scholarly journals Cognitive Abilities Explain Wording Effects in the Rosenberg Self-Esteem Scale

Assessment ◽  
2017 ◽  
Vol 27 (2) ◽  
pp. 404-418 ◽  
Author(s):  
Timo Gnambs ◽  
Ulrich Schroeders
Keyword(s):  
Factor Structure ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Common Factor ◽  
Self Esteem ◽  
Specific Factor ◽  
Measurement Models

There is consensus that the 10 items of the Rosenberg Self-Esteem Scale (RSES) reflect wording effects resulting from positively and negatively keyed items. The present study examined the effects of cognitive abilities on the factor structure of the RSES with a novel, nonparametric latent variable technique called local structural equation models. In a nationally representative German large-scale assessment including 12,437 students competing measurement models for the RSES were compared: a bifactor model with a common factor and a specific factor for all negatively worded items had an optimal fit. Local structural equation models showed that the unidimensionality of the scale increased with higher levels of reading competence and reasoning, while the proportion of variance attributed to the negatively keyed items declined. Wording effects on the factor structure of the RSES seem to represent a response style artifact associated with cognitive abilities.

A stronger latent‐variable methodology to actual–ideal discrepancy

2008 ◽  
Vol 22 (7) ◽  
pp. 629-654 ◽  
Author(s):  
L. Francesca Scalas ◽  
Herbert W. Marsh
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Self Esteem ◽  
Self Concept ◽  
Multiple Indicators ◽  
Multiple Group ◽  
Gender And Age ◽  
Ideal Discrepancy

We introduce a latent actual–ideal discrepancy (LAID) approach based on structural equation models (SEMs) with multiple indicators and empirically weighted variables. In Study 1, we demonstrate with simulated data, the superiority of a weighted approach to discrepancy in comparison to a classic unweighted one. In Study 2, we evaluate the effects of actual and ideal appearance on physical self‐concept and self‐esteem. Actual appearance contributes positively to physical self‐concept and self‐esteem, whereas ideal appearance contributes negatively. In support of multidimensional perspective, actual‐ and ideal‐appearance effects on self‐esteem are substantially—but not completely—mediated by physical self‐concept. Whereas this pattern of results generalises across gender and age, multiple‐group invariance tests show that the effect of actual appearance on physical self‐concept is larger for women than for men. Copyright © 2008 John Wiley & Sons, Ltd.

Beyond An Informal Everyday Concept of Self-Esteem

2015 ◽  
Vol 36 (4) ◽  
pp. 237-246 ◽  
Author(s):  
Petra Hank
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Latent Trait ◽  
Self Esteem ◽  
The State ◽  
State Variables ◽  
Measurement Models ◽  
State Test ◽  
Trait Measurement ◽  
Trait Factors

Abstract. The present study investigated a state-trait model of self-esteem. Analyses focused on determining if the trait of the observables measuring state self-esteem is equivalent to the trait of the observables measuring trait self-esteem. N = 439 college students completed the Multidimensional Scale of Self-Esteem (MSES) on two measurement occasions spaced 10 weeks apart. Structural equation models were used to test latent state-trait measurement models and the relation between the state and trait components of self-esteem. The results suggest that (1) except for physical self-esteem, the multi-state-single-trait models are suitable for all self-esteem dimensions investigated. This holds for the state test halves as well as for the trait test halves. (2) Concerning the association between the components of trait and state self-esteem, results were supportive of a model, including two latent trait variables, assumed to explain the latent state variables for the respective state form and trait form for the dimensions of general self-esteem. These latent trait factors correlate substantively with .94 ≤ r ≤ .99.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

scholarly journals Self-esteem, social comparison, and Facebook use

2018 ◽  
Vol 14 (4) ◽  
pp. 831-845 ◽  
Author(s):  
Elisa Bergagna ◽  
Stefano Tartaglia
Keyword(s):  
Social Interaction ◽  
Social Comparison ◽  
Technology Use ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Self Esteem ◽  
Self Report ◽  
Individual Traits ◽  
Facebook Use ◽  
The Individual

Facebook use is very popular among young people, but many open issues remain regarding the individual traits that are antecedents of different behaviours enacted online. This study aimed to investigate whether the relationship between self-esteem and the amount of time on Facebook could be mediated by a tendency towards social comparison. Moreover, three different modalities of Facebook use were distinguished, i.e., social interaction, simulation, and search for relations. Because of gender differences in technology use and social comparison, the mediation models were tested separately for males and females. Data were collected by means of a self-report questionnaire with a sample of 250 undergraduate and graduate Italian students (mean age: 22.18 years). The relations were examined empirically by means of four structural equation models. The results revealed the role of orientation to social comparison in mediating the relations between low self-esteem and some indicators of Facebook use, i.e., daily hours on Facebook and the use of Facebook for simulation. For females, the use of Facebook for social interaction was directly influenced by high self-esteem and indirectly influenced by low self-esteem. Globally, the dimension of social comparison on Facebook emerged as more important for females than for males.

