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Results for “Structural Equation Modeling”

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PubMed1992

Bayesian analysis of stochastic constraints in structural equation models.

Structural equation models are analysed in the presence of stochastic constraints. Based on a Bayesian perspective, a prior distribution on nuisance parameters in the unknown covariance matrix of error measurements with stochastic constraints is considered. An iterative procedure is implemented to produce the various Bayesian estimates with stochastic constraints. A simulation study is conducted to illustrate the accuracy and behaviour of this Bayesian approach. A real-life example is provided to illustrate the theory.

Bayes Theorem↗

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↗

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↗

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↗

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↗

PubMed1991

Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines.

Structural equation modeling (SEM) is now widely used in social and behavioral science research. SEM provides the possibility of fitting, and evaluating the fit, of well-specified, theoretical models to empirical data--more generally, of testing elaborated psychological theories. The options available to users of these approaches are many and varied. Popular SEM computational software packages, such as LISREL and EQS, provide a large amount of information, and there is some uncertainty as to what should be routinely reported. A series of guidelines are proposed for reporting SEM results in articles submitted to Psychology and Aging. The suggested guidelines ask authors using SEM methodology to provide important analysis information that will enable readers to evaluate the findings.

Aged↗

PubMed1991

Annotated bibliography of structural equation modelling: technical work.

Researchers must be familiar with a variety of source literature to facilitate the informed use of structural equation modelling. Knowledge can be acquired through the study of an expanding literature found in a diverse set of publishing forums. We propose that structural equation modelling publications can be roughly classified into two groups: (a) technical and (b) substantive applications. Technical materials focus on the procedures rather than substantive conclusions derived from applications. The focus of this article is the former category; included are foundational/major contributions, minor contributions, critical and evaluative reviews, integrations, simulations and computer applications, precursor and historical material, and pedagogical textbooks. After a brief introduction, we annotate 294 articles in the technical category dating back to Sewall Wright (1921).

Humans↗

PubMed1991

Structural equation modeling in environmental risk assessment.

Environmental epidemiology requires effective models that take individual observations of environmental factors and connect them into meaningful patterns. Single-factor relationships have given way to multivariable analyses; simple additive models have been augmented by multiplicative (logistic) models. Each of these steps has produced greater enlightenment and understanding. Models that allow for factors causing outputs that can affect later outputs with putative causation working at several different time points (e.g., linkage) are not commonly used in the environmental literature. Structural equation models are a class of covariance structure models that have been used extensively in economics/business and social science but are still little used in the realm of biostatistics. Path analysis in genetic studies is one simplified form of this class of models. We have been using these models in a study of the health and development of infants who have been exposed to lead in utero and in the postnatal home environment. These models require as input the directionality of the relationship and then produce fitted models for multiple inputs causing each factor and the opportunity to have outputs serve as input variables into the next phase of the simultaneously fitted model. Some examples of these models from our research are presented to increase familiarity with this class of models. Use of these models can provide insight into the effect of changing an environmental factor when assessing risk. The usual cautions concerning believing a model, believing causation has been proven, and the assumptions that are required for each model are operative.

Environmental Exposure↗

PubMed1990

Structural equation models of relationships between exercise and cognitive abilities.

Data were obtained from 300 men and women aged 55 to 91. Separate structural equation models of relationships between physical exercise and 3 cognitive performance variables--reaction time, working memory, and reasoning--fit the data well. Other variables in the models were age, health, education, and morale. Age and exercise affected each performance variable directly; education had a direct effect on reasoning only. There were also indirect effects of age and health on performance variables, mediated through exercise. The main hypothesis of the study, that exercise contributes to performance, was supported. A large decrease in model fit resulted when the path from exercise to each performance variable was deleted. Hypotheses that age-related deficits are primarily accounted for by lack of exercise or by poor health were not supported.

Aged↗

PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗

PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

PubMed2026

Genomic Structural Equation Modeling Identifies a Shared Inflammatory Genetic Dimension Across Inflammatory Arthritis Phenotypes and Biomarkers.

