Data - The Trait Meta-Mood Scale-24 (TMMS-24) and Academic Performance
Description
Relationship Between the Trait Meta-Mood Scale-24 (TMMS-24) and Academic Performance: This dataset contains the anonymized raw data from a cross-sectional study investigating the psychometric properties of the Trait Meta-Mood Scale-24 (TMMS-24) and its relationship with academic indicators among university students. The study was conducted with a sample of N = 491 undergraduate health science students from multiple regional centers in El Salvador. The dataset includes: Sociodemographic and Academic Variables: Age, sex, regional center, faculty, year of study, economic support, employment status, self-reported academic performance (Nota_real), academic aspiration (Nota_ideal), and strategies for coping with academic stress and procrastination. Responses to the TMMS-24 Items: Complete raw responses to the 24 items of the Trait Meta-Mood Scale, which measures intrapersonal emotional intelligence across three theoretical dimensions: Attention to Feelings, Clarity of Feelings, and Mood Repair. Derived Psychometric Variables: Composite scores and categorical levels (e.g., Low, Adequate, High) for the three TMMS-24 factors (Emotional Attention, Emotional Clarity, Emotional Repair), generated following exploratory (EFA) and confirmatory factor analysis (CFA). Potential for Reuse: This dataset is valuable for researchers interested in: The cross-cultural validation and factor structure of the TMMS-24. The relationship between emotional intelligence, academic performance, and student coping strategies. Meta-analyses on emotional intelligence in higher education, particularly in Latin American contexts. Educational and psychological research on the well-being of health science students.
Files
Steps to reproduce
Step-by-Step List for TMMS-24 Data Analysis Phase 1: Sample Characterization Perform data processing and cleaning (identify and handle missing values, outliers). Generate descriptive statistics (frequencies, percentages) for all sociodemographic variables (age, sex, study center, etc.). Calculate measures of central tendency and dispersion (mean, median, standard deviation) for quantitative variables (e.g., age, study hours). Phase 2: Psychometric Validation of the Instrument (TMMS-24) Step 2.1: Exploratory Factor Analysis (EFA) 4. Verify the assumptions for the EFA: * Calculate Bartlett's test of sphericity (must be significant, *p* < .05). * Calculate the KMO index (must be > 0.70, ideally > 0.80). 5. Run the EFA using the Unweighted Least Squares (ULS) extraction method. 6. Apply Promax rotation (assuming correlation between factors). 7. Determine the number of factors to retain using the eigenvalue > 1 criterion and the scree plot. 8. Interpret the rotated solution, retaining items with factor loadings > 0.60 on a single factor. Step 2.2: Confirmatory Factor Analysis (CFA) 9. Specify the measurement model in CFA software (e.g., AMOS) based on the EFA results (e.g., 3 factors with their assigned items). 10. Run the CFA using the MLR estimator (Maximum Likelihood with Robust Standard Errors). 11. Evaluate the model's goodness-of-fit indices: * CFI and TLI > 0.90 (acceptable) or > 0.95 (optimal). * RMSEA < 0.08 (acceptable) or < 0.05 (optimal) with its confidence interval. * SRMR < 0.08. 12. If the fit is not adequate, review the modification indices (MI) and perform theoretically justified readjustments (e.g., remove items with low loadings or add error covariances). Step 2.3: Reliability (Internal Consistency) 13. Calculate Cronbach's alpha (α) and McDonald's omega (ω) for each factor and for the total scale (if applicable). 14. Interpret the values: α/ω > 0.70 (acceptable), > 0.80 (good), > 0.90 (excellent). Phase 3: Relationship and Contrast Analysis 15. Create composite scores (e.g., item mean) for each TMMS-24 factor (Attention, Clarity, Repair) from the final model. 16. Perform association analysis using the Chi-square (χ²) test to cross the factors (in categorical levels: Low, Adequate, High) with qualitative variables of interest (e.g., coping strategies). 17. Perform group difference analysis using the Kruskal-Wallis H test to compare TMMS-24 factor scores among three or more independent groups (e.g., levels of academic performance). If the result is significant, perform post hoc pairwise comparisons.
Institutions
- Universidad Dr Andres Bello