scholarly journals A Mediation Analysis of the Association between Fundamental Motor Skills and Physical Activity during Middle Childhood

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 64
Author(s):  
Xiangli Gu ◽  
Priscila M. Tamplain ◽  
Weiyun Chen ◽  
Tao Zhang ◽  
M. Jean Keller ◽  
...  
Keyword(s):  
Physical Activity ◽  
Motor Skills ◽  
Middle Childhood ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Fit Indices ◽  
Fundamental Motor Skills ◽  
Behavioral Mechanism ◽  
Ball Skills

The purposes of the study were: (1) to investigate the associations between fundamental motor skills (FMS), health-related fitness (HRF) and physical activity (PA) during middle childhood; and (2) to examine whether HRF serves as a mediator in these pathways. The participants were 342 children (156 girls; Mage = 8.40, SD = 0.50) recruited in Texas. Children’s FMS (locomotor and ball skills) were assessed. School-based PA that included light, moderate, and vigorous PA was captured by accelerometers. The FITNESSGRAM battery was used to measure children’s HRF, including body composition, cardiorespiratory fitness, and muscular fitness. Structural equation models were used to evaluate two proposed models (model-1 = FMS»HRF»PA; model-2 = PA»HRF»FMS). Both locomotor and ball skills were associated with all components of HRF (p < 0.01), but not PA. The SEM analyses supported associations between FMS, HRF and PA, with sound goodness-of-fit indices: (1) model-1: CFI = 0.95; RMSEA = 0.072; and (2) model-2: CFI = 0.95; RMSEA = 0.071, respectively. The relationship between FMS and PA was fully mediated by the HRF in both directions. The behavioral mechanism (e.g., maintaining appropriate levels of HRF) provides meaningful insights to understand the obesity trajectory during middle childhood.

scholarly journals COVID-19 Concerns and Personality of Commerce Workers: Its Influence on Burnout

2021 ◽  
Vol 13 (22) ◽  
pp. 12908
Author(s):  
Ana María Rodríguez-López ◽  
Susana Rubio-Valdehita
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Online Survey ◽  
Structural Equation Models ◽  
Burnout Syndrome ◽  
Hierarchical Regression ◽  
Fit Indices ◽  
Cross Sectional ◽  
Sociodemographic Variables ◽  
Cross Sectional Design

We analyze burnout in a sample of commercial workers in Spain and its relationship with sociodemographic variables, personality, and concern about the influence of the COVID-19 pandemic on their jobs through a cross-sectional design. Participants (n = 614) answered an online survey, including questions about sociodemographic data, concern, NEO-FFI (personality), and MBI (burnout syndrome). The survey took place from October 2020 to May 2021. We assessed the relationships between sociodemographic variables, pandemic concern, and personality as predictors of burnout by hierarchical regression analysis and then tested using SEM (structural equation models). The proposed model showed adequate goodness-of-fit indices. The results of the present study suggest that the COVID-19 pandemic had little effect to the development of burnout syndrome in commerce employees. However, in agreement with previous literature, the present study shows that personality has a significant role in predicting burnout. Neuroticism, introversion, conscientiousness, and agreeableness were strong predictors for burnout dimensions. In addition, we found that personality directly affected the pandemic concern: individuals with high levels of Neuroticism and low levels of extraversion, agreeableness, and conscientiousness have more pandemic concerns. In conclusion, personality is an important factor that affects the level of workers’ concern about the influence of the pandemic on their job and the development of burnout syndrome. Furthermore, although we found significant differences between groups formed by various sociodemographic characteristics, the conclusion regarding this type of variable is that their ability to predict burnout is deficient.

scholarly journals Does Perceived Competence Mediate between Ball Skills and Children’s Physical Activity and Enjoyment?

Children ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 575
Author(s):  
Tao Zhang ◽  
Joonyoung Lee ◽  
Lisa M. Barnett ◽  
Xiangli Gu
Keyword(s):  
Physical Activity ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Perceived Competence ◽  
Equation Modeling ◽  
School Students ◽  
Major Purpose ◽  
Motor Skill Competence ◽  
Children’S Physical Activity ◽  
Ball Skills

The major purpose of this study was to examine the potential mediating role of perceived motor skill competence on relationships between actual ball skills and children’s physical activity (PA) and PA enjoyment. A total of 294 students (Mage = 10.96 ± 0.76; 51.7% boys) from three elementary schools completed validated questionnaires assessing their perceived competence, self-reported PA, and PA enjoyment. Students’ actual ball skills (i.e., basketball, overhand throwing, striking) were measured by PE MetricsTM. Correlation analyses showed positive relationships among the study variables (rs ranging from 0.12 to 0.56). The structural equation modeling (SEM) analyses demonstrated that the mediation model produces a goodness-of-fit to the data: χ2/df = 52.03/32; CFI = 0.96; NFI = 0.90; IFI = 0.96, RMSEA = 0.05, SRMR = 0.04. Path coefficients suggested that actual ball skill competence was strongly associated with perceived competence (β = 0.36, p < 0.01), which in turn significantly predicted PA (β = 0.29, p < 0.01) and PA enjoyment (β = 0.35, p < 0.01). The findings highlight that ball skills significantly impact students’ perceived competence, positively and indirectly affecting their PA and PA enjoyment. This study provides empirical evidence that recommends intervention strategies aimed at fostering elementary school students’ PA and PA enjoyment.

Should We Use F-Tests for Model Fit Instead of Chi-Square in Overidentified Structural Equation Models?

2018 ◽  
Vol 23 (3) ◽  
pp. 487-510 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Data Model ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Small Sample ◽  
Model Fit ◽  
Ratio Test ◽  
Fit Indices ◽  
Chi Square ◽  
Advantages And Disadvantages

Debate continues about whether the likelihood ratio test ( T ML) or goodness-of-fit indices are most appropriate for assessing data-model fit in structural equation models. Though potential advantages and disadvantages of these methods with large samples are often discussed, shortcomings concomitant with smaller samples are not. This article aims to (a) highlight the broader small sample issues with both approaches to data-model fit assessment, (b) note that what constitutes a small sample is common in empirical studies (approximately 20% to 50% in review studies, depending on the definition of “small”), and (c) more widely introduce F-tests as a desirable alternative than the traditional T ML tests, small-sample corrections, or goodness-of-fit indices with smaller samples. Both goodness-of-fit indices and comparing T ML to a chi-square distribution at smaller samples leads to overrejection of well-fitting models. Simulations and example analyses show that F-tests yield more desirable statistical properties—with or without normality—than standard approaches like chi-square tests or goodness-of-fit indices with smaller samples, roughly defined as N < 200 or N: df < 3.

Evaluation of goodness-of-fit indices for structural equation models.

1989 ◽  
Vol 105 (3) ◽  
pp. 430-445 ◽  
Author(s):  
Stanley A. Mulaik ◽  
Larry R. James ◽  
Judith Van Alstine ◽  
Nathan Bennett ◽  
Sherri Lind ◽  
...  
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Fit Indices

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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