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"Structural equation models"
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A long-term experiment to investigate the relationships between high school students' perceptions of mobile learning and peer interaction and higher-order thinking tendencies
In this study, a one-year program was conducted to investigate the relationships between students' perceptions of mobile learning and their tendencies of peer interaction and higher-order thinking in issue-based mobile learning activities. To achieve the research objective, a survey consisting of eight scales, namely, usability, continuity, adaptive content, collaboration, communication, problem-solving, critical thinking and creativity, was developed. A total of 658 students from 38 high schools in Taiwan filled in the questionnaire after experiencing issue-based mobile learning activities. From the exploratory and confirmatory factor analyses, it was found that the questionnaire had high reliability and validity. The structural equation model further revealed that the provision of adaptive content in the mobile learning had positive impacts on the students' tendency to interact with peers (i.e., collaboration and communication), which further affected their tendency to engage in higher-order thinking (i.e., problem-solving, critical thinking, and creativity). The findings of this study provide a good reference for researchers and school teachers who intend to promote mobile learning in school settings.
Journal Article
Social Causation Versus Health Selection in the Life Course: Does Their Relative Importance Differ by Dimension of SES?
A person's socioeconomic status (SES) can affect health (social causation) and health can affect SES (health selection). The findings for each of these pathways may depend on how SES is measured. We study (1) whether social causation or health selection is more important for overall health inequalities, (2) whether this differs between stages of the life course, and (3) between measures of SES. Using retrospective survey data from 10 European countries (SHARELIFE, n{\\thinspace}={\\thinspace}18,734), and structural equation models in a cross-lagged panel design, we determine the relative explanatory power of social causation and health selection through childhood, adulthood, and old age. We use three ways to measure SES: First, as a latent variable capturing different aspects of SES, second as material wealth, and third as occupational skill level. Between childhood and adulthood, social causation and health selection are equally important. In the transition from adulthood to old age, social causation becomes more important than health selection, making it the dominant mechanism in old age. The three measures of SES produce similar results. Only material wealth shows a stronger effect on health (between childhood and adulthood); it is also more affected by health (between adulthood and old age) than the other measures.
Journal Article
Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model is a directed acyclic graph describing the relationships between the variables. In Gaussian structural equation models with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes, assuming faithfulness.In this work, we prove full identifiability in the case where all noise variables have the same variance: the directed acyclic graph can be recovered from the joint Gaussian distribution.Our result has direct implications for causal inference: if the data follow a Gaussian structural equation model with equal error variances, then, assuming that all variables are observed, the causal structure can be inferred from observational data only. We propose a statistical method and an algorithm based on our theoretical findings.
Journal Article
Structural equations modeling: Fit Indices, sample size, and advanced topics
This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.
Journal Article
The diversity of benthic diatoms affects ecosystem productivity in heterogeneous coastal environments
The current decrease in biodiversity affects all ecosystems, and the impacts of diversity on ecosystem functioning need to be resolved. So far, marine studies about diversity–ecosystem productivity-relationships have concentrated on small-scale, controlled experiments, with often limited relevance to natural ecosystems. Here, we provide a real-world study on the effects of microorganismal diversity (measured as the diversity of benthic diatom communities) on ecosystem productivity (using chlorophyll a concentration as a surrogate) in a heterogeneous marine coastal archipelago. We collected 78 sediment cores at 17 sites in the northern Baltic Sea and found exceptionally high diatom diversity (328 observed species). We used structural equation models and quantile regression to explore relationships between diatom diversity and productivity. Previous studies have found contradictory results in the relationship between microorganismal diversity and ecosystem productivity, but we showed a linear and positive basal relationship between diatom diversity and productivity, which indicates that diatom diversity most likely forms the lowest boundary for productivity. Thus, although productivity can be high even when diatom diversity is low, high diatom diversity supports high productivity. The trait composition was more effective than taxonomical composition in showing such a relationship, which could be due to niche complementarity. Our results also indicated that environmental heterogeneity leads to substantial patchiness in the diversity of benthic diatom communities, mainly induced by the variation in sediment organic matter content. Therefore, future changes in precipitation and river runoff and associated changes in the quality and quantity of organic matter in the sea, will also affect diatom communities and, hence, ecosystem productivity. Our study suggests that benthic microorganisms are vital for ecosystem productivity, and together with the substantial heterogeneity of coastal ecosystems, they should be considered when evaluating the potential productivity of coastal areas.
