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Results for “Models, Structural”

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PubMed1999

Impact of environment on adolescent mental health and behavior: structural equation modeling.

This study uses structural equation models to describe how objective neighborhood, perceived neighborhood, and environmental support predict mental health; 792 adolescents responded to highly structured interviews. The effect of objective environment on mental health was mediated through its influence on perceived neighborhood. Environmental support mitigated negative perceptions of environment and the effect of perceived environment on mental health, while exposure to violence augmented the negative effect of perceived environment.

Adolescent↗

PubMed1988

An introduction to structural equation models.

This paper provides an overview of structural equation models, and their potential for advancing neuropsychological theory and practice. Four topics are covered: (1) an overview of the various classes of models, and an introduction to the terminology and diagrams used to describe them, (2) an outline of the steps involved in applying structural equation modeling to any research problem, (3) an overview of the information used in assessing model fit, and a discussion of the role of significance tests in structural models, and (4) an outline of the advantages and disadvantages of structural equation models, and their potential contribution to neuropsychology. The paper is intended to help researchers (1) assess the relevance of these advanced statistical techniques to their own research, and (2) begin the process of successful application.

Brain Damage, Chronic↗

PubMed2005

Multilevel modeling of two cyclical processes: extending differential structural equation modeling to nonlinear coupled systems.

The authors present a dynamical multilevel model that captures changes over time in the bidirectional, potentially asymmetric influence of 2 cyclical processes. S. M. Boker and J. Graham's (1998) differential structural equation modeling approach was expanded to the case of a nonlinear coupled oscillator that is common in bimanual coordination studies in which participants swing hand-held pendulums but is also applicable to social systems in general. The authors' nonlinear coupled oscillator model decomposed the fluctuations into a competitive component, unique to each individual variable, and a cooperative component that captured bidirectional influence. The authors' model also generated an index of the symmetry/asymmetry of bidirectional influence. Together, the models are useful quantitative tools for the study of interacting, changing processes.

Humans↗

PubMed2005

Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure.

Partial compliance with assigned treatment regimes is common in drug trials and calls for a causal analysis of the effect of treatment actually received. As such observed exposure is no longer randomized, selection bias must be carefully accounted for. The framework of potential outcomes allows this by defining a subject-specific treatment-free reference outcome, which may be latent and is modelled in relation to the observed (treated) data. Causal parameters enter these structural models explicitly. In this paper we review recent progress in randomization-based inference for structural mean modelling, from the additive linear model to the structural generalized linear models. An arsenal of tools currently available for standard association regression has steadily been developed in the structural setting, providing many parallel features to help randomization-based inference. We argue that measurement error on exposure is an important practical complication that has, however, not yet been addressed. We show how standard additive linear structural mean models are robust against unbiased measurement error and how efficient, asymptotically unbiased inference can be drawn when the degree of measurement error bias is known. The impact of measurement error is illustrated in a blood pressure example and finite sample properties are verified by simulation. We end with a plea for more and careful use of this methodology and point to directions for further development.

Guideline Adherence↗

PubMed2004

Convergence of a structured metapopulation model to Levins's model.

We consider a structured metapopulation model describing the dynamics of a single species, whose members are located in separate patches that are linked through migration according to a mean field rule. Our main aim is to find conditions under which its equilibrium distribution is reasonably approximated by that of the unstructured model of Levins (1969). We do this by showing that the (positive) equilibrium distribution converges, as the carrying capacity of each population goes to infinity together with appropriate scalings on the other parameters, to a bimodal distribution, consisting of a point mass at 0, together with a positive part which is closely approximated by a shifted Poisson centred near the carrying capacity. Under this limiting regime, we also give simpler approximate formulae for the equilibrium distribution. We conclude by showing how to compute persistence regions in parameter space for the exact model, and then illustrate all our results with numerical examples. Our proofs are based on Stein's method.

Animals↗

PubMed2004

Indirect protection obtained by Haemophilus influenzae type b vaccination: analysis in a structured population model.

We used a structured population model to study factors determining the magnitude of indirect protection in Haemophilus influenzae type b (Hib) vaccination. On a simulation platform mimicking the population of Finland, a Hib transmission and immunity model, including cross-reactive bacterial encounters, was formulated. Utilizing different vaccination coverages and vaccine types we could study how fast the incidence of Hib disease declined due to direct and indirect vaccination effects. With the Finnish vaccination schedule we could reproduce the observed disappearance of Hib cases. Our results show that an indirect effect was already significant with a relatively low vaccine coverage, even with a vaccine only partly reducing carriage acquisition. This suggests that the vaccination schedule and vaccine to be used should be chosen to result, in addition to immunological memory, in high antibody concentrations, sufficient to reduce carriage, the latter being the main factor behind successful elimination of transmission and disease.

Adolescent↗

PubMed2006

Structural equation modeling with interchangeable dyads.

