Course Dropout Intention Scale: Development and Validation of a New Brief Measure in Academic College Context
Daniel E. Yupanqui-Lorenzo
,
Lizbeth Angela Jara-Osorio
,
Carlos Carbajal-León
,
Tomás Caycho-Rodríguez
,
Manuel Antonio Cardoza Sernaqué
,
Kerly Stefanny Duran Quispe
University students may encounter situations where they perform poorly in a course and contemplate dropping out. This intention to drop out of a cours.
- Pub. date: January 15, 2024
- Pages: 103-113
- 342 Downloads
- 395 Views
- 0 Citations
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.
Keywords: Brief measure, college student, course dropout, dropout intention, dropout studies.
References
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
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
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
Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Open University Press.
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
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (Eds.). (2014). Standards for educational and psychological testing. AERA Publications Sales.
Ato, M., López-García, J. J., & Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología [A classification system for research designs in psychology]. Anales de Psicología, 29(3), 1038–1059. https://doi.org/10.6018/analesps.29.3.178511
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
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
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
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
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
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
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
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publishing.
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
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
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
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
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates Publishers.
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.
Fishbein, M., & Ajzen, I. (2009). Predicting and changing behavior. Psychology Press. https://doi.org/10.4324/9780203838020
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
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.
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
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
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
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
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
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
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
McDonald, R. P. (1999). Test theory: A unified treatment. Taylor & Francis. https://doi.org/10.4324/9781410601087
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
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
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
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
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
Rumberger, R. W. (2011). Dropping out. Harvard University Press. https://doi.org/10.4159/harvard.9780674063167
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
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
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
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
Schumacker, R. E., & Lomax, R. G. (2015). A beginner’s guide to structural equation modeling (4th ed.). Routledge. https://doi.org/10.4324/9781315749105
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
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
United Nations Educational, Scientific and Cultural Organization. (2020). COVID-19 education response: How many students are at risk of not returning to school? Advocacy paper. https://unesdoc.unesco.org/ark:/48223/pf0000373992
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
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