Innovación en evaluación longitudinal de percepciones económicas en universidades

Francisco Jacobo Murillo-López; Oyuki María A. Calvillo-Félix

doi:10.55965/setp.6.12.a1

Authors

Francisco Jacobo Murillo-López Universidad Autónoma de Aguascalientes, Aguascalientes, México. https://orcid.org/0009-0005-2104-2634
Oyuki María A. Calvillo-Félix El Colegio de Morelos, Cuernavaca, Morelos, México. https://orcid.org/0009-0003-6974-7858

DOI:

https://doi.org/10.55965/setp.6.12.a1

Keywords:

longitudinal assessment, economic perceptions, subjective social status, latent growth curve modeling

Abstract

Context. Perceived inflation (INF) and subjective socioeconomic status (PSS) shape university students' experiences in economically volatile contexts.

Problem. Most studies rely on cross-sectional designs, limiting the understanding of temporal dynamics and individual change trajectories. How do these constructs evolve longitudinally among university students, and how does objective socioeconomic status (SES) condition their trajectories?

Purpose. This study examines the temporal dynamics of INF and PSS through a parallel latent growth curve model, generating multidisciplinary knowledge aligned with the OECD Oslo Manual and the Sustainable Development Goals 4 and 10.

Methodology. A longitudinal study was conducted during the January–June 2026 semester at the Universidad Autónoma de Aguascalientes (UAA), Mexico, with 500 university students over 16 weeks (five waves). A Conditional Parallel Latent Growth Curve Model (MLR, FIML) was estimated, incorporating SES as an exogenous predictor of intercepts and slopes.

Findings. Theoretical findings (Scientia): INF increased linearly: μₛ = 0.658, p < .001; while PSS decreased linearly: μₛ = -0.862, p < .001; with significant variability in both slopes: σ² = 0.038 (INF) and σ² = 0.045 (PSS). SES was positively associated with the baseline level of PSS: β = 0.710, p < .001; but, paradoxically, negatively predicted its slope: β = -0.105, p = .002. The model showed marginal fit: CFI = 0.945, TLI = 0.938, RMSEA = 0.066 (90% CI: 0.059–0.073), SRMR = 0.050. SES explained 50.4% of the initial variance of PSS (R² = 0.504), but only 8.2% of its slope (R² = 0.082). Practical finding (Praxis): The results supported longitudinal monitoring and differentiated support based on students’ trajectories of economic perceptions and subjective socioeconomic status.

Originality. The research integrates economic psychology, longitudinal econometrics, and educational policy through a methodological innovation based on a parallel LGCM; the application of a longitudinal monitoring and analysis method aligns with the process innovation guidelines of the Oslo Manual.

Conclusions and limitations. The model shows marginal fit, suggesting the need to incorporate time-varying covariates in future multidisciplinary studies.

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Author Biographies

Francisco Jacobo Murillo-López, Universidad Autónoma de Aguascalientes, Aguascalientes, México.

Research-Professor at the Universidad Autónoma de Aguascalientes, Aguascalientes, México.

Oyuki María A. Calvillo-Félix, El Colegio de Morelos, Cuernavaca, Morelos, México.

Research-Professor at the El Colegio de Morelos, Cuernavaca, Morelos, México.

References

Barenca-Sotelo, C. U., Maciel-Arellano, M. R., & Larios-Rosillo, V. M. (2026). Toward a sustainable innovation in the faculty evaluation process: A systematic review of AI, data science, and NLP applications in higher education. Scientia et PRAXIS, 6(11). https://doi.org/10.55965/setp.6.11.a1

Bruine de Bruin, W., van der Klaauw, W., & Topa, G. (2011). Expectations of inflation: The biasing effect of thoughts about specific prices. Journal of Economic Psychology, 32(5), 834-845. https://doi.org/10.1016/j.joep.2011.07.002

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464-504. https://doi.org/10.1080/10705510701301834

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233-255. https://doi.org/10.1207/S15328007SEM0902_5

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587

Coibion, O., Gorodnichenko, Y., & Weber, M. (2023). Monetary policy communications and their effects on household inflation expectations. Journal of Political Economy, 131(1), 153-207. https://doi.org/10.1086/720982

