual differences in change should be a secondary focus.     

Developmental paths of psychological health from early adolescence to later adulthood

Developmental paths of psychological health were examined for 236 participants of the Berkeley Growth Study, the Berkeley Guidance Study, and the Oakland Growth Study. A clinician-reported aggregate index, the Psychological Health Index (PHI), based on California Q-Sort ratings, was created for subsets of participants at 14, 18, 30, 40, 50, and 62 years of age. Latent curve analysis was then used to explicate the life span development of psychological health. Psychological health development could be successfully modeled via 2 piecewise latent growth curves. Psychological health appears to be stable in adolescence and to steadily increase from 30 to 62 years of age. A moderately strong positive correlation between the 2 developmental curves indicates that those with greater psychological health in adolescence show more improvement in adult psychological health tend to also. Results illustrate the value of the PHI and the power of latent curve analysis to explicate longitudinal stability and change.     

Self‐Regulation and Personality Profiles: Empirical Development,Longitudinal Stability and Predictive Ability

We used Latent Profile and Latent Profile Transition Analysis to empirically develop and compare competing models of personality profiles (three‐ and four‐profile models). We do so using data from the New Zealand Attitudes and Values Study, a large longitudinal national probability sample of New Zealanders. Both three‐ and four‐profile solutions demonstrated good fit and longitudinal stability. Trait configurations and predictive outcomes of the four‐profile model were the most interpretable in terms of the theoretical literature, as this solution mirrored the theoretical foundation of self‐regulatory ego‐constructs. This supported the interpretation of a four‐profile model as providing a useful distinction over and above the three‐profile model. We conclude that, compared to the three‐profile model, the four‐profile solution provides a better foundation to serve as a complementary approach to variable‐centre research. Copyright © 2016 European Association of Personality Psychology     

A meta-analytic approach to growth curve analysis

A meta-analytic approach to growth curve analysis is described and illustrated by applying it to the evaluation of the Arizona Pilot Project, an experimental project for financing the treatment of the severely mentally ill. In this approach to longitudinal data analysis, each individual subject for which repeated measures are obtained is initially treated as a separate case study for analysis. This approach has at least two distinct advantages. First, it does not assume a balanced design (equal numbers of repeated observations) across all subjects; to accommodate a variable number of observations for each subject, individual growth curve parameters are differentially weighted by the number of repeated measures on which they are based. Second, it does not assume homogeneity of treatment effects (equal slopes) across all subjects. Individual differences in growth curve parameters representing potentially unequal developmental rates through time are explicitly modeled. A meta-analytic approach to growth curve analysis may be the optimal analytical strategy for longitudinal studies where either (1) a balanced design is not feasible or (2) an assumption of homogeneity of treatment effects across all individuals is theoretically indefensible. In our evaluation of the Arizona Pilot Project, individual growth curve parameters were obtained for each of the 13 rationally derived subscales of the New York Functional Assessment Survey, over time, by linear regression analysis. The slopes, intercepts, and residuals obtained for each individual were then subjected to meta-analytic causal modeling. Using factor analytic models and then general linear models for the latent constructs, the growth curve parameters of all individuals were systematically related to each other via common factors and predicted based on hypothesized exogenous causal factors. The same two highly correlated common factors were found for all three growth curve parameters analyzed, a general psychological factor and a general functional factor. The factor patterns were found to be nearly identical across the separate analyses of individual intercepts, slopes, and residuals. Direct effects on the unique factors of each subscale of the New York Functional Assessment Survey were tested for each growth curve parameter by including the common factors as hierarchically prior predictors in the structural model for each of the indicator variables, thus statistically controlling for any indirect effect produced on the indicator through the common factors. The exogenous predictors modeled were theoretically specified orthogonal contrasts for Method of Payment (comparing Arizona Pilot Project treatment or "capitation" to traditional or "fee-for-service" care as a control), Treatment Administration Site (comparing various locations within treatment or control groups), Pretreatment Assessment (comparing general functional level at intake as assigned by an Outside Assessment Team), and various interactions among these main effects. The intercepts, representing the initial status of individual subjects on both the two common factors and the 13 unique factors of the subscales of the New York Functional Assessment Survey, were found to vary significantly across many of the various different treatment conditions, treatment administration sites, and pretreatment functional levels. This indicated a severe threat to the validity of the originally intended design of the Arizona Pilot Project as a randomized experiment. When the systematic variations were statistically controlled by including intercepts as hierarchically prior predictors in the structural models for slopes, recasting the experiment as a nonequivalent groups design, the effects of the intercepts on the slopes were found to be both statistically significant and substantial in magnitude. (ABSTRACT TRUNCATED)     

A Longitudinal Investigation of Egoistic and Moralistic Self-Enhancement

This study used a 3-wave longitudinal design to investigate stability and change of egoistic and moralistic self-enhancement. Participants (n = 195) were followed for 6 years, from late adolescence (age 15) to emerging adulthood (age 21). A significant degree of rank-order stability was found that reveals a reasonable continuity in the individuals’ relative standing on measures of egoistic and moralistic self-enhancement. Latent growth curve analysis was used to track the developmental trajectories of the examined constructs. Findings revealed that egoistic self-enhancement is stable from 15 to 21 years, whereas moralistic self-enhancement slightly decreases during the same developmental period. Cross-lagged models were used to examine the reciprocal relations between self-enhancement and the Big Five personality traits. No prospective effects were found between the constructs, which develop independently from late adolescence to young adulthood, showing only synchronous associations. Implications for personality research and assessment are discussed.     

