A psychometric analysis of the clinical screening scale
Main Article Content
Abstract
A psychometric analysis of the SQR-20 screening scale is proposed, composed of twenty dichotomous items that assess indicators of common mental disorders (CMD). Databases from the years 2021, 2022, and 2023 were used, obtained through questionnaires applied by GAPSI and Apoia USP, answered by undergraduate students from USP São Carlos. The results indicate that the test is reliable and unidimensional, and CMD scores can be obtained both through classical test theory and the two-parameter model of item response theory (IRT). Considering IRT, the results showed that the two-parameter model was the most suitable to represent the responses, highlighting that questions related to feelings of sadness and nervousness are more discriminating than physical questions. More severe items, such as suicidal thoughts, are concerning but less effective for discrimination. The comparative analysis between sociodemographic variables, such as age, gender, and course, revealed a significant impact on the well-being of students. Additionally, the weighting of responses highlighted the importance of the course in determining the difficulties faced by students. With these results, it is expected to propose more effective interventions for promoting mental health and well-being in the academic environment.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
1. Amaral, G., Quatrochi, B., Bazán, J. L and da Pap da Silva, E. Análise Psicométrica da escala de rastreio SQR-20 usando dados do GAPSI e Apoia USP. Version v1.0. (2025). DOI: https://doi.org/10.5281/zenodo.15246854
2. Andrade, D. F., Tavares, H. R., Valle, R. C. Teoria da Resposta ao Item. Conceitos e Aplicações. Associação Brasileira de Estatística: São Paulo. (2000).
3. Azevedo, C. L., Bolfarine, H., & Andrade, D. F. Bayesian inference for a skew-normal IRT model under the centred parameterization. Computational Statistics & Data Analysis, 55(1), 353-365 (2011).
4. Bazán J. L. Psicometria e avaliação por testes: um marco metodológico. In Avaliação da educação: referências para uma primeira conversa. São Carlos, SP: EdUFSCar. (2018).
5. Bazán, J. L., Bolfarine, H., & Branco, M. D. A skew item response model. Bayesian Anal. 1(4): 861-892 (2006).
6. Bazán, J. L., Valdivieso, L. H., & Calderón, A. Enfoque Bayesiano en modelos de teoria de respuesta al ítem. (PUCP, 2010). Available in: http://argos.pucp.edu.pe/~jlbazan/download/Reportef27.pdf
7. Carmines, E. G., y Zeller, R. A. Reliability and Validity Assessment (Vol. 17). Thousand Oaks, CA: Sage. (1979).
8. Chalmers, R., P. mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29 (2012). doi: 10.18637/jss.v048.i06
9. da Silva, M. A., Bazán, J. L., & Huggins-Manley, A. C. Sensitivity analysis and choosing between alternative polytomous IRT models using Bayesian model comparison criteria. Communications in Statistics-Simulation and Computation, 48(2), 601-620 (2019).
10. de Oliveira, E. S., Wang, X., & Bazán, J. L. A classification model for continuous responses: Identifying risk perception groups on health‐related activities. Biometrical Journal, 65(4), 2100222. (2023).
11. Fernandes, R., Bazán, J. L., & Cúri, M. A Bayesian approach for the G-DINA model. Brazilian Journal of Probability and Statistics, 38(4), 503-530 (2024).
12. Gadermann, A., Guhn, M. and Zumbo, B. Estimating Ordinal Reliability for Likert-Type and Ordinal Item Response Data: A Conceptual, Empirical, and Practical Guide. Practical Assessment, Research & Evaluation, 17, 1-13 (2012).
13. Harman, Harry and Jones, Wayne (1966). Factor analysis by minimizing residuals (minres). Psychometrika, 31(3), 351-378 (1966).
14. Lumley, T. Complex Surveys: A Guide to Analysis Using R. John Wiley and Sons. (2010).
15. Lumley, T., & Scott, A. J. (2013). Two-sample rank tests under complex sampling. Biometrika, 100(4), 831-842 (2013).
16. Nunnally, Jum & Bernstein, Ira. Psychometric Theory. New York: McGraw Hill, 3rd ed. (1994).
17. Orlando, M., Sherbourne, C.D., Thissen, D. Summed-score linking using item responsetheory: Application to depression measurement, Psychological Assessment, 12(3), 354- 359 (2000).
18. Pasquali, L., & Primi, R. Fundamentos da teoria da resposta ao item: TRI. Avaliação Psicológica: Interamerican Journal of Psychological Assessment, 2(2), 99-110 (2003).
19. Pettersson, E., & Turkheimer, E. Item selection, evaluation, and simple structure in personality data. Journal of Research in Personality, 44(4), 407–420 (2010). https://doi.org/10.1016/j.jrp.2010.03.002
20. Rao, C. R., & Sinharay, S. Handbook of Statistics. Vol. 26. Psychometrics, 26(26), 235 (Elsevier, 2007).
21. Revelle, W. How To: Use the psych package for Factor Analysis and data reduction. (2022). Available in: https://cran.r-project.org/web/packages/psychTools/vignettes/factor.pdf
22. Silveira, L. B., Kroeff, C. da R., Teixeira, M. A. P., & Bandeira, D. R. Uso do Self-Reporting Questionnaire (SRQ-20) para identificação de grupo clínico e predição de risco de suicídio. Revista Psicologia E Saúde, 13(4), 49–61 (2022). https://doi.org/10.20435/pssa.v13i4.1219
23. Smeeton, N., Spencer, N., & Sprent, P. Applied nonparametric statistical methods. CRC Press. (2025).
24. Wright, B. D., & Stone, M. H. Best test design. Mesa Press. (1979).
25. Wu, T., Wu, Y., Shi, C., Qu, H., & Cui, W. Piecestack: Toward better understanding of stacked graphs. IEEE Transactions on Visualization and Computer Graphics, 22(6), 1640-1651 (2016).
26. Zumbo, Bruno D., Gadermann, Anne M., & Zeisser, Cornelia. Ordinal Versions of Coefficients Alpha and Theta for Likert Rating Scales. Journal of Modern Applied Statistical Methods, 6(1), Article 4. (2007). https://doi.org/10.22237/jmasm/1177992180