Development of Science Delay of Gratification Scale (SDOGS): Novel Amalgamation of Automated Item Generation, Genetic Algorithm, Nash equilibrium and Network Approaches in ‎Psychometrics

  • Authors

    • Rajib Chakraborty School of Education, Lovely Professional University, Phagwara, Punjab, India
    • Pavitar Parkash Singh School of Social Sciences and Languages, Lovely Professional University, Phagwara, Punjab, India https://orcid.org/0000-0001-9334-1952
    https://doi.org/10.14419/tex0pd96

    Received date: June 22, 2025

    Accepted date: July 22, 2025

    Published date: July 27, 2025

  • Automated Item Generation; Genetic Algorithm; Nash Equilibrium; Network Psychometrics; Science Delay of Gratification; Secondary School ‎Students

  • Abstract

    Delay of gratification is a critical variable of study in the context of self-regulated learning. A science domain-specific instrument to measure ‎this variable was not found in the literature. A new five-point Likert scale to measure Science Delay of Gratification was developed, purified, ‎validated, and tested for measurement invariance concerning gender and batch in 719 students (395 girls and 324 boys) studying in 9th ‎and 10th classes and belonging to the secondary schools spread across six states of India. The initial pool of 35 items was developed using ‎the neural network-based Automated Item Generation approach using ChatGPT version. 1.1.0, based on the Cognitive-Affective Personality ‎System (CAPS) theory proposed by [43], covering its three dimensions (Cognitive, Affective, and Behavioral). A novel approach of fusing ‎Genetic Algorithm and Nash equilibrium concepts was used to extract the final parsimonious version of the scale comprising 21 ‎psychometrically optimal and purified items. Network Psychometrics was used for exploring and validating the network’s structure. Its ‎invariance concerning gender and batch was also tested through a network consistency estimation technique. Appropriate packages of the ‎open-source R version 4.2.3 were used to conduct the statistical analysis. The estimates of the obtained uniclustered network suggest the scale ‎is psychometrically robust and displays invariance in measurement concerning gender and batch of the secondary school students. Open-ended responses from the sample subjects about the scale provided positive feedback about it studied using word cloud and thematic ‎analysis. Implications of the study are discussed‎.

