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Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

ABSTRACT: Confirmatory Factor Analysis (CFA) has been a popular yet limited approach to assessing latent factor structures. Despite items rarely loading exclusively on one latent factor in multifactorial scales, CFA assumes all indicators/items should load uniquely on their allocated latent dimensions. To address this weakness, Exploratory Structural Equation Modeling (ESEM) combines exploratory factor analyses (EFA) and CFA procedures, allowing cross-loadings to occur when assessing hypothesized models. Although such advantages have enhanced ESEM popularity, its adoption is often limited by software rigidity and complex coding difficulties. To address these obstacles, the current tutorial presents a streamlined, step-by-step approach using the open-source software R while providing both R and Mplus ESEM syntax. The tutorial demonstrates the sequence of the ESEM stages by examining the frequently debated Strengths and Difficulties Questionnaire (SDQ) factor structure, using openly accessible data from the Longitudinal Study of Australian Children (LSAC). As ESEM may allow a better understanding of the complex associations in multidimensional scales, this tutorial may optimize the epidemiological and clinical assessment of common yet multifaceted psychiatric presentations.

SUBMITTER: Prokofieva M

PROVIDER: S-EPMC10375619 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

Prokofieva Maria M Zarate Daniel D Parker Alex A Palikara Olympia O Stavropoulos Vasileios V

BMC psychiatry 20230728 1

Confirmatory Factor Analysis (CFA) has been a popular yet limited approach to assessing latent factor structures. Despite items rarely loading exclusively on one latent factor in multifactorial scales, CFA assumes all indicators/items should load uniquely on their allocated latent dimensions. To address this weakness, Exploratory Structural Equation Modeling (ESEM) combines exploratory factor analyses (EFA) and CFA procedures, allowing cross-loadings to occur when assessing hypothesized models. ...[more]

PMID: 37507658

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Project description:The few studies that have analyzed the factorial structure of early number skills have mainly used confirmatory factor analysis (CFA) and have yielded inconsistent results, since early numeracy is considered to be unidimensional, multidimensional or even underpinned by a general factor. Recently, the bifactor exploratory structural equation modeling (bifactor-ESEM)-which has been proposed as a way to overcome the shortcomings of both the CFA and the exploratory structural equation modeling (ESEM)-proved to be valuable to account for the multidimensionality and the hierarchical nature of several psychological constructs. The present study is the first to investigate the dimensionality of early number skills measurement through the application of the bifactor-ESEM framework. Using data from 644 prekindergarten and kindergarten children (4 to 6 years old), several competing models were contrasted: the one-factor CFA model; the independent cluster model (ICM-CFA); the exploratory structural equation modeling (ESEM); and their bifactor counterpart (bifactor-CFA and bifactor-ESEM, respectively). Results indicated acceptable fit indexes for the one-factor CFA and the ICM-CFA models and excellent fit for the others. Among these, the bifactor-ESEM with one general factor and three specific factors (Counting, Relations, Arithmetic) not only showed the best model fit, but also the best coherent factor loadings structure and full measurement invariance across gender. The bifactor-ESEM appears relevant to help disentangle and account for general and specific factors of early numerical ability. While early numerical ability appears to be mainly underpinned by a general factor whose exact nature still has to be determined, this study highlights that specific latent dimensions with substantive value also exist. Identifying these specific facets is important in order to increase quality of early numerical ability measurement, predictive validity, and for practical implications.

Project description:IntroductionGlobal evidence suggests that natural disasters are on the rise, and that earthquake harm disproportionally impacts developing countries. To mitigate these harms, disaster preparedness is an important behavioral strategy in earthquake risk mitigation. However, there is limited evidence about the levels of preparedness in developing countries, especially Iran. Therefore, this study aimed to investigate earthquake preparedness among the general public, using the Theory of Planned Behavior (TPB) as a theoretical framework.MethodThis methodological study was conducted in Iran in 2022, involving 302 participants selected through stratified random sampling. Data was collected using a valid and reliable questionnaire. Structural equation modeling (SEM) was employed to analyze TPB constructs, including attitudes, subjective norms, perceived behavioral control, and intentions, in relation to earthquake preparedness behaviors. The statistical analyses encompassed descriptive statistics, t-tests, ANOVA, correlation coefficients, confirmatory factor analysis, and structural equation modeling.ResultsResults show that attitudes toward disaster preparedness, subjective norms, and perceived behavioral control significantly (< 0.05) influenced residents' disaster preparedness intentions. Additionally, there was good model fit of the TPB in explaining earthquake preparedness behavior. For confirmatory factor analysis (CFA) of TPB model the fit indices were CMin/df = 1.35 (< 3), RMSEA = 0.03 (< 0.05); CFI = 0.96 (> 0.95); TLI = 0.95 (= 0.95), and for SEM model they were CMin/df = 1.36 (< 3), RMSEA = 0.03 (< 0.05); CFI = 0.96 (> 0.95); TLI = 0.96 (> 0.95).ConclusionThe overall fit indexes were acceptable indicating the TPB model is a valid and reliable measurement model for screening population-level readiness for earthquake preparedness, developing and/or implementing health promotion interventions. The findings also suggest that disaster education programs should emphasize the importance of attitudes, subjective norms, and perceived behavioral control in promoting disaster preparedness.

Project description:Firearm homicides disproportionately affect Black communities. Redlining - discriminatory lending practices of the early 20th century - are associated with current increased rates of firearm violence. Poverty and concentrated disadvantage are also associated with firearm violence. The interaction of these factors with racist redlining housing practices remains unclear. We used generalized structural equation modeling to characterize the mediators through which redlining practices of the 1930s led to present rates of firearm violence in Boston using a negative binomial model. Principle component analysis was used to create four distinct mediating variables representing census block socioeconomic and built environment information, while reducing dimensionality. We calculated the direct effect between harmful (Red and Yellow) vs beneficial (Green) designations and firearm incident rate, indirect effect between redlining designation and firearm incident rate through each mediating variable, and the total effect. The percentage mediation of each mediator was subsequently calculated. Red and Yellow areas of Boston were associated with an 11•1 (95% CI 5•5,22•4) and 11•4 (5•7,22•8) increased incident rate of shooting when compared to Green. In the pathway between Red designation and firearm incident rate, poverty and poor educational attainment mediated 20% of the interaction, share of rented housing mediated 8%, and Black share of the population 3%. In the pathway between Yellow designation and firearm incident rate, poverty and poor educational attainment mediated 16% of the association, and Black share of the population mediated 13%. Redlining practices of the 1930s potentially contribute to increased rates of firearm violence through changes to neighborhood environments, namely through preclusion from homeownership, poverty, poor educational attainment, and concentration (i.e. segregation) of Black communities. These downstream mediating factors serve as points for policy interventions to address urban firearm violence. Michael Poulson and Miriam Neufeld were supported by T32 Training Grants (HP10028, GM86308).

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Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

Publications

Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

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