Structural equation modeling (SEM) can be adapted in a relatively straightforward fashion to analyze data from interchangeable dyads (i.e., dyads in which the 2 members cannot be differentiated). The authors describe a general strategy for SEM model estimation, comparison, and fit assessment that can be used with either dyad-level or pairwise (double-entered) dyadic data. They present applications illustrating this approach with the actor-partner interdependence model, confirmatory factor analysis, and latent growth curve analysis.

Bias↗

PubMedDate not supplied

Determinants of health-related quality of life in coronary artery disease patients: a prospective study generating a structural equation model.

The authors used structural equation modeling to test a conceptual model of HRQL in coronary artery disease. The model, which included biomedical factors and individual and environmental characteristics, was tested in a multicenter group of 465 patients at three timepoints (baseline evaluation of chest pain and 1- and 3-month follow-ups). A satisfactory fit was obtained for the model over time. Depression and anxiety symptoms exerted the most significant influence on HRQL. HRQL and the mediating factors were found to be distinct phenomena. The authors concluded that mediating factors, especially depression and anxiety symptoms, should be taken into consideration in clinical routine if HRQL is regarded as a clinical outcome.

Chest Pain↗

PubMed1992

Structural equation models in medical research.

Structural equation modelling (SEM) is a modern statistical method that allows one to evaluate causal hypotheses on a set of intercorrelated nonexperimental data. The sample variances and covariances, and possibly the means, are compared to those predicted by a theory-based hypothetical model after optimal estimation of the parameters of the model. The goodness-of-fit of the empirical data to the hypothesized model is evaluated statistically. This review describes the underlying statistical theory and rationale of SEM. Both confirmatory factor analysis and latent variable path models are discussed. The applicability of SEM to assessment of reliability and validity is noted. A detailed example is provided, and several examples from the medical literature are briefly reviewed. Cautions regarding the possible misuse or misinterpretation of the technique are also mentioned. Possible future directions for the use of SEM in medical research are suggested. Two appendices provide more technical details.

Factor Analysis, Statistical↗

PubMed1987

Latent growth curves within developmental structural equation models.

This report uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM). When merged with repeated-measures data, this technique permits the estimation of parameters representing both individual and group dynamics. The statistical basis of this model allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions. Aspects of these latent growth models are illustrated with a set of longitudinal WISC data from young children and by using the LISREL V computer program.

Child↗

PubMed2004

Automated protein structure homology modeling: a progress report.

Understanding the molecular function of proteins is greatly enhanced by insights gained from their three-dimensional structures. Since experimental structures are only available for a small fraction of proteins, computational methods for protein structure modeling play an increasingly important role. Comparative protein structure modeling is currently the most accurate method, yielding models suitable for a wide spectrum of applications, such as structure-guided drug development or virtual screening. Stable and reliable automated prediction pipelines have been developed to apply large-scale comparative modeling to whole genomes or entire sequence databases. Model repositories give access to these annotated and evaluated models. In this review, we will discuss recent developments in automated comparative modeling and provide selected examples illustrating the use of homology models.

Animals↗

PubMed2005

Application of structural equation modeling to health outcomes research.

This article provides an overview of the basic underlying principles of structural equation modeling (SEM). SEM models have two basic elements: a measurement model and a structural model. The measurement model describes the associations between the indicators (observed measures) of the latent variables, whereas the structural model delineates the direct and indirect substantive effects among latent variables and between measured and latent variables. The application of SEM to health outcomes research is illustrated using two examples: (a) assessing the equivalence of the SF-36 and patient evaluations of care for English- and Spanish-language respondents and (b) evaluating a theoretical model of health in myocardial infarction patients. The results of SEM studies can contribute to better understanding of the validity of health outcome measures and of relationships between physiologic, clinical, and health outcome variables.

Cross-Cultural Comparison↗

PubMedDate not supplied

Equivalence relationships between stage-structured population models.

Matrix population models are widely applied in conservation ecology to help predict future population trends and guide conservation effort. Researchers must decide upon an appropriate level of model complexity, yet there is little theoretical work to guide such decisions. In this paper we present an analysis of a stage-structured model, and prove that the model's structure can be simplified and parameterised in such a way that the long-term growth rate, the stable-stage distribution and the generation time are all invariant to the simplification. We further show that for certain structures of model the simplified models require less effort in data collection. We also discuss features of the models which are not invariant to the simplification and the implications of our results for the selection of an appropriate model. We illustrate the ideas using a population model for short-tailed shearwaters (Puffinus tenuirostris). In this example, model simplification can increase parameter elasticity, indicating that an intermediate level of complexity is likely to be preferred.

Animals↗

PubMed2001

Mechanism of photosynthetic water oxidation: combining biophysical studies of photosystem II with inorganic model chemistry.

