606 research outputs found
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
Network psychometrics
In recent years, research on dynamical systems in psychology has emerged, which is analogous to other fields such as biology and physics. One popular and promising line of research involves the modeling of psychological systems as causal systems or networks of cellular automat. The general hypothesis is that noticeable macroscopic behaviorâthe co-occurrence of aspects of psychology such as cognitive abilities, psychopathological symptoms, or behaviorâis not due to the influence of unobserved common causes, such as general intelligence, psychopathological disorders, or personality traits, but rather to emergent behavior in a network of interacting psychological, sociological, biological, and other components. This dissertation concerns the estimation of such psychological networks from datasets. While this line of research originated from a dynamical systems perspective, the developed methods have shown strong utility as exploratory data analysis tools, highlighting unique variance between variables rather than shared variance across variables (e.g., factor analysis). In addition, this dissertation shows that network modeling and latent variable modeling are closely related and can complement one-another. The methods are thus widely applicable in diverse fields of psychological research. To this end, the dissertation is split in three parts. Part I is aimed at empirical researchers with an emphasis on clinical psychology, and introduces the methods in conceptual terms and tutorials. Part II is aimed at psychometricians and methodologists, and discusses the methods in technical terms. Finally, Part III is aimed at R users with an emphasis on personality research
Within- and between individual variability of personality characteristics and physical exercise
Using two independent samples, the study investigated links of within- and between-individual variability in personality states in three personality domainsâNeuroticism, Extraversion, and Conscientiousnessâwith physical activity. Activity was defined as self-reported quantity of exercising or walking/cycling. More physical activity was associated with people reporting higher levels of Extraversion and Conscientiousness than they usually did, with the associations clearly replicating across samples and generalizing to all items of these domains. This pattern tended to reflect associations at the level of between-individual differences. When the three domains simultaneously predicted activity, within-individual variance in Neuroticism also emerged as a positive predictor, whereas between-individual level associations waned. The findings are consistent with within-individual differences in personality ratings reflecting meaningful, context-relevant variability
- âŠ