Validation of a socioemotional battery for online higher education applicants

Authors

DOI:

https://doi.org/10.12795/revistafuentes.2026.28828

Keywords:

academic success, emotional regulation, factor analysis, higher education, online education, psychometric validation, socioemotional variables

Abstract

Introduction: This study aimed to validate an institutional socioemotional battery for online bachelor's degree applicants in Mexico, given the importance of these variables for academic success. Method: Two samples participated: Sample 1 (N=5272) for exploratory analysis and Sample 2 (N=2227) for confirmatory analysis. A 73-item self-report battery was initially administered. Data analysis involved item screening, exploratory graph analysis, and exploratory factor analysis on Sample 1, followed by confirmatory factor analysis on Sample 2 to validate the structure and assess reliability. Results: The final 30-item battery yielded seven stable dimensions: academic self-efficacy, family support, growth expectations, emotional expression, expressive suppression, emotional dysregulation, and adaptive emotional regulation. The model fit was excellent in both samples, with strong factor loadings, distinct inter-factor correlations, and high reliability. Most scales exhibited skewed distributions. Discussion: The identified dimensions align with literature on online academic success. The reduced, validated instrument enhances measurement efficiency and provides crucial early information on socioemotional factors. The combined use of network psychometric and traditional factor analytical methods strengthens the findings. Conclusions: This battery is a valid tool for assessing socioemotional characteristics in online learning, enabling early identification of risks and informing pedagogical strategies.

Downloads

Download data is not yet available.

References

Bandalos, D. L., & Finney, S. J. (2019). Factor analysis. Exploratory and confirmatory. In G. R. Hancock, L. M. Stapleton, & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (2nd ed., pp. 98–122). Routledge.

Beaumont, J., Putwain, D. W., Gallard, D., Malone, E., Marsh, H. W., & Pekrun, R. (2023). Students’ emotion regulation and school-related well-being: Longitudinal models juxtaposing between- and within-person perspectives. Journal of Educational Psychology, 115(7), 932–950. https://doi.org/10.1037/edu0000800

Borsboom, D., Cramer, A. O. J., Fried, E. I., Isvoranu, A.-M., Robinaugh, D. J., Dalege, J., & Van Der Maas, H. L. J. (2022). Network perspectives. In A.-M. Isvoranu, S. Epskamp, L. J. Waldorp, & D. Borsboom, Network Psychometrics with R (1st ed., pp. 9–27). Routledge. https://doi.org/10.4324/9781003111238-2

Borup, J., Graham, C. R., West, R. E., Archambault, L., & Spring, K. J. (2020). Academic Communities of Engagement: An expansive lens for examining support structures in blended and online learning. Educational Technology Research and Development, 68(2), 807–832. https://doi.org/10.1007/s11423-020-09744-x

Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460–470. https://doi.org/10.1080/00273171.2014.933697

Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research, 47(6), 904–930. https://doi.org/10.1080/00273171.2012.715252

Christensen, A. P., & Golino, H. (2021). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial. Psych, 3(3), 479–500. https://doi.org/10.3390/psych3030032

De Neve, D., Bronstein, M. V., Leroy, A., Truyts, A., & Everaert, J. (2023). Emotion regulation in the classroom: A network approach to model relations among emotion regulation difficulties, engagement to learn, and relationships with peers and teachers. Journal of Youth and Adolescence, 52(2), 273–286. https://doi.org/10.1007/s10964-022-01678-2

Engin, M. (2017). Analysis of students’ online learning readiness based on their emotional intelligence level. Universal Journal of Educational Research, 5(12A), 32–40. https://doi.org/10.13189/ujer.2017.051306

Ferraces Otero, M. J., Lorenzo Moledo, M., Godás Otero, A., & Santos Rego, M. A. (2020). Students’ mediator variables in the relationship between family involvement and academic performance: Effects of the styles of involvement. Psicología Educativa, 27(1), 85–92. https://doi.org/10.5093/psed2020a19

