The research, published in the journal BMC Psychology, was led by Aishah Moraya Asiri of the Department of Statistics and Research Methods, together with Fatimah Essa Q. Yahya, Nada Alamry and Walaa Badawy. The team recruited 412 undergraduates at King Khalid University during the 2025–2026 academic year. Of an initial 450 responses, 38 were excluded because of missing questionnaire or demographic data, and the authors note that these cases were dropped without imputation, meaning no values were estimated or filled in to preserve the sample size. All participants completed two well-established instruments: the Wong and Law Emotional Intelligence Scale, known as the WLEIS, and the short form of the Cognitive Emotion Regulation Questionnaire, or CERQ-short. The study was approved by the university’s Research Ethics Committee under approval number ECM#2024–3107 in August 2025, and electronic informed consent was obtained from every participant before the survey began.

What makes this study methodologically interesting is the analytical tool the researchers chose: latent profile analysis, or LPA. Unlike traditional approaches that examine how variables correlate across a whole sample, LPA is a person-centred technique. It searches for unobserved subgroups of individuals who share similar patterns of scores across multiple dimensions. In this case, the four WLEIS dimensions were fed into the analysis: self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion. The central question was whether these dimensions would combine into qualitatively distinct configurations, for example students who are skilled at reading emotions in others but poor at regulating their own, or whether the profiles would simply mirror one another at different overall elevations.

Model selection in latent profile analysis is a careful balancing act, and the researchers took it seriously. They compared solutions with different numbers of profiles using information criteria such as the Akaike information criterion, the Bayesian information criterion and the sample-size-adjusted Bayesian information criterion, alongside the bootstrap likelihood ratio test and the Lo-Mendell-Rubin adjusted likelihood ratio test. They also examined entropy, a measure of how accurately individuals are assigned to profiles, and checked classification accuracy and parsimony. The winning model was a three-profile solution with an entropy of 0.91, a very high value indicating clean separation between groups, and average posterior probabilities ranging from 0.93 to 0.96. In plain terms, the model could confidently place almost every student into the right group.

The three profiles themselves were strikingly simple. The first group, comprising 91 students or 22.1 percent of the sample, showed lower scores across the board. The second and largest group, with 221 students or 53.6 percent, occupied an intermediate position. The third group of 100 students, or 24.3 percent, scored highest. Crucially, the profile means were ordered consistently on all four emotional intelligence dimensions, meaning that students high in self-emotion appraisal were also high in regulation of emotion, use of emotion and others’ emotion appraisal, and so on down the scale. There was no evidence of the crossed or idiosyncratic patterns that would signal genuinely different emotional types. The dimensions, in this sample, largely rose and fell together.

With the profiles established, the team turned to the second half of the puzzle: how do these emotional intelligence levels relate to the cognitive strategies people use to manage their feelings? The CERQ-short measures nine such strategies, ranging from adaptive ones like positive reappraisal, refocusing on planning, positive refocusing and putting into perspective, to less helpful ones such as catastrophising, rumination, self-blame and blaming others. To compare strategies across profiles without inflating confidence by treating uncertain group memberships as certain, the researchers used the Bolck-Croon-Hagenaars method, or BCH, which explicitly accounts for classification error. They applied a Bonferroni correction across nine omnibus tests to guard against false positives, and characterised the size of differences using approximate eta-squared values, confidence intervals and standardized mean differences.

The results painted a clear gradient. After correction, seven of the nine strategies differed significantly across profiles. The strongest associations appeared for catastrophising, with an eta-squared of approximately 0.13, and positive reappraisal at 0.12, followed by refocus on planning and putting into perspective at 0.09 each, self-blame at 0.08, positive refocusing at 0.07 and rumination at 0.06, all with adjusted p-values below 0.001. Students in the higher emotional intelligence profile reported more adaptive strategies and fewer maladaptive ones than their lower-scoring peers. The standardized differences between the highest and lowest profiles ranged from about 0.76 for rumination to 1.13 for catastrophising, which are large effects by conventional psychological standards. Two strategies, acceptance and blaming others, showed no reliable differences at all, with adjusted p-values of 1.000.

The implications cut both ways. On one hand, the graded pattern is encouraging for anyone who uses emotional intelligence questionnaires in research or practice. If profiles differ mainly in level rather than shape, then a single continuous WLEIS score may be sufficient for many purposes, simplifying assessment and interpretation. There is no need, the authors suggest, to sort students into elaborate emotional archetypes before drawing conclusions. On the other hand, the strong links between higher emotional intelligence and adaptive cognitive regulation strategies, particularly the reduced tendency to catastrophise, hint at practical targets for student support programmes, since catastrophising and rumination are well-known risk factors for anxiety and depression. The authors are careful, however, to note that cross-sectional self-report data cannot establish causality or demonstrate the effectiveness of profile-based support.

The study also carries important caveats about its scope. It was conducted at a single university in Saudi Arabia, so the findings may not generalise to other cultural or educational settings, and all measures relied on self-report, which is vulnerable to bias and to the possibility that students who believe they are emotionally skilled also believe they regulate emotions well. The authors received no external funding for the work and declare no competing interests. Still, as one of the first person-centred analyses to test directly whether emotional intelligence subtypes exist among university students, the study delivers a refreshingly sober message: in this sample, emotional intelligence is not a set of hidden types waiting to be discovered, but a continuum on which students are spread, with their habits of emotional thought rising and falling in step along the way.

Subject of Research: Latent profile analysis of self-reported emotional intelligence and cognitive emotion regulation strategies among university students

Article Title: Profiles of self-reported emotional intelligence and cognitive emotion regulation strategies among university students: a latent profile analysis

Article References: Profiles of self-reported emotional intelligence and cognitive emotion regulation strategies among university students: a latent profile analysis. (n.d.). https://doi.org/10.1186/s40359-026-05664-2

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05664-2

Keywords: emotional intelligence, emotion regulation, latent profile analysis, university students, cognitive emotion regulation, psychometrics, person-centred analysis, Saudi Arabia, WLEIS, CERQ-short, psychology, self-report