Parent–child interaction,task-oriented regulation,and cognitive development in toddlers facing developmental risks

The importance of supportive parent–child interactions and the development of regulatory abilities with regard to growth in children's cognitive abilities has been established. This study investigated the longitudinal relations among parental supportiveness, child task-oriented regulation, and cognitive development for low-income children facing developmental risks. Interlocking growth models were fit to these variables based on measurements made when the children were 14, 24, and 36 months of age. Results showed that growth rates of child cognition were related to baseline (14 months) parental supportiveness and changes in child task-oriented regulation. We additionally found that baseline levels of cognition were related to the baseline levels of child task-oriented regulation. This study suggests that both parental supportiveness during infancy and the concurrent development of child task-oriented regulation are related to cognitive development among children facing developmental risks.     

Verbal and nonverbal predictors of executive function in early childhood

The study of executive function (EF) has become increasingly popular in multiple areas of research. A wealth of evidence has supported the value of EF in shaping notable outcomes across typical and atypical development; however, little evidence has supported the cognitive contributors to early EF development. The current study used data from a large longitudinal sample of healthy children to investigate the differential influence of verbal and nonverbal cognition on later EF. Participants were assessed at 2 years of age using the Mullen Scales of Early Learning, and Mullen scores were used to calculate nonverbal and verbal developmental quotients. Executive function was measured at 6 years using assessments from the Stanford-Binet, Cambridge Neuropsychological Test Automated Battery, and the Behavior Rating Inventory of Executive Function. Results suggested that early nonverbal cognition was a better predictor of 6-year EF as measured by task-based laboratory assessments, whereas verbal cognition was a better predictor of parent-reported EF. Findings are discussed in regard to EF development and characteristics of EF measurement.     

What needs to be explained to account for age-related effects on multiple cognitive variables?

Structural equation methodology was used to investigate age-related influences across a number of cognitive variables in 204 adults ranging from 18 to 91 years of age with a hierarchical structure that contained 4 1st-order factors and 1 2nd-order common factor. Direct age relations were found to the common factor as well as to 1st-order speed and memory factors. Replicability of the findings was explored by investigating the same structure of age relations, using 2 different data sets, and a similar patten was found in each. These results suggest that at least 3 statistically distinct types of age-related influences are operating on a wide variety of cognitive variables and presumably require separate explanatory mechanisms.     

Developing Depressive Symptoms over the Adolescent Years: The Influence of Affiliated Cultural Values among Taiwanese Youth

The purpose of this study was (a) to identify latent subgroups of Taiwanese adolescents who vary in their cultural value affiliations and (b) to examine how latent-subgroup membership in early adolescence predicted depressive symptoms for 6 years throughout adolescence into young adulthood. Participants consisted of 2458 youth from the longitudinal Taiwan Youth Project (TYP). Latent profile analysis indicated five classes (patterns) of cultural value affiliation. A zero-inflated Poisson (ZIP) analysis followed, which identified the estimated (a) probability of experiencing depressive symptoms and (b) number of depressive symptoms experienced by individuals who reported depressive symptoms across the five observational time points. Results showed that among the five classes of value affiliation, two classes had a greater likelihood of experiencing depressive symptoms at the beginning of the assessment (age 15). Youth who were least likely to embrace societally prescribed culture values were at the greatest risk for manifesting subsequent depressive symptoms. These findings support the idea that the way adolescents identify with their cultural values predicts subsequent depressive symptoms.     

Heritable variation for latent inhibition and its correlation with reversal learning in honeybees (Apis mellifera)

Latent inhibition (LI) in honeybees (Apis mellifera) was studied by using a proboscis extension response conditioning procedure. Individual queens, drones, and workers differed in the degree to which they revealed LI. The authors hypothesized that individual differences would have a substantial genetic basis. Two sets of progeny were established by crossing virgin queens and individual drones, both of which had been selected for differential expression of inhibition. LI was stronger in the progeny from the queens and drones that had shown greater inhibition. The expression of LI was also dependent on environmental factors that are most likely associated with age, foraging experience outside of the colony, or both. Furthermore, there was a correlated response in the speed at which progeny reversed a learned discrimination of 2 odors. These genetic analyses may reveal underlying mechanisms that these 2 learning paradigms have in common.