Latent Growth Models with Floors,Ceilings, and Random Knots

In longitudinal/developmental studies, individual growth trajectories are sometimes bounded by a floor at the beginning of the observation period and/or a ceiling toward the end of the observation period (or vice versa), resulting in inherently nonlinear growth patterns. If the trajectories between the floor and ceiling are approximately linear, such longitudinal growth patterns can be described with a linear piecewise (spline) model in which segments join at knots. In these scenarios, it may be of specific interest for researchers to examine the timing when transition occurs, and in some occasions also to examine the levels of the floors and/or ceilings if they are not known and fixed. In the current study, we propose a reparameterized piecewise latent growth curve model so that a direct estimation of the random knots (and, if needed, a direct estimation of random floors and ceilings) is possible. We derive the model reparameterization using a 4-step structured latent curve modeling approach. We provide two illustrative examples to demonstrate how the proposed reparameterized models can be fitted to longitudinal growth data using the popular SEM software Mplus and we supply the full coding for applied researchers’ reference.     

Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical Ordinal Item Response Data

When categorical ordinal item response data are collected over multiple timepoints from a repeated measures design, an item response theory (IRT) modeling approach whose unit of analysis is an item response is suitable. This study proposes a few longitudinal IRT models and illustrates how a popular compensatory multidimensional IRT model can be utilized to formulate such longitudinal IRT models, which permits an investigation of ability growth at both individual and population levels. The equivalence of an existing multidimensional IRT model and those longitudinal IRT models is also elaborated so that one can make use of an existing multidimensional IRT model to implement the longitudinal IRT models.     

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.     

Analyzing temporal patterns of infant sleep and negative affective behavior: a comparison between different statistical models

Objective

Variability in infant sleep and negative affective behavior (NAB) is a developmental phenomenon that has long been of interest to researchers and clinicians. However, analyses and delineation of such temporal patterns were often limited to basic statistical approaches, which may prevent adequate identification of meaningful variation within these patterns. Modern statistical procedures such as additive models may detect specific patterns of temporal variation in infant behavior more effectively.

Method

Hundred and twenty-one mothers were asked to record different behaviors of their 4–44 weeks old healthy infants by diaries for three days consecutively. Circadian patterns as well as individual trajectories and day-to-day variability of infant sleep and NAB were modeled with generalized linear models (GLMs) including a linear and quadratic polynomial for time, a GLM with a polynomial of the 8th order, a GLM with a harmonic function, a generalized linear mixed model (GLMM) with a polynomial of the 8th order, a generalized additive model, and a generalized additive mixed model (GAMM).

Results

The semi-parametric model GAMM was found to fit the data of infant sleep better than any other parametric model used. GLMM with a polynomial of the 8th order and GAMM modeled temporal patterns of infant NAB equally well, although the GLMM exhibited a slightly better model fit while GAMM was easier to interpret. Besides the well-known evening clustering in infant NAB we found a significant second peak in NAB around midday that was not affected by the constant decline in the amounts of NAB across the 3-day study period.

Conclusion

Using advanced statistical procedures (GAMM and GLMM) even small variations and phenomena in infant behavior can be reliably detected. Future studies investigating variability and temporal patterns in infant variables may benefit from these statistical approaches.     

A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision‐making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies—that the heritability of cognitive ability is based on cognitive processing speed—may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.     

个人成长主动性的概念、测量及影响

个人成长主动性是个体有意识地提升和完善自己的倾向, 包括认知倾向和行为倾向两个方面, 由对改变的准备、计划性、利用资源和主动的行为四个维度构成。研究表明, 人格、家庭功能影响到个体的个人成长主动性。同时, 个人成长主动性对个体的心理健康和职业认同等具有显著的影响。然而, 纵观已有个人成长主动性的研究, 有关个人成长主动性的结构、研究样本、文化差异、影响因素等方面还有待深入探讨。     

Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation.     

Models for the statistics and mechanisms of response speed and accuracy

Van Breukelen offers a promising method for modeling both response speed and response accuracy. However, the underlying conception of both dependent measures is somewhat flawed, leading the author to conclude that the approach possesses limitations that, under revised assumptions, may not hold. The central misconception, and a set of related misconceptions, is addressed, and it is suggested that this approach holds a good deal of promise for application in the perceptual and cognitive sciences.     

Long-term efficacy of exercise across diagnostically heterogenous mental disorders and the mediating role of affect regulation skills

BackgroundExercise interventions are efficacious in reducing disorder-specific symptoms in various mental disorders. However, little is known about long-term transdiagnostic efficacy of exercise across heterogenous mental disorders and the potential mechanisms underlying treatment effects.MethodsPhysically inactive outpatients, with depressive disorders, anxiety disorders, insomnia or attention deficit hyperactivity disorder were randomized to a standardized 12-week exercise intervention, combining moderate exercise with behavior change techniques (BCTs) (n = 38), or a passive control group (n = 36). Primary outcome was global symptom severity (Symptom Checklist-90, SCL-90-R) and secondary outcomes were self-reported exercise (Physical Activity, Exercise, and Sport Questionnaire), exercise-specific affect regulation (Physical Activity-related Health Competence Questionnaire) and depression (SCL-90-R) assessed at baseline (T1), post-treatment (T2) and one year after post-treatment (T3). Intention-to-treat analyses were conducted using linear mixed models and structural equations modeling.ResultsFrom T1 to T3, the intervention group significantly improved on global symptom severity (d = −0.43, p = .031), depression among a depressed subsample (d = −0.62, p = .014), exercise (d = 0.45, p = .011) and exercise-specific affect regulation (d = 0.44, p = .028) relative to the control group. The intervention group was more likely to reveal clinically significant changes from T1 to T3 (p = .033). Increases in exercise-specific affect regulation mediated intervention effects on global symptom severity (ß = −0.28, p = .037) and clinically significant changes (ß = −0.24, p = .042).ConclusionsThe exercise intervention showed long-term efficacy among a diagnostically heterogeneous outpatient sample and led to long-lasting exercise behavior change. Long-term increases in exercise-specific affect regulation within exercise interventions seem to be essential for long-lasting symptom reduction.