Ferrando, P. J., Hernandez-Dorado, A., & Lorenzo-Seva, U. (2022). Detecting correlated residuals in exploratory factor analysis: New proposals and a comparison of procedures. Structural Equation Modeling: A Multidisciplinary Journal, 29(4), 630–638. https://doi.org/10.1080/10705511.2021.2004543

Ferrando, P. J., & Lorenzo-Seva, U. (2000). Unrestricted versus restricted factor analysis of multidimensional test items: Some aspects of the problem and some suggestions. Psicológica, 21(3), 301–323. https://bit.ly/40RAVNc

Ferrando, P. J., Lorenzo-Seva, U., & Bargalló-Escrivà, M. T. (2023). Gulliksen’s pool: A quick tool for preliminary detection of problematic items in item factor analysis. PLoS ONE, 18(8), e0290611. https://doi.org/10.1371/journal.pone.0290611

Gao, H., Ou, Y., Zhang, Z., Ni, M., Zhou, X., & Liao, L. (2021). The relationship between family support and e-learning engagement in college students: The mediating role of e-learning normative consciousness and behaviors and self-efficacy. Frontiers in Psychology, 12, 573779. https://doi.org/10.3389/fpsyg.2021.573779

Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035

Harley, J. M., Pekrun, R., Taxer, J. L., & Gross, J. J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 106–126. https://doi.org/10.1080/00461520.2019.1587297

Hassan Abuhassna, Freed Awae, Kawthar Bayoumi, Diaya Uddeen Alzitawi, Ahmed H Alsharif, & Noraffandy Yahaya. (2022). Understanding online learning readiness among university students: A bibliometric analysis. International Journal of Interactive Mobile Technologies (iJIM), 16(13), 81–94. https://doi.org/10.3991/ijim.v16i13.30605

Henry, M. (2020). Online student expectations: A multifaceted, student-centred understanding of online education. Student Success, 11(2), 91–98. https://doi.org/10.5204/ssj.1678

Hernández González, C. A., & Blackford, B. J. (2022). Engagement as antecedent of academic achievement and the moderating impact of work-family-school inter-role conflict for online graduate students. The International Journal of Management Education, 20(3), 100676. https://doi.org/10.1016/j.ijme.2022.100676

Hernández Gutiérrez, M., & Enríquez Vázquez, L. (2022). Habilidades del futuro para los estudiantes del SUAyED [Skills for the future in students at SUAyED]. Revista Digital Universitaria, 23(6). https://doi.org/10.22201/cuaieed.16076079e.2022.23.6.2

Hernández Ortiz, S. E., Edel Navarro, R., & Esquivel Gámez, I. (2023). Agentes de la educación en línea asociados con la permanencia del estudiante [Online educational agents associated with student retention]. Educación Superior, 34, 133–157. https://doi.org/10.56918/es.2022.i34.pp133-157

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

James, J. L. (2022). Students as stakeholders: Understanding expectations can increase student retention. Journal of College Student Retention: Research, Theory & Practice, 24(1), 20–42. https://doi.org/10.1177/1521025119898844

Kundu, A. (2020). Toward a framework for strengthening participants’ self-efficacy in online education. Asian Association of Open Universities Journal, 15(3), 351–370. https://doi.org/10.1108/AAOUJ-06-2020-0039

Landrum, B., Bannister, J., Garza, G., & Rhame, S. (2021). A class of one: Students’ satisfaction with online learning. Journal of Education for Business, 96(2), 82–88. https://doi.org/10.1080/08832323.2020.1757592

Lehikko, A. (2021). Measuring self-efficacy in immersive virtual learning environments: A systematic literature review. Journal of Interactive Learning Research, 32(2), 125–146. https://doi.org/10.70725/672759jhzbkw

Li, S. (2024). Learning analytics enhanced online learning support. Routledge.

