Estimating Local Structural Equation Models

Robitzsch, Alexander

doi:10.3390/jintelligence11090175

Cited by 3 publications

(3 citation statements)

References 109 publications

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“…It can, therefore, be concluded that the created hypothetical model confirms the fact that it is applicable in this form, as it shows a good degree of agreement with real data. This statement is confirmed also by values of other indicators: χ2 = 507.962, df = 468, p = 0.098, and it is in accordance with the theory presented in [40,43,47,60,61].…”

Section: The Model Of Cybersecuritysupporting

confidence: 88%

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

Gombár,

Vagaská,

Korauš

et al. 2024

Mathematics

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In the current digital transformation to Industry 4.0, the demands on the ability of countries to react responsibly and effectively to threats in the field of cyber security (CS) are increasing. Cyber safety is one of the pillars and concepts of Industry 4.0, as digitization brings convergence and integration of information technologies (IT) and operational technologies (OT), IT/OT systems, and data. Collecting and connecting a large amount of data in smart factories and cities poses risks, in a broader context for the entire state. The authors focus attention on the issue of CS, where, despite all digitization, the human factor plays a key role—an actor of risk as well as strengthening the sustainability and resilience of CS. It is obvious that in accordance with how the individuals (decision-makers) perceive the risk, thus they subsequently evaluate the situation and countermeasures. Perceiving cyber threats/risks in their complexity as a part of hybrid threats (HT) helps decision-makers prevent and manage them. Due to the growing trend of HT, the need for research focused on the perception of threats by individuals and companies is increasing. Moreover, the literature review points out a lack of methodology and evaluation strategy. This study presents the results of the research aimed at the mathematical modelling of risk perception of threats to the state and industry through the disruption of CS. The authors provide the developed factor model of cyber security (FMCS), i.e., the model of CS threat risk perception. When creating the FMCS, the researchers applied SEM (structural equation modelling) and confirmatory factor analysis to the data obtained by the implementation of the research tool (a questionnaire designed by the authors). The pillars and sub-pillars of CS defined within the questionnaire enable quantification in the perception of the level of risk of CS as well as differentiation and comparison between the analyzed groups of respondents (students of considered universities in SK and CZ). The convergent and discriminant validity of the research instrument is verified, and its reliability is confirmed (Cronbach’s alpha = 0.95047). The influence of the individual pillars is demonstrated as significant at the significance level of α = 5%. For the entire research set N = 964, the highest share of risk perception of CS threats is achieved by the DISRIT pillar (disruption or reduction of the resistance of IT infrastructure).

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Section: The Model Of Cybersecuritysupporting

confidence: 88%

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

Gombár,

Vagaská,

Korauš

et al. 2024

Mathematics

View full text Add to dashboard Cite

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“…While this method simplified the analysis, it introduced the risk of artificial dichotomization. We acknowledge the potential benefits of employing the moderated factor model or local structural equation modeling, as recommended by Molenaar et al ( 2010 ) and Robitzsch ( 2023 ), which evaluates if personality factor loadings vary across levels of cognitive ability. This approach, which models cognitive ability on a continuous scale, could complement traditional categorization methods by providing additional insights into how factor loadings vary with cognitive ability levels.…”

Section: Discussionmentioning

confidence: 99%

How Cognitive Ability Shapes Personality Differentiation in Real Job Candidates: Insights from a Large-Scale Study

Stamate,

Denis,

Sauvé

2024

J. Intell.

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The differentiation of personality by the cognitive ability hypothesis proposes that individuals with higher cognitive ability have more variability in their personality structure than those with lower cognitive ability. A large sample of actual job candidates (n = 14,462) who participated in an online proctored test session, providing socio-demographic information and completing cognitive ability, personality, and language proficiency assessments, was used to test this hypothesis. The total sample was divided into three equal groups (low, average, high) using percentiles as the cutoff point to investigate the effects of cognitive ability. An ANCOVA demonstrated the significant effect of cognitive ability on personality traits, controlling for language proficiency. Principal component analyses showed that the personality structure differed between the cognitive ability groups, with the high-cognitive-ability group having an additional personality component. Similarly, analyses across job complexity levels indicated more personality components for high-job-complexity positions. The implications, limitations, and future directions of this study are discussed.

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“…However, some authors consider that the selection of the measurement invariance method appears to lack a clear rationale. In theory, local structural equation modeling (LSEM) [44] and moderated nonlinear factor analysis (MNLFA) [45,46] are considered more appropriate methodologies when compared to the use of multiple-group confirmatory factor analysis.…”

Section: Discussionmentioning

confidence: 99%

Measurement Invariance of the Multidimensional Jealousy Scale and Quality of Relationships Inventory (Friend)

Leite,

Silva,

Vilela

et al. 2024

Behavioral Sciences

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The aim of this study is to measure the invariance of the Multidimensional Jealousy Scale (MJS) and of the Quality of Relationships Inventory (Friend) (QRI-F) across gender, age, education, and being in a romantic relationship in a Portuguese sample (N = 662). A confirmatory factor analysis was performed to test the fit of different potential factor structures. The results pointed out that both MJS and QRI-F were most suitable if represented by three first-order factors correlated between them. Results from multi-group analyses suggested there was factorial invariance for these structures across groups, suggesting that the MJS and the QRI-F provide, respectively, an assessment of romantic jealousy and quality of relationship that are equivalent across gender, age, education, and being in a romantic relationship. The study established the strong psychometric properties of its instruments, validating reliability and convergent and discriminant validity, thereby bolstering the research’s overall credibility. Additionally, cognitive jealousy is primarily influenced by heightened conflict values, with education, relationship status, and gender moderating the associations between QRI-F dimensions and MJS behavioral and cognitive jealousy. The research offered in-depth perspectives on jealousy, underscoring its diverse manifestations across demographic variables and illuminating the complexities within the dynamics of friendships.

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Estimating Local Structural Equation Models

Cited by 3 publications

References 109 publications

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

How Cognitive Ability Shapes Personality Differentiation in Real Job Candidates: Insights from a Large-Scale Study

Measurement Invariance of the Multidimensional Jealousy Scale and Quality of Relationships Inventory (Friend)

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