scholarly journals Subjective Perceptions of the Teaching–Research Nexus and Occupational Stress at Universities

2018 ◽  
Vol 50 (3) ◽  
pp. 126-138 ◽  
Author(s):  
Martin Daumiller ◽  
Markus Dresel
Keyword(s):  
Structural Equation Modeling ◽  
Occupational Stress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Subjective Perceptions ◽  
Research And Teaching ◽  
University Systems ◽  
Teaching Research

Abstract. The duality of research and teaching is fundamental for university systems; however, it is often experienced as stressful. A study with 819 university scholars indicated the importance of subjective perceptions of the teaching–research nexus. Such views could be differentiated in perceived integration (e. g., research is part of teaching) and perceived synergy (e. g., research enriches teaching). Structural equation modeling showed that only perceived synergy was associated with reduced occupational stress.

scholarly journals Subjective perceptions of the teaching-research nexus and occupational stress at universities

2019 ◽  
Author(s):  
Martin Daumiller ◽  
Markus Dresel
Keyword(s):  
Structural Equation Modeling ◽  
Occupational Stress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Subjective Perceptions ◽  
Research And Teaching ◽  
University Systems ◽  
Teaching Research

The duality of research and teaching is fundamental for university systems; however, it is often experienced as stressful. A study with 819 university scholars indicated the importance of subjective perceptions of the teaching–research nexus. Such views could be differentiated in perceived integration (e. g., research is part of teaching) and perceived synergy (e. g., research enriches teaching). Structural equation modeling showed that only perceived synergy was associated with reduced occupational stress.

scholarly journals Handling Time in Occupational Stress and Well-Being Research: Considerations, Examples, and Recommendations

2021 ◽  
Author(s):  
Rachel Sisu Rauvola ◽  
Cort Rudolph ◽  
Hannes Zacher
Keyword(s):  
Structural Equation Modeling ◽  
Occupational Health ◽  
Occupational Stress ◽  
Structural Equation ◽  
Handling Time ◽  
Well Being ◽  
Equation Modeling ◽  
Time Lags ◽  
Lagged Effects

In this chapter, we consider the role of time for research in occupational stress and well-being. We first discuss temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision). Then, we discuss time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches. Finally, we provide recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health.

scholarly journals Vocational Misfit: Source of Occupational Stress Among Accountants

2011 ◽  
Vol 13 (3) ◽  
pp. 43 ◽  
Author(s):  
G. R. Cluskey, Jr. ◽  
Alan Vaux
Keyword(s):  
Structural Equation Modeling ◽  
Occupational Stress ◽  
Survey Data ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Self Esteem ◽  
Equation Model ◽  
Job Dissatisfaction ◽  
Equation Modeling ◽  
Management Accountants

<span>The career path of management accountants involves rotation through disparate positions that, we hypothesized, puts these professionals at risk for episodes of vocational misfit. Survey data from 188 management accountants were examined through correlational analyses and structural equation modeling. As predicted, many respondents (40%) met Hollands (1985b) criterion for vocational misfit. Poor vocational fit was significantly associated in correlational analysis with job dissatisfaction, how self-esteem, and turnover, and in a structural equation model, with a stress process that included both stressors and strains.</span>

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.

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