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.

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.

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This study investigates the effects of fitness changes on hippocampal microstructure and hippocampal volume. Fifty-two healthy participants aged 59-74years with a sedentary lifestyle were randomly assigned to either of two levels of exercise intensity. Training lasted for six months. Physical fitness, hippocampal volumes, and hippocampal microstructure were measured before and after training. Hippocampal microstructure was assessed by mean diffusivity, which inversely reflects tissue density; hence, mean diffusivity is lower for more densely packed tissue. Mean changes in fitness did not differ reliably across intensity levels of training, so data were collapsed across groups. Multivariate modeling of pretest-posttest differences using structural equation modeling (SEM) revealed that individual differences in latent change were reliable for all three constructs. More positive changes in fitness were associated with more positive changes in tissue density (i.e., more negative changes in mean diffusivity), and more positive changes in tissue density were associated with more positive changes in volume. We conclude that fitness-related changes in hippocampal volume may be brought about by changes in tissue density. The relative contributions of angiogenesis, gliogenesis, and/or neurogenesis to changes in tissue density remain to be identified.

Although we were unable to address potential racial and ethnic disparities in screening for substances at birth, we found no evidence that racial disparities in infant CPS reports arise from variable responses to prenatal substance exposure.