Structural Equation Modeling of Mental Toughness Among University Learners
Notice bibliographique
Résumé
BACKGROUND: Mental toughness is recognized as an important component towards academic success thus making its psychological qualities determine how challenges are effectively addressed during pressurized situations. Challenges facing undergraduate learners in the context of mental toughness had been broadly investigated mostly in developed countries. Most of the studies centered on sports and descriptive findings lack critical analysis. OBJECTIVE: The main objective of the current study was to investigate the level of mental toughness of university learners and the impact on the learners' academic performance. The current study also investigated whether university learners who were reported with greater mental toughness are more likely to be academically successful than those lower in mental toughness. METHODS: This quantitative study employed SmartPls 3 software to predict the significant level of motivation, self-reliance, concentration and coping with pressure on academic performance among university learners. Two universities were considered for assessing the structural equation modeling of mental toughness. Additionally, sources of data included reviews of different books on the related topics, research studies, articles, journals, newspapers, and magazines. Substantial information has been gathered from these sources thus allowing for appropriate analysis, compilation, interpretation, and structuring of the entire study. Thus, in an attempt to isolate and categorize potential attributes of mental toughness and its impact on academic performance, the available literature reviewed. This quantitative study considered adoptable in handling bias findings. A sample size of 417 considered appropriate for a variance based structural equation modeling. A total number of 417 responses gathered from Angeles University Foundation (AUF) and Baliuag University (BU), Philippines considered for this mental toughness study. RESULTS: A total of a 75 percent from the questionnaires (477) returned from a sum 600 questionnaires distributed to specified respondents. Demographic details report that female responded with round-off 60%, this implied that female strive more in education than male. Ages 17-20 occupied 55% nursing/medicine marked around of 34% to top among the six colleges investigated in this study, next was college of business and administration marked around of 20% to take second place. This study suggested that students considered more to be medical doctors, professional nurses and business practitioners in the future rather than being professional teachers or system engineers. Reliability and validity of this study reported according to the Smart-Pls algorithm factor matrix, Cronbach's alpha, rho_A, and composite reliability all above 0.7 thresholds. Also, the average variance extracted from 0.5 achieved. The discriminant validity of this study based on Fornell-Lackner criterion, factor loading at 0.6 above and Heterotrait Monotrait Ratio quality achieved. Conclusively, all supported path coefficients significant at the p-values < 0.01. In a nutshell, partial least squares algorithm reported about a 58% variance explained from the entire structured model. CONCLUSION: The adopted factors for this structural equation modeling of mental toughness for university learners achieved fifty-eight percent variance explained in the study. Future studies can be directed towards replicating the use of this model in other locations and different analytical techniques.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».