D'Acunto, F., Malmendier, U., & Weber, M. (2019). What do the data tell us about inflation expectations? Journal of Economic Perspectives, 33(3), 199-228. https://doi.org/10.1257/jep.33.3.199

D'Acunto, F., Malmendier, U., & Weber, M. (2021). Gender roles produce divergent economic expectations. Proceedings of the National Academy of Sciences, 118(21), e2008534118. https://doi.org/10.1073/pnas.2008534118

Loeb, E., & Hurd, N. M. (2019). Subjective Social Status, Perceived Academic Competence, and Academic Achievement Among Underrepresented Students. Journal of College Student Retention: Research, Theory & Practice, 21(2), 150–165. https://doi.org/10.1177/1521025117696821

Di Tella, R., Haisken-De New, J., & MacCulloch, R. (2010). Happiness adaptation to income and to status in an individual panel. Journal of Economic Behavior & Organization, 76(3), 834–852. https://doi.org/10.1016/j.jebo.2010.09.016

Hernández-Rodríguez, T. M., & Mora-Pérez, C. O. (2026). Gender, culture of peace and citizen participation as evidence-based social innovation for sustainable urban governance. Scientia et PRAXIS, 6(11). https://doi.org/10.55965/setp.6.11.a3

Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling (R package version 0.5-6). https://CRAN.R-project.org/package=semTools

Lilly, K. J., Sibley, C. G., & Osborne, D. (2023). Examining the between- and within-person effects of relative deprivation: Results from a 7-year longitudinal panel sample. European Journal of Social Psychology, 53(3), 435–449. https://doi.org/10.1002/ejsp.2913

Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198-1202. https://doi.org/10.1080/01621459.1988.10478722

Matthews, K. A., & Gallo, L. C. (2011). Psychological Perspectives on Pathways Linking Socioeconomic Status and Physical Health. Annual Review of Psychology, 62, 501–530. https://doi.org/10.1146/annurev.psych.031809.130711

McNeish, D. (2020). Should We Use F-Tests for Model Fit Instead of Chi-Square in Overidentified Structural Equation Models? Organizational Research Methods, 23(3), 487–510. https://doi.org/10.1177/1094428118809495

Murillo-López, F. J. (2026). Emotions and machine learning in innovation within Mexico's sustainable used-vehicle market. Scientia et PRAXIS, 6(11). https://doi.org/10.55965/setp.6.11.a2

Navarro-Carrillo, G., Alonso-Ferres, M., Moya, M., & Valor-Segura, I. (2020). Socioeconomic status and psychological well-being: Revisiting the role of subjective socioeconomic status. Frontiers in Psychology, 11, 1303. https://doi.org/10.3389/fpsyg.2020.01303

OECD/Eurostat. (2018). Oslo Manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD Publishing. https://doi.org/10.1787/9789264304604-en

Pettigrew, T. F. (2016). In pursuit of three theories: Authoritarianism, relative deprivation, and intergroup contact. Annual Review of Psychology, 67, 1–21. https://doi.org/10.1146/annurev-psych-122414-033327

Quon, E. C., & McGrath, J. J. (2014). Subjective socioeconomic status and adolescent health: A meta-analysis. Health Psychology, 33(5), 433–447. https://doi.org/10.1037/a0033716

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). United Nations. https://sdgs.un.org/2030agenda

Weber, M., D'Acunto, F., Gorodnichenko, Y., & Coibion, O. (2022). The subjective inflation expectations of households. Journal of Economic Perspectives, 60(3), 829-873. https://doi.org/10.1257/jep.36.3.157

West, S. G., Wu, W., McNeish, D., & Savord, A. (2023). Model fit in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 184–202). Guilford Press.

Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal, 11(2), 49–54. https://doi.org/10.21315/eimj2019.11.2.6

Zell, E., Strickhouser, J. E., & Krizan, Z. (2018). Subjective social status and health: A meta-analysis of community and student samples. Health Psychology, 37(10), 945-956. https://doi.org/10.1037/hea0000667