Lim, J. R. N., Rosenthal, S., Sim, Y. J. M., Lim, Z.-Y., & Oh, K. R. (2021). Making online learning more satisfying: The effects of online-learning self-efficacy, social presence and content structure. Technology, Pedagogy and Education, 30(4), 543–556. https://doi.org/10.1080/1475939X.2021.1934102

Matteucci, M. C., & Soncini, A. (2021). Self-efficacy and psychological well-being in a sample of Italian university students with and without Specific Learning Disorder. Research in Developmental Disabilities, 110, 103858. https://doi.org/10.1016/j.ridd.2021.103858

Mejri, S., & Borawski, S. (2023). Predictors of persistence and success in online education. International Journal on E-Learning, 22(3), 239–257. https://doi.org/10.70725/807249yyafpy

Mensah, C., Kugbonu, M. A., Appietu, M. E., Nti, G. A., & Forson, M. A. (2024). Social support, computer self-efficacy, online learning engagement and satisfaction among undergraduate hospitality students. Cogent Education, 11(1), 2335803. https://doi.org/10.1080/2331186X.2024.2335803

Nadeem, A., Umer, F., & Anwar, M. J. (2023). Emotion regulation as predictor of academic performance in university students. Journal of Professional & Applied Psychology, 4(1), 20–33. https://doi.org/10.52053/jpap.v4i1.157

Neroni, J., Meijs, C., Kirschner, P. A., Xu, K. M., & De Groot, R. H. M. (2022). Academic self-efficacy, self-esteem, and grit in higher online education: Consistency of interests predicts academic success. Social Psychology of Education, 25(4), 951–975. https://doi.org/10.1007/s11218-022-09696-5

Ozer, S. (2024). Social support, self‐efficacy, self‐esteem, and well‐being during COVID-19 lockdown: A two‐wave study of Danish students. Scandinavian Journal of Psychology, 65(1), 42–52. https://doi.org/10.1111/sjop.12952

Panigrahi, R., Srivastava, P. R., & Panigrahi, P. K. (2021). Effectiveness of e-learning: The mediating role of student engagement on perceived learning effectiveness. Information Technology & People, 34(7), 1840–1862. https://doi.org/10.1108/ITP-07-2019-0380

Pekrun, R., Marsh, H. W., Suessenbach, F., Frenzel, A. C., & Goetz, T. (2023). School grades and students’ emotions: Longitudinal models of within-person reciprocal effects. Learning and Instruction, 83, 101626. https://doi.org/10.1016/j.learninstruc.2022.101626

Yang, D., Chen, P., Wang, K., Li, Z., Zhang, C., & Huang, R. (2023). Parental involvement and student engagement: A review of the literature. Sustainability, 15(7), 5859. https://doi.org/10.3390/su15075859

Yokoyama, S. (2024). Impact of academic self-efficacy on online learning outcomes: A recent literature review. EXCLI Journal, 23. https://doi.org/10.17179/EXCLI2024-7502

Yu, J., Huang, C., He, T., Wang, X., & Zhang, L. (2022). Investigating students’ emotional self-efficacy profiles and their relations to self-regulation, motivation, and academic performance in online learning contexts: A person-centered approach. Education and Information Technologies, 27(8), 11715–11740. https://doi.org/10.1007/s10639-022-11099-0

Yunusa, A. A., & Umar, I. N. (2021). A scoping review of Critical Predictive Factors (CPFs) of satisfaction and perceived learning outcomes in E-learning environments. Education and Information Technologies, 26(1), 1223–1270. https://doi.org/10.1007/s10639-020-10286-1

Published

2026-01-19

How to Cite

De la Rosa Gómez, A., Valencia, P. D., Ochoa Carrasco, I., & Razo-Ruvalcaba, A. C. (2026). Validation of a socioemotional battery for online higher education applicants. Revista Fuentes, 28(1), 14–27. https://doi.org/10.12795/revistafuentes.2026.28828

Issue

Section

Investigaciones