Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 4Evasion and Academic Success FactorsEducational Outcomes and InfluencesHigher Education Research Studies

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Course Dropout Intention Scale: Development and Validation of a New Brief Measure in Academic College Context

Open access
Yupanqui-Lorenzo, Daniel E., Jara-Osorio, Lizbeth Angela, Carbajal-León, Carlos, Caycho-Rodríguez, Tomás, Sernaqué, Manuel Antonio Cardoza, Quispe, Kerly Stefanny Duran·2024·European Journal of Educational Research

Abstract

University students may encounter situations where they perform poorly in a course and contemplate dropping out. This intention to drop out of a course manifests not only in thoughts or ideas but also in a cognitive self-evaluation of their performance and skills, enabling them to reflect on the possibility of dropping out. In this sense, there is a shortage of instruments that evaluate the intention to drop out of a course, so the aim was to develop and validate the Course Dropout Intention Scale (CDIS). Data from two samples (N1 = 198; N2 = 675) were used; the first was for the EFA, and the second was for the CFA, GRM, and SEM. The one-factor model was derived from the EFA and confirmed in the second sample, exhibiting appropriate goodness-of-fit indices. Similarly, the GRM obtained adequate fit indices; all items discriminated adequately, and the difficulty parameter had a monotonic increase. The SEM model of the effect of satisfaction with studies on the CDIS showed a negative and statistically significant effect. Thus, it was demonstrated that the CDIS is a robust instrument in its psychometric properties and empirical evidence with other variables.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.12973/eu-jer.13.1.103

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Regression Model of the Effect of EBSE on CDIS
Figure 1 Regression Model of the Effect of EBSE on CDISCC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 45

