Structural Equation Modeling of Mental Toughness Among University Learners
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".