Symposium 21: Structural equation modeling: When are athletes most at risk of dropping out? A new method: The survival analysis

2003 ◽  
Author(s):  
Emma Guillet ◽  
Philippe Sarrazin ◽  
Paul Fontayne
Keyword(s):  
At Risk ◽  
Survival Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
New Method ◽  
Equation Modeling ◽  
Dropping Out

scholarly journals Early Predictors of Long-Term Disability After Injury

2003 ◽  
Vol 12 (3) ◽  
pp. 197-205 ◽  
Author(s):  
Therese S. Richmond ◽  
Donald Kauder ◽  
Janice Hinkle ◽  
Justine Shults
Keyword(s):  
At Risk ◽  
Structural Equation Modeling ◽  
Psychological Distress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Specific Information ◽  
Care Providers ◽  
Early Predictors ◽  
Patients At Risk

• Background Improving outcomes after serious injury is important to patients, patients’ families, and healthcare providers. Identifying early risk factors for long-term disability after injury will help critical care providers recognize patients at risk. • Objectives To identify early predictors of long-term disability after injury and to ascertain if age, level of disability before injury, posttraumatic psychological distress, and social network factors during hospitalization and recovery significantly contribute to long-term disability after injury. • Methods A prospective, correlational design was used. Injury-specific information on 63 patients with serious, non–central nervous system injury was obtained from medical records; all other data were obtained from interviews (3 per patient) during a 2½-year period. A model was developed to test the theoretical propositions of the disabling process. Predictors of long-term disability were evaluated using path analysis in the context of structural equation modeling. • Results Injuries were predominately due to motor vehicle crashes (37%) or violent assaults (21%). Mean Injury Severity Score was 13.46, and mean length of stay was 12 days. With structural equation modeling, 36% of the variance in long-term disability was explained by predictors present at the time of injury (age, disability before injury), during hospitalization (psychological distress), or soon after discharge (psychological distress, short-term disability after injury). • Conclusions Disability after injury is due partly to an interplay between physical and psychological factors that can be identified soon after injury. By identifying these early predictors, patients at risk for suboptimal outcomes can be detected.

scholarly journals S75. PERFORMANCE IN WORKING MEMORY AND EXECUTIVE FUNCTIONS AS IT RELATES TO THEORY OF MIND ABILITIES IN A NONCLINICAL SAMPLE OF INDIVIDUALS WITH SCHIZOTYPAL TRAITS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S62-S63
Author(s):  
Kyrsten Grimes ◽  
Konstantine Zakzanis
Keyword(s):  
At Risk ◽  
Working Memory ◽  
Structural Equation Modeling ◽  
Theory Of Mind ◽  
Early Detection ◽  
Structural Equation ◽  
Cognitive Models ◽  
Equation Modeling ◽  
Nonclinical Sample ◽  
The Relationship

Abstract Background In recent decades, research in the treatment of schizophrenia has shifted to early detection and intervention. Unfortunately, the development of psychosis is still poorly understood, making such an endeavour more challenging. Cognitive models of psychosis suggest that neurocognitive deficits place an individual at greater risk of developing metacognitive deficits. Such deficits in metacognition have been shown to contribute to the development of positive symptomatology. A large body of literature supports that patients with schizophrenia exhibit impairments across nearly all domains of neurocognition, as well as metacognition. Theory of mind (ToM) is one of the most widely studied components of metacognition, which includes both cognitive (i.e., understanding what another person is thinking) and affective (i.e., understanding what another person is feeling) processes. Research indicates patients with schizophrenia demonstrate deficits in cognitive and affective ToM, and these deficits are associated with delusional symptomatology. If ToM is involved in the development of positive symptoms, it is expected that this deficit would be present prior to the onset of a first episode psychosis. It is unclear from current research findings if this is the case, however. Additionally, research examining the role of neurocognition as it relates to ToM is lacking. While some research has examined these variables in clinically-high-risk (CHR) groups, little research has examined nonclinical samples at risk for psychosis. Thus, this study sought to examine the relationship between ToM and neurocognition in a nonclinical sample with schizotypal traits, as research suggests these individuals may be at risk of developing a psychotic illness. It was hypothesized that lower performance in working memory and executive functioning would be related to poorer performance in cognitive and affective ToM, which would subsequently be associated with subsyndromal delusions. It was further predicted that schizotypal traits would moderate the relationship between neurocognitive performance and ToM abilities. Methods Undergraduate students (N = 99) completed self-report measures of personality and psychosocial functioning, including the Schizotypal Personality Questionnaire, Beck Depression Inventory-II, Launay-Slade Hallucination Scale-Revised, and 21-Item Peters Delusions Inventory. Participants also completed the Neuropsychological Assessment Battery Screening Module, which is a screening measure for neurocognitive dysfunction. Finally, they completed the Recognition of Faux Pas Test, a task-based measure that evaluates cognitive and affective ToM. Results Data collection is complete, and the data will be analysed using partial least squares structural equation modeling. This is a regression-based path analysis designed for exploratory models. This statistical method is better able to handle non-normally distributed data and smaller sample sizes when compared to covariance-based structural equation modeling. Discussion Study findings will be discussed in the context of cognitive models for the development of psychosis. The ways in which these findings, and cognitive models more broadly, can facilitate early detection of schizophrenia will be discussed, along with how such models can be used to inform psychosocial interventions for the illness.

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

The Measurement Invariance of the Short Dark Triad

2020 ◽  
Vol 41 (4) ◽  
pp. 207-218
Author(s):  
Mihaela Grigoraș ◽  
Andreea Butucescu ◽  
Amalia Miulescu ◽  
Cristian Opariuc-Dan ◽  
Dragoș Iliescu
Keyword(s):  
Structural Equation Modeling ◽  
Item Response ◽  
Structural Equation ◽  
Dark Triad ◽  
Theory And Practice ◽  
Equation Modeling ◽  
Scale Level ◽  
High Stake ◽  
Differential Functioning ◽  
Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

Celebrity Worship Among University Students in Malaysia

2011 ◽  
Vol 16 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Viren Swami ◽  
Tomas Chamorro-Premuzic ◽  
Khairul Mastor ◽  
Fatin Hazwani Siran ◽  
Mohammad Mohsein Mohammad Said ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Attitude Scale ◽  
Equation Modeling ◽  
Future Research ◽  
The West ◽  
Factor Solution ◽  
Movie Stars ◽  
Three Factor

The present study examined conceptual issues surrounding celebrity worship in a Malay-speaking population. In total, 512 Malay and 269 Chinese participants from Malaysia indicated who their favorite celebrity was and completed the Celebrity Attitude Scale (CAS) as well as a range of demographic items. Results showed that the majority of Malay and Chinese participants selected pop stars and movie stars as their favourite celebrities, mirroring findings in Western settings. In addition, exploratory factor analysis revealed a three-factor solution of the CAS that was consistent with previous studies conducted in the West. Structural equation modeling further revealed that participant’s age was negatively associated with celebrity worship and that self-rated attractiveness was positively associated with celebrity worship. Overall, the present results suggest that celebrity worship in Malaysia may be driven by market and media forces, and future research may well be guided by use of the CAS.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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