A validation study of the State Self-Esteem Scale-20 (SSES-20) and the State Self-Esteem Scale-6 (SSES-6) in a Spanish-speaking sample
Bibliographic record
Abstract
The State Self-Esteem Scale has not been studied in the Spanish population yet. Our objective was to assess the factor structure and internal consistency of the State Self-Esteem Scale (SSES-20 and SSES-6) in a Spanish-speaking sample. The second objective was to determine its convergent and discriminant validity by examining its relationships with variables such as trait self-esteem, social desirability, depression, and anxiety. The sample consisted of 713 Spaniards (77.2% female; Mean Age = 25.32 years). Findings suggest that a bifactor model with a general factor and three subdimensions provided a better fit for SSES-20 data. For the SSES-6 version, a hierarchical model with three non-correlated first-order factors and a common hierarchical factor was found to be the best fit. All dimensions exhibited moderate to excellent reliability. All factors were positively linked to trait self-esteem and social desirability, while inversely related to depression and state anxiety. Finally, performance, appearance, and social state self-esteem dimensions from SSES-20 negatively predicted depression and state anxiety using linear regression models. Both Spanish versions, SSES-20 and SSES-6, demonstrated adequate psychometric properties within this sample, suggesting potential generalizability to diverse Spanish populations. La Escala de Autoestima Estado no se había estudiado en la población española. Nuestro objetivo fue evaluar la estructura factorial y consistencia interna de la Escala de Autoestima Estado (SSES-20 y SSES-6) en una muestra de hablantes de español. El segundo objetivo fue determinar su validez convergente y discriminante examinando su relación con variables como la autoestima rasgo, deseabilidad social, depresión y ansiedad. La muestra consistió en 713 españoles (79.4% mujeres; Edad Media = 25.32 años). Los resultados sugieren que un modelo bifactor con un factor general y tres subdimensiones obtuvo un mejor ajuste para los datos del SSES-20. Para la versión SSES-6, un modelo jerárquico con tres factores de primer orden no correlacionados y un factor jerárquico común fue el que obtuvo mejor ajuste. Todas las dimensiones exhibieron una fiabilidad entre moderada y excelente. Todos los factores se correlacionaron positivamente con la autoestima rasgo y la deseabilidad social, mientras que se relacionaron inversamente con la depresión y la ansiedad estado. Finalmente, las dimensiones de autoestima de estado de rendimiento, apariencia y social del SSES-20 predijeron positivamente la depresión y la ansiedad estado usando modelos de regresión lineal. Ambas versiones españolas del SSES-20 y SSES-6 demostraron propiedades psicométricas adecuadas en esta muestra, sugiriendo unaposible generalización a diversas poblaciones hispanas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".