  1. Alves, S. A., Sinval, J., Neto, L. L., Marôco, J., Gonçalves Ferreira, A., & Oliveira, P. (2022). Burnout and dropout intention in medical students: The protective role of academic engagement. BMC Medical Education, 22, Article 83. https://doi.org/10.1186/s12909-021-03094-9 · DOI
  2. Aiken, L. R. (1980). Content validity and reliability of single items or questionnaires. Educational and Psychological Measurement, 40(4), 955–959. https://doi.org/10.1177/001316448004000419 · DOI · Paper page
  3. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds)., Action control (pp. 11–39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2 · DOI · Paper page
  4. Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Open University Press.
  5. Allen, J., & Robbins, S. B. (2008). Prediction of college major persistence based on vocational interests, academic preparation, and first-year academic performance. Research in Higher Education, 49, 62–79. https://doi.org/10.1007/s11162-007-9064-5 · DOI
  6. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (Eds.). (2014). Standards for educational and psychological testing. AERA Publications Sales.
  7. Bardach, L., Lüftenegger, M., Oczlon, S., Spiel, C., & Schober, B. (2020). Context-related problems and university students’ dropout intentions—the buffering effect of personal best goals. European Journal of Psychology of Education, 35, 477–493. https://doi.org/10.1007/s10212-019-00433-9 · DOI
  8. Beaujean, A. A. (2019). Sample size determination for regression models using Monte Carlo methods in R. Practical Assessment, Research, and Evaluation, 19, Article 12. https://doi.org/10.7275/d5pv-8v28 · DOI
  9. Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005 · DOI · Paper page
  10. Burisch, M. (1984). You don’t always get what you pay for: Measuring depression with short and simple versus long and sophisticated scales. Journal of Research in Personality, 18(1), 81–98. https://doi.org/10.1016/0092-6566(84)90040-0 · DOI
  11. Cai, L., Chung, S. W., & Lee, T. (2023). Incremental model fit assessment in the case of categorical data: Tucker–Lewis index for item response theory modeling. Prevention Science, 24, 455–466. https://doi.org/10.1007/s11121-021-01253-4 · DOI
  12. Cai, L., & Monroe, S. (2013). IRT model fit evaluation from theory to practice: Progress and some unanswered questions. Measurement: Interdisciplinary Research and Perspective, 11(3), 102–106. https://doi.org/10.1080/15366367.2013.835172 · DOI
  13. Choi, J., Fan, W., & Hancock, G. R. (2009). A note on confidence intervals for two-group latent mean effect size measures. Multivariate Behavioral Research, 44(3), 396–406. https://doi.org/10.1080/00273170902938902 · DOI
  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587 · DOI · Paper page
  15. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publishing.
  16. Díaz Mujica, A. E., Pérez Villalobos, M. V., Bernardo Gutiérrez, A. B., Fernández-Castañón, A. C., & González-Pienda, J. A. (2019). Affective and cognitive variables involved in structural prediction of university droput. Psicothema, 31(4), 429–436. https://doi.org/10.7334/psicothema2019.124 · DOI
  17. Dominguez-Lara, S. A. (2018). Propuesta de puntos de corte para cargas factoriales: Una perspectiva de fiabilidad de constructo [Proposal cut-offs for factor loadings: A construct reliability perspective]. Enfermería Clínica, 28(6), 401–402. https://doi.org/10.1016/j.enfcli.2018.06.002 · DOI
  18. Duque, L. C., Duque, J. C., & Suriñach, J. (2013). Learning outcomes and dropout intentions: an analytical model for spanish universities. Educational Studies, 39(3), 261–284. https://doi.org/10.1080/03055698.2012.724353 · DOI
  19. Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(S1), 5–18. https://doi.org/10.1007/s11136-007-9198-0 · DOI
  20. Finney, S. J., & DiStefano, C. (2013). Nonnormal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 439–492). IAP Information Age Publishing.
  21. Fishbein, M., & Ajzen, I. (2009). Predicting and changing behavior. Psychology Press. https://doi.org/10.4324/9780203838020 · DOI · Paper page
  22. Gorsuch, R. L. (1997). Exploratory factor analysis: Its role in item analysis. Journal of Personality Assessment, 68(3), 532–560. https://doi.org/10.1207/s15327752jpa6803_5 · DOI
  23. Hambleton, R. K., van der Linden, W. J., & Wells, C. S. (2010). IRT models for the analysis of polytomously scored data: brief and selected history of modelbuilding advances. In M. L. Nering & R. Ostini (Eds.), Handbook of polytomous item response models (pp. 21–42). Routledge.
  24. Hom, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffeth, R. W. (1992). A meta-analytical structural equations analysis of a model of employee turnover. Journal of Applied Psychology, 77(6), 890–909. https://doi.org/10.1037/0021-9010.77.6.890 · DOI
  25. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
  26. Litalien, D., & Guay, F. (2015). Dropout intentions in PhD studies: A comprehensive model based on interpersonal relationships and motivational resources. Contemporary Educational Psychology, 41, 218–231. https://doi.org/10.1016/j.cedpsych.2015.03.004 · DOI
  27. Marôco, J., Assunção, H., Harju-Luukkainen, H., Lin, S.-W., Sit, P.-S., Cheung, K.-C., Maloa, B., Ilic, I. S., Smith, T. J., & Campos, J. A. D. B. (2020). Predictors of academic efficacy and dropout intention in university students: Can engagement suppress burnout? PLOS ONE, 15(10), e0239816. https://doi.org/10.1371/journal.pone.0239816 · DOI
  28. Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2 · DOI · Paper page
  29. Mashburn, A. J. (2000). A psychological process of college student dropout. Journal of College Student Retention: Research, Theory & Practice, 2(3), 173–190. https://doi.org/10.2190/U2QB-52J9-GHGP-6LEE · DOI
  30. Maydeu-Olivares, A., & Joe, H. (2014). Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research, 49(4), 305–328. https://doi.org/10.1080/00273171.2014.911075 · DOI
  31. McDonald, R. P. (1999). Test theory: A unified treatment. Taylor & Francis. https://doi.org/10.4324/9781410601087 · DOI
  32. Merino-Soto, C., Dominguez-Lara, S., & Fernández-Arata, M. (2017). Validación inicial de una Escala Breve de Satisfacción con los Estudios en estudiantes universitarios de Lima [Initial validation of a Brief Satisfaction with Studies Scale in university students in Lima]. Educación Médica, 18(1), 74–77. https://doi.org/10.1016/j.edumed.2016.06.016 · DOI
  33. Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., Eisman, E. J., Kubiszyn, T. W., & Reed, G. M. (2001). Psychological testing and psychological assessment: A review of evidence and issues. American Psychologist, 56(2), 128–165. https://doi.org/10.1037/0003-066X.56.2.128 · DOI · Paper page
  34. Noman, M., Kaur, A., & Nafees, N. (2021). Covid-19 fallout: Interplay between stressors and support on academic functioning of Malaysian university students. Children and Youth Services Review, 125, 106001. https://doi.org/10.1016/j.childyouth.2021.106001 · DOI
  35. Raykov, T., & Hancock, G. R. (2005). Examining change in maximal reliability for multiple-component measuring instruments. British Journal of Mathematical and Statistical Psychology, 58(1), 65–82. https://doi.org/10.1348/000711005X38753 · DOI
  36. Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315 · DOI · Paper page
  37. Ryan, R. M., & Deci, E. L. (2017). Self-determination Theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. https://doi.org/10.1521/978.14625/28806 · DOI · Paper page
  38. Samejima, F. (1997). Graded response model. In W. J. van der Linden & R. K. Hambleton (Eds). Handbook of modern item response theory (pp. 85–100). Springer. https://doi.org/10.1007/978-1-4757-2691-6_5 · DOI
  39. Samuel, R., & Burger, K. (2020). Negative life events, self-efficacy, and social support: Risk and protective factors for school dropout intentions and dropout. Journal of Educational Psychology, 112(5), 973–986. https://doi.org/10.1037/edu0000406 · DOI
  40. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338 · DOI · Paper page
  41. Schumacker, R. E., & Lomax, R. G. (2015). A beginner’s guide to structural equation modeling (4th ed.). Routledge. https://doi.org/10.4324/9781315749105 · DOI · Paper page
  42. Tayebi, A., Gomez, J., & Delgado, C. (2021). Analysis on the lack of motivation and dropout in engineering students in spain. IEEE Access, 9, 66253–66265. https://doi.org/10.1109/ACCESS.2021.3076751 · DOI
  43. Tinto, V. (1994). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press. https://doi.org/10.7208/chicago/9780226922461.001.0001 · DOI
  44. Woodard, D. B., Mallory, S. L., & De Luca, A. M. (2001). Retention and institutional effort: A self-study framework. NASPA Journal, 39(1), 53–83. https://doi.org/10.2202/1949-6605.1159 · DOI
  45. Yuan, K.-H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30(1), 165–200. https://doi.org/10.1111/0081-1750.00078 · DOI

