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Structural equation model (SEM) trees, a combination of SEMs and decision trees, have been proposed as a data-analytic tool for theory-guided exploration of empirical data. With respect to a hypothesized model of multivariate outcomes, such trees recursively find subgroups with similar patterns of observed data. SEM trees allow for the automatic selection of variables that predict differences across individuals in specific theoretical models, for instance, differences in latent factor profiles or developmental trajectories. However, SEM trees are unstable when small variations in the data can result in different trees. As a remedy, SEM forests, which are ensembles of SEM trees based on resamplings of the original dataset, provide increased stability. Because large forests are less suitable for visual inspection and interpretation, aggregate measures provide researchers with hints on how to improve their models: (a) variable importance is based on random permutations of the out-of-bag (OOB) samples of the individual trees and quantifies, for each variable, the average reduction of uncertainty about the model-predicted distribution; and (b) case proximity enables researchers to perform clustering and outlier detection. We provide an overview of SEM forests and illustrate their utility in the context of cross-sectional factor models of intelligence and episodic memory. We discuss benefits and limitations, and provide advice on how and when to use SEM trees and forests in future research. [ABSTRACT FROM AUTHOR]

Objective: Deficits in emotion regulation (ER) skills are considered a putative maintaining factor for major depressive disorder (MDD) and hence a promising target in the treatment of MDD. However, to date, the association between the successful application of arguably adaptive ER skills and changes in depressive symptom severity (DSS) has yet to be investigated over the course of treatment. Thus, the primary aim of this study was to clarify reciprocal prospective associations between successful ER skills application and DSS over the course of inpatient cognitive behavioral therapy for MDD. Additionally, we explored whether such associations would differ across specific ER skills. Method: We assessed successful ER skills application and DSS 4 times during the first 3 weeks of treatment in 152 inpatients (62.5% women, average age 45.6 years) meeting criteria for MDD. We first tested whether successful skills application and depression were cross-sectionally associated by computing Pearson's correlations. Then, we utilized latent curve modeling to test whether changes in successful skills application were negatively associated with changes in DSS during treatment. Finally, we used latent change score models to clarify whether successful skills application would predict subsequent reduction of DSS. Results: Cross-sectionally, successful ER skills application was associated with lower levels of DSS at all assessment times, and an increase of successful skills application during treatment was associated with a decrease of DSS. Moreover, successful overall ER skills application predicted subsequent changes in DSS (but not vice versa). Finally, strength of associations between successful application and DSS differed across specific ER skills. Among a broad range of potentially adaptive skills, only the abilities to tolerate negative emotions and to actively modify undesired emotions were significantly associated with subsequent improvement in DSS. Conclusions: Systematically enhancing health-relevant ER skills with specific interventions may help reduce DSS in patients suffering from MDD. [ABSTRACT FROM AUTHOR]

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model. [ABSTRACT FROM AUTHOR]

The authors use multiple-sample longitudinal data from different test batteries to examine propositions about changes in constructs over the life span. The data come from 3 classic studies on intellectual abilities in which, in combination, 441 persons were repeatedly measured as many as 16 times over 70 years. They measured cognitive constructs of vocabulary and memory using 8 age-appropriate intelligence test batteries and explore possible linkage of these scales using item response theory (IRT). They simultaneously estimated the parameters of both IRT and latent curve models based on a joint model likelihood approach (i.e., NLMIXED and WINBUGS). They included group differences the model to examine potential interindividual differences in levels and change. The resulting longitudinal invariant Rasch test analyses lead to a few new methodological suggestions for dealing with repeated constructs based on changing measurements in developmental studies. [ABSTRACT FROM AUTHOR]

The authors analyzed longitudinal data from a cognitive training experiment—Advanced Cognitive Training for Independent and Vital Elderly—using several alternative contemporary statistical models to test dynamic hypotheses based on latent change scores. The analyses focused on pretest and posttest data for only the group who received Reasoning training compared with the No-Contact (control) group. The initial structural equation modeling (SEM) path model isolated several training effects and an important source of transfer of training, Near→Far, but this transfer was not increased due to training. The subsequent models, which accounted for pretest differences and latent changes, implied that only the Near measurements were influenced by training, and the change transfer was small, Introduction of common factors for both Near and Far measurements showed the factor patterns were unaffected by training or time and suggested training was a broader effect than in any single variable. The bivariate analysis of common factors did not appear to alter the previous results. Addition of demographic covariates and latent mixture analysis of the trained group led to further results. The uses of contemporary SEMs with experimental data are discussed. [ABSTRACT FROM AUTHOR]

The authors applied linear dynamic models to longitudinal data to examine the dynamics of reading and cognition from 1st to 12th grade. They used longitudinal data (N = 445) from the Connecticut Longitudinal Study (S. E. Shaywitz, B. A. Shaywitz, J. M. Fletcher, & M. D. Escobar, 1990) to map the dynamic interrelations of various scales of the Wechsler Intelligence Scales for Children-Revised (i.e., Full, Performance, and Verbal) and specific markers of the Woodcock-Johnson Psycho-Educational Battery-Revised reading cluster (i.e., Letter-Word ID, Decoding, and Comprehension). The results of these analyses indicate that (a) there is a positive dynamic relation between reading and cognition across the selected age range; (b) this dynamic relation is symbiotic, with positive influences in both directions; (c) the influence from cognition to reading is stronger when considering the Wechsler Intelligence Scales for Children Performance-Revised Performance scale and weaker with the Verbal scale; (d) when examining the different Reading subtests, the influences from cognition are more apparent for Letter- Word ID and Comprehension and are less perceptible for Decoding; and (e) the dynamics of reading and cognition appear to be of stronger magnitude during 1st to 3rd grade, less strong during 4th to 8th grade, and weaker from 9th to 12th grade. [ABSTRACT FROM AUTHOR]

Bivariate dual change score models were applied to longitudinal data from the Swedish Adoption/Twin Study of Aging to compare the dynamic predictions of 2-component theories of intelligence and the processing speed theory of cognitive aging. Data from up to 5 measurement occasions covering a 16-year period were available from 806 participants ranging in age from 50 to 88 years at the first measurement wave. Factors were generated to tap 4 general cognitive domains: verbal ability, spatial ability, memory, and processing speed. Model fitting indicated no dynamic relationship between verbal and spatial factors, providing no support for the hypothesis that age changes in fluid abilities drive age changes in crystallized abilities. The results suggest that, as predicted by the processing speed theory of cognitive aging, processing speed is a leading indicator of age changes in memory and spatial ability, but not verbal ability. [ABSTRACT FROM AUTHOR]

The present study was conducted to better describe age trends in cognition among older adults in the longitudinal Health and Retirement Study (HRS) from 1992 to 2004 (N > 17,000). The authors used contemporary latent variable models to organize this information in terms of both cross-sectional and longitudinal inferences about age and cognition. Common factor analysis results yielded evidence for at least 2 common factors, labeled Episodic Memory and Mental Status, largely separable from vocabulary. Latent path models with these common factors were based on demographic characteristics. Multilevel models of factorial invariance over age indicated that at least 2 common factors were needed. Latent curve models of episodic memory were based on age at testing and showed substantial age differences and age changes, including impacts due to retesting as well as several time-invariant and time-varying predictors. [ABSTRACT FROM AUTHOR]