Generalized Network Psychometrics: Combining Network and Latent Variable Models

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of Structural Equation Modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework Latent Network Modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance-covariance structure of indicators is modeled as a network. We term this generalization Residual Network Modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms performs adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.Comment: Published in Psychometrik

Two-Method Planned Missing Designs for Longitudinal Research

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a “gold standard” that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based) measure that contains systematic measurement bias (e.g., response bias). Using simulated data on four measurement occasions, we compared the cost-efficiency and validity of longitudinal designs where the gold standard is measured at one or more measurement occasions. We manipulated the nature of the response bias over time (constant, increasing, fluctuating), the factorial structure of the response bias over time, and the constraints placed on the latent variable model. Our results showed that parameter bias is lowest when the gold standard is measured on at least two occasions. When a multifactorial structure was used to model response bias over time, it is necessary to have the “gold standard” measures included at every time point, in which case most of the parameters showed low bias. Almost all parameters in all conditions displayed high relative efficiency, suggesting that the 2-method design is an effective way to reduce costs and improve both power and accuracy in longitudinal research

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg

Correlated longitudinal changes across linguistic, achievement, and psychomotor domains in early childhood: Evidence for a global dimension of development

This is the peer reviewed version of the following article: Rhemtulla, M., & Tucker-Drob, E. M. (2011). Correlated longitudinal changes across linguistic, achievement, and psychomotor domains in early childhood: Evidence for a global dimension of development. Developmental Science, 14(5), 1245–1254. http://doi.org/10.1111/j.1467-7687.2011.01071.x, which has been published in final form at http://doi.org/10.1111/j.1467-7687.2011.01071.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.An important question within developmental psychology concerns the extent to which the maturational gains that children make across multiple diverse domains of functioning can be attributed to global (domain-general) developmental processes. The present study investigated this question by examining the extent to which individual differences in change across children’s development in five different domains are correlated. Multivariate growth-curve models were fit to longitudinal data on linguistic, mathematics, reading, gross motor, and fine motor skills in 8,950 children ranging in age from 44 to 86 months (3.7 years to 7.2 years). All five rates of change were positively intercorrelated. A common factor accounted for 42% of the individual differences in change. These results suggest that a global dimension underlies substantial proportions of cognitive and psychomotor development

Gene-by-Socioeconomic Status Interaction on School Readiness

In previous work with a nationally representative sample of over 1,400 monozygotic and dizygotic twins born in the US, Tucker-Drob et al. (Psychological Science, 22, 125–133, 2011) uncovered a gene × environment interaction on scores on the Bayley Short Form test of mental ability (MA) at 2 years of age—higher socioeconomic status (SES) was associated not only with higher MA, but also with larger genetic contributions to individual differences in MA. The current study examined gene × SES interactions in mathematics skill and reading skill at 4 years of age (preschool age) in the same sample of twins, and further examined whether interactions detected at 4 years could be attributed to the persistence of the interaction previously observed at 2 years. For early mathematics skill but not early reading skill, genetic influences were more pronounced at higher levels of SES. This interaction was not accounted for by the interaction observed at 2 years. These findings indicate that SES moderates the etiological influences on certain cognitive functions at multiple stages of child development

Small montane cloud forest fragments are important for conserving tree diversity in the Ecuadorian Andes

Montane tropical cloud forests, with their complex topography, biodiversity, high numbers of endemic species, and rapid rates of clearing, are a top global conservation priority. However, species distributions at local and landscape scales in cloud forests are still poorly understood, in part because few regions have been surveyed. Empirical work has focused on species distributions along elevation gradients, but spatial variation among forests at the same elevation is less commonly investigated. In this study, the first to compare tree communities across multiple Andean cloud forests at similar elevations, we surveyed trees in five ridge‐top forest reserves at the upper end of the ‘mid‐elevation diversity bulge’ (1900–2250 masl) in the Intag Valley, a heavily deforested region in the Ecuadorian Andes. We found that tree communities were distinct in reserves located as close as 10 to 35 km apart, and that spatially closer forests were not more similar to one another. Although larger (1500 to 6880 ha), more intact forests contained significantly more tree species (108–120 species/0.1 ha) than smaller (30 to 780 ha) ones (56–87 species/0.1 ha), each reserve had unique combinations of more common species, and contained high proportions of species not found in the others. Results thus suggest that protecting multiple cloud forest patches within this narrow elevational band is essential to conserve landscape‐level tree diversity, and that even small forest reserves contribute significantly to biodiversity conservation. These findings can be applied to create management plans to conserve and restore cloud forests in the Andes and tropical montane cloud forests elsewhere.RESUMENLos bosques nublados tropicales montanos, con su compleja topografía, biodiversidad, alto número de especies endémicas, y altas tasas de tala de bosque son una prioridad de conservación global. Sin embrago, la distribución de especies a escala local y de paisaje en bosques nublados es aun pobremente entendida, en parte porque pocas regiones han sido muestreadas. Estudios empíricos se han enfocado en la distribución de especies a lo largo de gradientes altitudinales, pero la variación espacial entre bosques ubicados a la misma elevación ha sido comúnmente menos investigada. En este estudio, el primero en comparar comunidades de árboles a lo largo de múltiples bosques nublados Andinos en elevaciones similares, muestreamos árboles en cinco reservas de bosque de cresta de montaña en el límite superior del pico de diversidad de elevaciones intermedias (‘mid‐elevation diversity bulge’) (1900–2250 m.s.n.m.) en el Valle de Intag, una región altamente deforestada en los Andes ecuatorianos. Encontramos que las comunidades de árboles fueron distintas en reservas tan cercanas como de 10 a 35 km de distancia entre sí, y que bosques espacialmente más cercanos no fueron más similares entre ellos. Aunque bosques extensos más intactos contuvieron significativamente más especies de árboles (108–120 especies/0.1 ha) que los de menor tamaño (56–87 especies/0.1 ha), cada reserva tuvo una combinación única de especies comunes, y una alta proporción de especies no encontradas en las otras reservas. Los resultados sugieren que proteger múltiples parches de bosque nublado dentro de este estrecho rango altitudinal es esencial para conservar la diversidad de árboles a escala de paisaje, y que aun pequeñas reservas de bosque contribuyen significativamente a la conservación de la biodiversidad. Estos resultados se pueden aplicar para la creación de planes de manejo para conservación y restauración del bosque nublado en los Andes y bosques tropicales montanos en otros sitios.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144672/1/btp12542_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144672/2/btp12542.pd