Cited by

See all 4

Metadata

Authors
Yupanqui-Lorenzo, D. E., Jara-Osorio, L. A., Carbajal-León, C., Caycho-Rodríguez, T., Sernaqué, M. A. C., & Quispe, K. S. D.
Affiliations
Peruvian University of Applied Sciences (PE)
Venue
European Journal of Educational Research
Published
2024-01-15
Volume
volume-13-2024
Issue
volume-13-issue-1-january-2024
Pages
103-113
ISSN
2165-8714 (ISSN-L)
OpenAlex
W4387427118
Cited by
4
References
2
Citations by year
2026: 2 · 2025: 1 · 2024: 1
FWCI
1.16
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • European Journal of Educational Research (publishedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Education › Evasion and Academic Success Factors
  • Social Sciences › Education › Educational Outcomes and Influences
  • Social Sciences › Education › Higher Education Research Studies
Keywords
scale development, psychometric validation, university students, student attrition

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Course Dropout Intention Scale: Development and Validation of a New Brief Measure in Academic College Context. https://app.cochat.ai/papers/10.12973/eu-jer.13.1.103
The paper
Yupanqui-Lorenzo, D. E., Jara-Osorio, L. A., Carbajal-León, C., Caycho-Rodríguez, T., Sernaqué, M. A. C., & Quispe, K. S. D. (2024). Course Dropout Intention Scale: Development and Validation of a New Brief Measure in Academic College Context. European Journal of Educational Research. https://doi.org/10.12973/eu-jer.13.1.103