Analysis of the Relationship between Ego Identity Status and Personality Traits and General Self-Efficacy with Structural Equation Modeling
Bibliographic record
Abstract
In this study, it is aimed to investigate the partial mediating effect of ego identity status and personality traits on general self-efficacy levels of physical education and sports teacher candidates. To do that, a model showing possible relationships between these variables was established and this model was tested by using structural equation modeling. The sample of the study consisted of 578 students in the freshmen, sophomore, junior, and senior years in the Physical Education Sports Teaching Departments of universities Erciyes, Mehmet Akif, Dumlupinar, and Ahi Evran. The Socio-Demographic Information Form created by the researchers, General Self-Efficacy Scale adapted to Turkish by Aypay (2010), Ego Identity Status Scale adapted by Oskay (1998), and Eysenck Personality Questionnaire adapted to Turkish by Karancı et al. (2007) were used as the data collection tools. SPSS 22 program and Structural Equation Modeling/AMOS/Path Diagram program were used for data analysis. Firstly, the measurement models were tested and the proposed measurement model proved to be able to use to test the structural model. The structural model proposed based on the validated measurement models were then tested and verified. The fit indices of the model established in the study were found to be good and the coefficients obtained were found to be statistically significant. According to the results obtained about the structural model, it was found that there was a significant relationship between the general self-efficacy with ego identity status and personality traits, and that the ego identity status and personality traits predict the general self-efficacy in an advanced level. As a result, it is thought that the general self-efficacy levels of the students will increase with the positive development of ego identity status and personality traits, and this increase will enable the students to increase their academic achievement and self-confidence, as well as will make them more active in the society.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".