Latent Profile Analysis of Mental Health among Chinese University Students: Evidence for the Dual-Factor Model
6
Citation
34
Reference
10
Related Paper
Citation Trend
Abstract:
The dual-factor model of mental health has garnered substantial support, positing the necessity of encompassing both negative (e.g., psychological problems) and positive (e.g., well-being) indicators in comprehensive evaluations of people’s mental health. Nonetheless, the nature of the profiles and predictors (such as academic emotions) during four years of university life lack clarity, hampering a profound understanding of mental well-being among university students. This research included 135 items designed to assess an array of depression symptoms, negative emotional experiences, life satisfaction, positive emotional experiences, and academic emotions. First, this research affirmed the applicability of the dual-factor model in the context of Chinese university students (N = 2277) with the utilization of confirmatory factor analysis (CFA). Furthermore, latent profile analysis (LPA) was employed to delineate prevalent constellations of psychological symptoms and subjective well-being within participants. The outcomes unveiled the existence of three distinct clusters: (1) Complete Mental Health, (2) Vulnerable, and (3) Troubled. Third, by employing R3stept on academic emotions and mental health classifications, this study revealed that there were associations between this variable and specific amalgams of psychological symptoms and well-being levels. These findings bear influence on the practice of mental health screening and the identification of individuals necessitating targeted interventions.Keywords:
CLARITY
In this research, the authors examined the construct validity of scores of the Academic Motivation Scale using exploratory structural equation modeling. Study 1 and Study 2 involved 1,416 college students and 4,498 high school students, respectively. First, results of both studies indicated that the factor structure tested with exploratory structural equation modeling provides better fit to the data than the one tested with confirmatory factor analysis. Second, the factor structure was gender invariant in the exploratory structural equation modeling framework. Third, the pattern of convergent and divergent correlations among Academic Motivation Scale factors was more in line with theoretical expectations when computed with exploratory structural equation modeling rather than confirmatory factor analysis. Fourth, the configuration of convergent and divergent correlations connecting each Academic Motivation Scale factors to a validity criterion was more in line with theoretical expectations with exploratory structural equation modeling than with confirmatory factor analysis.
Exploratory factor analysis
Convergent validity
Cite
Citations (194)
The authors provide a basic set of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis or structural equation modeling as the primary statistical analysis technique. The authors provide an introduction to both techniques, along with sample analyses, recommendations for reporting, evaluation of articles in The Journal of Educational Research using these techniques, and concluding remarks.
Cite
Citations (6,083)
Exploratory factor analysis
Cite
Citations (4)
This workshop will expose participants to the statistical technique of Structural Equation Modeling (SEM), with a focus on confirmatory factor analysis (CFA), using the statistical software AMOS. Structural equation modeling is a multivariate statistical analysis technique that is used to analyze structural relationships. Confirmatory Factor Analysis examines whether collected data fit a hypothesized model of what the data are meant to measure. It is the measurement part of SEM, which shows relationships between latent variables and the observed variables.
Statistical Analysis
Cite
Citations (0)
This article examines the factors that determine the internet banking adoption amongst International Islamic University Malaysia (IIUM) and its causal effects using a theoretical model based on the Technology Acceptance Model (TAM). The research model consists of four exogenous latent constructs, namely, awareness, perceived usefulness, trust and perceived risk and endogenous latent construct namely Internet banking adoption. Data relating to constructs were collected from 200 university’s students in (IIUM) and subjected to structural Equation Modeling (SEM) analysis. Confirmatory Factor Analysis (CFA) was performed to examine the reliability, construct validity, convergent validity and goodness of fit of structural models and measurement models. The hypothesized structural model fits the data well. The results show that the significant factor that leads to the adoption of internet banking is perceived usefulness but awareness, trust and risk have negative significant towards the use of internet banking.
Goodness of fit
Technology Acceptance Model
Convergent validity
Cite
Citations (13)
This chapter contains sections titled: Construct Validation: A Multiple Perspective Approach Data and Estimation Model Evaluation and Goodness of Fit Substantive Applications of Confirmatory Factor Analysis and Structural Equation Modeling Conclusion
Goodness of fit
Cite
Citations (361)
This study examines the factor structure of the shortened version of the Leadership Scale for Sport, through a survey of 201 collegiate swimmers at National Collegiate Athletic Association Division II and III institutions, using both exploratory structural equation modeling and confirmatory factor analysis. Both exploratory structural equation modeling and confirmatory factor analysis showed that a five-factor solution fit the data adequately. The sizes of factor loadings on target factors substantially differed between the confirmatory factor analysis and exploratory structural equation modeling solutions. In addition, the inter-correlations between factors of the Leadership Scale for Sport and the correlations with athletes’ satisfaction were found to be inflated in the confirmatory factor analysis solution. Overall, the findings provide evidence of the factorial validity of the shortened Leadership Scale for Sport.
Exploratory factor analysis
Cite
Citations (13)
Confirmatory factor analysis (CFA) is a powerful and flexible statistical technique that has become an increasingly popular tool in all areas of psychology including educational research. CFA focuses on modeling the relationship between manifest (i.e., observed) indicators and underlying latent variables (factors).
Cite
Citations (74)
Compared the results of exploratory factor analysis(EFA)by SPSS software with the confirmatory factor analysis(CFA)under the structural equation model obtained that the factor loading out of the confirmatory factor analysis under the structural equation is higher than the factor loading out of confirmatory factor analysis.Best to use the two methods when make factor analysis of the data it is suggested.
Exploratory factor analysis
Factor (programming language)
LISREL
Cite
Citations (1)