Journal Article
Stress, Sleep, and Coping Self-Efficacy in Adolescents
Adults are thought to show a sleep-stress spiral in which greater stress worsens sleep quality, which amplifies stress, which leads to worse sleep. This study examined whether adolescents show a similar spiral, and if so, whether coping self-efficacy—believing one can cope with stress—interrupts the spiral. Temporal dynamics of perceived stress, sleep quality, and coping self-efficacy were tracked in 381 9th graders (49% female, mean age 14.43, age range 14–16) using daily surveys across two school weeks (3184 observations). Though expected associations were evident between individuals, only a unidirectional path was found within individuals from sleep quality to perceived stress via coping self-efficacy. This challenges the conventional bidirectional understanding of sleep-stress relations and suggests coping self-efficacy as an intervention target.
Journal Article
CAM: CAUSAL ADDITIVE MODELS, HIGH-DIMENSIONAL ORDER SEARCH AND PENALIZED REGRESSION
We develop estimation for potentially high-dimensional additive structural equation models. A key component of our approach is to decouple order search among the variables from feature or edge selection in a directed acyclic graph encoding the causal structure. We show that the former can be done with nonregularized (restricted) maximum likelihood estimation while the latter can be efficiently addressed using sparse regression techniques. Thus, we substantially simplify the problem of structure search and estimation for an important class of causal models. We establish consistency of the (restricted) maximum likelihood estimator for low- and high-dimensional scenarios, and we also allow for misspecification of the error distribution. Furthermore, we develop an efficient computational algorithm which can deal with many variables, and the new method's accuracy and performance is illustrated on simulated and real data.
Journal Article
On the specification of structural equation models for ecological systems
The use of structural equation modeling (SEM) is often motivated by its utility for investigating complex networks of relationships, but also because of its promise as a means of representing theoretical concepts using latent variables. In this paper, we discuss characteristics of ecological theory and some of the challenges for proper specification of theoretical ideas in structural equation models (SE models). In our presentation, we describe some of the requirements for classical latent variable models in which observed variables (indicators) are interpreted as the effects of underlying causes. We also describe alternative model specifications in which indicators are interpreted as having causal influences on the theoretical concepts. We suggest that this latter nonclassical specification (which involves another variable type—the composite) will often be appropriate for ecological studies because of the multifaceted nature of our theoretical concepts. In this paper, we employ the use of meta-models to aid the translation of theory into SE models and also to facilitate our ability to relate results back to our theories. We demonstrate our approach by showing how a synthetic theory of grassland biodiversity can be evaluated using SEM and data from a coastal grassland. In this example, the theory focuses on the responses of species richness to abiotic stress and disturbance, both directly and through intervening effects on community biomass. Models examined include both those based on classical forms (where each concept is represented using a single latent variable) and also ones in which the concepts are recognized to be multifaceted and modeled as such. To address the challenge of matching SE models with the conceptual level of our theory, two approaches are illustrated, compositing and aggregation. Both approaches are shown to have merits, with the former being preferable for cases where the multiple facets of a concept have widely differing effects in the system and the latter being preferable where facets act together consistently when influencing other parts of the system. Because ecological theory characteristically deals with concepts that are multifaceted, we expect the methods presented in this paper will be useful for ecologists wishing to use SEM.
Journal Article
ℓ0-PENALIZED MAXIMUM LIKELIHOOD FOR SPARSE DIRECTED ACYCLIC GRAPHS
We consider the problem of regularized maximum likelihood estimation for the structure and parameters of a high-dimensional, sparse directed acyclic graphical (DAG) model with Gaussian distribution, or equivalently, of a Gaussian structural equation model. We show that the ℓ 0 -penalized maximum likelihood estimator of a DAG has about the same number of edges as the minimal-edge I-MAP (a DAG with minimal number of edges representing the distribution), and that it converges in Frobenius norm. We allow the number of nodes p to be much larger than sample size n but assume a sparsity condition and that any representation of the true DAG has at least a fixed proportion of its nonzero edge weights above the noise level. Our results do not rely on the faithfulness assumption nor on the restrictive strong faithfulness condition which are required for methods based on conditional independence testing such as the PC-algorithm.
Journal Article