BACKGROUND: Inflammatory arthritis (IA), including rheumatoid arthritis (RA), psoriatic arthritis (PsA) and gout, shares systemic inflammatory features indexed by C-reactive protein (CRP) and interleukin-6 (IL-6), yet the extent of their common genetic basis remains unclear. AIMS: We aimed to delineate the shared genetic architecture across IA phenotypes and inflammatory biomarkers. MATERIALS AND METHODS: We applied genomic structural equation modelling (Genomic SEM) to GWAS summary statistics for RA, PsA, gout, CRP and IL-6, fitted a single common factor, and performed multivariate GWAS followed by fine-mapping, transcriptome-wide association, gene-based analysis, pathway enrichment, and cell-type and spatial mapping. RESULTS: A single common factor was fitted (CFI = 0.990, SRMR = 0.045). The multivariate GWAS identified 56 genome-wide significant SNPs across 10 independent lead loci, including one novel signal. Fine-mapping prioritized high-confidence variants near PTPN22, the CRP gene cluster and a urate-associated locus. Gene-level analyses converged on DCLRE1B, PTPN22, IL6R, NLRP3 and HNF1A, with pathway enrichment implicating inflammasome assembly and metabolic-inflammatory overlap. Cell-type enrichment highlighted myeloid populations, and spatial mapping localized signals to lung, kidney, mucosal epithelium and gastrointestinal tissues. DISCUSSION: These results delineate a shared inflammatory genetic dimension across IA phenotypes and biomarkers, anchored in immune, inflammasome, cytokine-receptor and metabolic pathways. CONCLUSION: Together, these findings provide a valuable framework for prioritizing candidate genes and cellular contexts for future investigation.

TWAS↗

PubMed1991

The use of structural equation modeling in generative research: toward the design of a preventive intervention for bereaved children.

Describes a generative study of processes which may lead to symptomatology in children who have experienced the death of a parent. Based on existing literature, four putative mediating variables were identified: parental demoralization, family warmth, negative family events, and positive stable family events. Structural equation modeling techniques were used to compare several potential causal models involving these variables. The results were most consistent with a model in which bereavement was not directly related to the child symptomatology, but rather its effects were transmitted through these four mediational mechanisms. The implications of the results of the structural modeling for the design and evaluation of preventive interventions are discussed briefly.

Adaptation, Psychological↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed1990

Appropriateness of composites in structural equation models.

The appropriateness of using composites instead of multiple indicators in a structural model of physical health was evaluated. Liang's (1986) specification of self-reported physical health is a relatively complex multiple indicators model which may not be practical in actual application. To simplify this formulation, a two-stage strategy was used. First, reliability was estimated for each composite to fix the measurement error variance and the regression of the composite on the latent variable. Second, the model was reestimated by constraining these parameters. Regression analyses were undertaken to assess the impact of using composites instead of multiple indicators. Parameter estimates for causal linkages and residual error variances based on multiple indicators approach were closely reproduced by using composites, thus providing justification for the proposed strategy.

Activities of Daily Living↗

PubMed1987

Structural-equation models of current drug use: are appropriate models so simple(x)?

The simplex and common-factor models of drug use were compared using maximum-likelihood estimation of latent variable structural models in two samples: a sample of 226 high school students, using ratio-scale measures of current drug use, and a sample of 310 industrial workers and 811 college students, using ordinal-scale measures of current drug use. Latent variables of alcohol, marijuana, enhancer hard drugs, and dampener hard drugs were specified in a series of structural models. Contrary to previous findings with cumulative drug-use data, the common-factor model provided a more acceptable representation of the observed current-use data than did the simplex model in both samples. In addition, the similarity of results across both of these samples supports recent contentions by Huba and Bentler (1982) that quantitatively measured variables are not necessarily superior to qualitative, ordinal indicators in latent variable models of drug use.

Adolescent↗

PubMed1988

Explaining adolescent drug use: an elaboration strategy for structural equations modeling.

We report a series of analyses designed to estimate increasingly elaborated theoretical models that explain adolescent drug use. Each of the successive elaborations adds a theoretical construct to the explanatory model in order to increase our understanding of drug use by specifying in greater detail the nature of the structural relationships among the latent variables. The more detailed specification is accomplished by 1) specifying new direct effects that increase explained variance in drug use, 2) decomposing direct effects through the interpolation of hypothesized intervening variables, 3) specifying antecedents of variables that modify their direct effects, and 4) exposing suppressor effects. Where indicated, we evaluate alternative explanations of the observed relationships. We do this by controlling for common antecedent effects to reduce spuriousness or by examining different specifications of causal linkages among the explanatory constructs.

Adolescent↗