Clarifying the Factor Structure of the Self-Compassion Scale

2021 ◽  
Author(s):  
Noelle J. Strickland ◽  
Raquel Nogueira-Arjona ◽  
Sean Mackinnon ◽  
Christine Wekerle ◽  
Sherry H. Stewart
Keyword(s):  
Factor Structure ◽  
Lower Order ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Well Being ◽  
The Self ◽  
Self Compassion ◽  
The Hierarchical Structure ◽  
Compassion Scale

Abstract. Self-compassion is associated with greater well-being and lower psychopathology. There are mixed findings regarding the factor structure and scoring of the Self-Compassion Scale (SCS). Using confirmatory factor analysis, we tested and conducted nested comparisons of six previously posited factor structures of the SCS. Participants were N = 1,158 Canadian undergraduates (72.8% women, 26.6% men, 0.6% non-binary; Mage = 19.0 years, SD = 2.3). Results best supported a two-factor hierarchical model with six lower-order factors. A general self-compassion factor was not supported at the higher- or lower-order levels; thus, a single total score is not recommended. Given the hierarchical structure, researchers are encouraged to use structural equation models of the SCS with two latent variables: self-caring and self-coldness. A strength of this study is the large sample, while the undergraduate sample may limit generalizability.

Structural equation modelling with complex sampling designs and non-random attrition: A tutorial using lavaan and Mplus

2021 ◽  
Author(s):  
Aja Louise Murray ◽  
Anastasia Ushakova ◽  
Helen Wright ◽  
Tom Booth ◽  
Peter Lynn
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Cluster Sampling ◽  
Complex Sampling ◽  
Latent Trajectory ◽  
Adequate Sampling ◽  
Complex Sampling Designs ◽  
Selection Probabilities ◽  
Pseudo Maximum Likelihood

Complex sampling designs involving features such as stratification, cluster sampling, and unequal selection probabilities are often used in large-scale longitudinal surveys to improve cost-effectiveness and ensure adequate sampling of small or under-represented groups. However, complex sampling designs create challenges when there is a need to account for non-random attrition; a near inevitability in social science longitudinal studies. In this article we discuss these challenges and demonstrate the application of weighting approaches to simultaneously account for non-random attrition and complex design in a large UK-population representative survey. Using an auto-regressive latent trajectory model with structured residuals (ALT-SR) to model the relations between relationship satisfaction and mental health in the Understanding Society study as an example, we provide guidance on implementation of this approach in both R and Mplus is provided. Two standard error estimation approaches are illustrated: pseudo-maximum likelihood robust estimation and Bootstrap resampling. A comparison of unadjusted and design-adjusted results also highlights that ignoring the complex survey designs when fitting structural equation models can result in misleading conclusions.

scholarly journals A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

2020 ◽  
pp. 107699862097855
Author(s):  
Takashi Yamashita ◽  
Thomas J. Smith ◽  
Phyllis A. Cummins
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Preparation Process ◽  
Data Preparation ◽  
Assessment Data ◽  
Statistical Software ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
International Assessment

In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.

scholarly journals THE RELATIONSHIP BETWEEN COGNITION, FUNCTIONAL ABILITIES, AND THE LATENT DEMENTIA PHENOTYPE AMONG CENTENARIANS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S965-S965
Author(s):  
Jonathan Sober ◽  
John L Woodard ◽  
L Stephen Miller ◽  
Adam Davey ◽  
Peter Martin ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Structural Equation ◽  
Cognitive Status ◽  
Community Dwelling ◽  
Self Report ◽  
Functional Abilities ◽  
Cognitive Changes ◽  
Dementia Severity

Abstract Adequate assessment of cognitive abilities and functional capacity is essential for a diagnosis of dementia. However, cognition is only moderately related to functional status, and this relationship is poorly understood among centenarians, a group of older adults with high risk for dementia. A bifactor structural equation model can be used to delineate the variance attributed to dementia-specific related cognitive changes (i.e., the latent variable delta) and the variance due to general intelligence (i.e., g’). This study aimed to determine the validity of delta as a marker of cognitive decline among centenarians. It was hypothesized that delta was correlated with cognitive status, functional abilities and, dementia severity. Overall, 244 community dwelling centenarians (Mage = 100.58, 84.8% female) were recruited through the Georgia Centenarian Study, a population-based study of octogenarians and centenarians from northern Georgia. Older adults were administered measures of cognition and a self-report measure of functional abilities. Latent variable scores (i.e., g’ and delta) were modeled and correlated with standard global cognitive screening measures (i.e., MMSE) and measures of dementia severity. Results indicate that delta was significantly correlated with functional ability and cognitive abilities. Consistent with our hypotheses, delta was also significantly related to dementia severity. Overall, estimates of the latent dementia phenotype, delta, were significantly related to cognitive and functional abilities among centenarians, providing validation of delta as a useful index of dementia severity.

scholarly journals Are global and specific interindividual differences in cortical thickness associated with facets of cognitive abilities, including face cognition?

2019 ◽  
Vol 6 (7) ◽  
pp. 180857 ◽  
Author(s):  
Kristina Meyer ◽  
Benjamín Garzón ◽  
Martin Lövdén ◽  
Andrea Hildebrandt
Keyword(s):  
Cortical Thickness ◽  
Cognitive Abilities ◽  
Structural Equation ◽  
Brain Network ◽  
Morphological Properties ◽  
Interindividual Differences ◽  
Measurement Models ◽  
The Core ◽  
Human Connectome Project ◽  
Small Effect Size

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

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