A mechanism for photosynthetic water oxidation is proposed based on a structural model of the oxygen-evolving complex (OEC) and its placement into the modeled structure of the D1/D2 core of photosystem II. The structural model of the OEC satisfies many of the geometrical constraints imposed by spectroscopic and biophysical results. The model includes the tetranuclear manganese cluster, calcium, chloride, tyrosine Z, H190, D170, H332 and H337 of the D1 polypeptide and is patterned after the reversible O2-binding diferric site in oxyhemerythrin. The mechanism for water oxidation readily follows from the structural model. Concerted proton-coupled electron transfer in the S2-->S3 and S3-->S4 transitions forms a terminal Mn(V)=O moiety. Nucleophilic attack on this electron-deficient Mn(V)=O by a calcium-bound water molecule results in a Mn(III)-OOH species, similar to the ferric hydroperoxide in oxyhemerythrin. Dioxygen is released in a manner analogous to that in oxyhemerythrin, concomitant with reduction of manganese and protonation of a mu-oxo bridge.

Crystallography↗

PubMedDate not supplied

Age-based construct validation using structural equation modeling.

In this paper we describe some mathematical and statistical models based on structural equation modeling (SEM) using computer programs like LISREL. We focus on SEM methodology for the simultaneous examination of the internal validity of psychological constructs and the external validity represented by age relations. To illustrate these ideas we use a latent variable path model to examine the organization of intellectual abilities measured by the WAIS-R in the standardization sample. We also examine different ways in which age can be used to structure this organization. This is primarily a methodological paper, but we try to integrate conceptual principles of modeling with some substantive issues of research on the psychology of aging.

Aging↗

PubMed1975

A model for chromatin structure.

A model for chromatin structure is presented. (a) Each of four histone species, H2A (IIbl or f2a2), H2B (IIb2 or f2b), H3 (III or f3) and H4 (IV or f2al) can form a parallel dimer. (b) These dimers can form two tetramers, (H2A)2(H2b)2 and (H3)2(H4)2. (C) These two tetramers bind a segment of DNA and condense it into a "C" segments. (d) The adjacent segments, termed extended or "E" segments, are bound by histone H1 (I or fl) for the major fraction of chromatin; the other "E" regions can be either bound by non-histone proteins or free of protein binding. (e) The binding of histones causes a structural distortion of the DNA which, depending upon the external conditions, may generate the formation of either an open structure with a heterogeneous and non-uniform supercoil or a compact structure with a string of beads. The model is supported by experimental data on histone-histone interaction, histone-DNA interaction and histone subunit-DNA interaction.

Binding Sites↗

PubMed2001

Goal importance within planned behaviour theory as 'the' predictor of study behaviour in college.

BACKGROUND: The theory of planned behaviour has been rarely used for the explanation of student study behaviour and achievement. Although successful, the theory has been criticised for not including important cognitions, so goal importance was added in the present study. Goal importance refers to the weight-importance an individual assigns towards achieving a specific goal (Hollenbeck & Williams, 1987). AIMS: The purpose of Study 1 was to explain the study behaviour habits of first year college students, using a) Ajzen and Madden's (1986) theory of planned behaviour, and b) planned behaviour with the addition of goal importance. The purpose of Study 2 was to replicate the findings of Study 1. SAMPLES: The sample of Study 1 included 149 first year students of an American College located in northern Greece. Study 2 included 85 first year students of the same institution. METHODS: The students in Study 1 were given a questionnaire four weeks prior to the end of the spring 1998 semester, and those in Study 2 in the autumn of 1998, including all elements of the theory of planned behaviour and goal importance. The data were modelled using Covariance Structural Modelling (CSM) and EQS 5.7b (Bentler, 1998). RESULTS: The planned behaviour model was not well supported in Study 1 providing a Comparative Fit Index (CFI) of.83. However, when goal importance was included in the equation, the resulting structural model produced a CFI of.94. The final structural model of Study 1 was re-tested with the sample of Study 2 and produced a CFI =.95. CONCLUSIONS: Findings suggest that goal importance is the causal agent in directing all elements necessary to achieve high levels of study behaviour. Future studies should examine the role of goal importance with other behaviours as well.

Adolescent↗

PubMed1991

Structural equation modeling in clinical assessment research with children.

The use of structural equation modeling has gained increased interest in recent years in the social and behavioral sciences. This article reviews the basic tenets of structural modeling in relation to issues in research and practice involving clinical assessment and compares this approach with more traditional psychometric approaches to the validation of assessment instruments with children. Arguments for and against the inclusion of nonexperimental variables in causal studies aimed at establishing construct validity are also discussed. An illustrative example of the application of structural equation modeling in clinical assessment research is provided, and a comparison is made between this approach and traditional psychometric procedures. Implications and suggestions for the use of structural modeling are discussed for both the practitioner and the clinical researcher.

Arousal↗