  • References

    1. Abd-El-Fattah, S.M. & Al-Nabhani, H.Z. (2012). From Self-Theories of Intelligence to Academic Delay of Gratification: The Mediating Role ‎of Achievement Goals. Australian Journal of Educational and Developmental Psychology, 12, pp:93–107, ‎http://files.eric.ed.gov/fulltext/EJ1002250.pdf.
    2. ‎Arabzadeh, M., Kadivar, P. & Dlavar, A. (2012). The effects of teaching self-regulated learning strategy on students’ academic delay of ‎gratification. Interdisciplinary Journal of Contemporary Research in Business. 4(2), pp: 580-587.‎
    3. ‎Bembenutty, H. & Karabenick, S. A. (2004). Inherent association between academic delay of gratification, future time perspective, and Self-‎Regulated learning. Educational Psychology Review, 16(1), pp:35–57. https://doi.org/10.1023/B:EDPR.0000012344.34008.5c
    4. ‎Bembenutty, H. & Karabenick, S.A. (1998). Academic delay of gratification. Learning and Individual Differences, 10(4), pp.329–346. ‎https://doi.org/10.1016/S1041-6080(99)80126-5.
    5. ‎Bembenutty,H. & Karabenick, S.A. (1996). Academic Delay of Gratification Scale. A New Measurement for Delay of Gratification. Paper ‎presented at the Annual Meeting of the Eastern Psychological Association (Philadelphia, PA, March 1996).‎
    6. ‎Banwari, H. (2021). A Study of Various Factors that have contributed to the Alarming Surge in the Need for Instant Gratification in ‎Generation-Z. International Journal of Indian Psychology, 9(3), pp:1880-1888.‎
    7. ‎Battig, W.F. (1962). Parsimony in Psychology. Psychological Reports, 11(2), pp:555-572, ‎https://doi.org/10.2466/pr0.1962.11.2.555.
    8. ‎Bembenutty, H. & Karabenick, S.A. (2013). Self-Regulation, culture, and academic delay of gratification. Journal of Cognitive Education and ‎Psychology, 12(3), pp: 323–337. https://doi.org/10.1891/1945-8959.12.3.323.
    9. ‎Bringmann, L., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., & Tuerlinckx, F. (2013). A Network Approach to ‎Psychopathology: New Insights into Clinical Longitudinal Data. PLOS ONE, 8(4).‎ https://doi.org/10.1371/journal.pone.0060188.
    10. ‎Bhatt, S.A., Mir, A.A. & Islam,S.B. (2022). Scale Purification and Validation: A Methodological Approach to Sustainable Online Rating, The ‎Journal for Decision Makers, 47(3), pp:217-234, https://doi.org/10.1177/02560909221123632.‎
    11. ‎Bembenutty, H. (2007). Self-Regulation of learning and academic delay of gratification: gender and ethnic differences among college ‎students. Journal of Advanced Academics, 18(4), pp:586–616. https://doi.org/10.4219/jaa-2007-553.
    12. ‎Cobern W.W. & Aikenhead, G. (1997). Cultural Aspects of Learning Science. Scientific Literacy and Cultural Studies, Project. 13, Paper ‎presented at the 1997 annual meeting of the National Association for Research in Science Teaching, Chicago, March 21-24, 1997.‎
    13. ‎Chakraborty, R. & Chechi, V.K. (2024). Equivalence testing and Bayesian reliability estimation of volitional model: Instilling inferential ‎nature in structural equation modeling, 3rd International Conference on Functional Materials, Manufacturing, and Performances. AIP ‎Conference Proceed-ings. 2986, 030178-1–030178-8, https://doi.org/10.1063/5.0192649.
    14. ‎Christensen, A.P., Golino, H., & Silvia, P.J. (2020). A Psychometric Network Perspective on the Validity and Validation of Personality Trait ‎Questionnaires, European Journal of Personality, https://doi.org/10.1002/per.2265.
    15. ‎Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L.J., & Cramer, A.O.J. (2014). State of the aRt personality ‎research: A tutorial on network analysis of personality data in R, Journal of Research in Personality, https://doi.org/10.1016/j.jrp.2014.07.003.
    16. ‎Dorrenbacher, L. & Perels, F. (2015). Volition completes the puzzle: Development and evaluation of an integrative trait model of self-‎regulated learning, Frontline Learning Research, 3(4), pp:14-36.‎
    17. ‎Epskamp, S. (2016). Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection. ‎arXiv:1606.05771.‎
    18. Epskamp, S. & Fried, E.I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, pp:617–634.‎ https://doi.org/10.1037/met0000167.
    19. Epskamp, S., Borsboom, D. & Fried, E.I. (2018). “Estimating Psychological Networks and their Accuracy: A Tutorial Paper.” Behavior ‎Research Methods, 50, pp. 195–212.‎ https://doi.org/10.3758/s13428-017-0862-1.
    20. ‎Epskamp, S., Cramer, A.O.J., Waldorp, L.J., Schmittmann, V.D., & Borsboom, D. (2012). “qgraph: Network Visualizations of Relationships ‎in Psychometric Data.” Journal of Statistical Software, 48(4), pp:1–18, 2012.‎ https://doi.org/10.18637/jss.v048.i04.
    21. ‎Götz, F.M. Maertens, R., Loomba, S. & van der Linden, S. (2023). Let the Algorithm Speak: How to Use Neural Networks for Automatic ‎Item Generation in Psychological Scale Development. Psychological Methods. Advance online publication. ‎https://doi.org/10.1037/met0000540.
    22. ‎Haladyna,. T. (2004). Developing and validation multiple-choice test items. Routledge.‎ https://doi.org/10.4324/9780203825945.
    23. ‎Hommel, B.E., Wollang, F.J.M., Kotova, V., Zacher, H. & Schmukle, S.C. (2022). Transformer-based deep neural language modelling for ‎construct-specific automatic item generation. Psychometrika, 87(2), pp: 749–772.‎ https://doi.org/10.1007/s11336-021-09823-9.
    24. ‎Homburg, C., Schwemmle, M., & Kuehnl, C. (2015). “New product design: Concept, measurement, and consequences”, Journal of ‎Marketing, 79(3), pp:41–56.‎ https://doi.org/10.1509/jm.14.0199.
    25. ‎Hudson, G. & Alexander, P.C. (2024). EGAnet: Exploratory Graph Analysis – A framework for estimating the number of dimensions in ‎multivariate data using network psychometrics., R package version 2.0.7, https://r-ega.net.‎ https://doi.org/10.32614/CRAN.package.EGAnet.
    26. ‎Haslbeck, J.M.B. & Waldorp, L. J. (2020). “mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data.” Journal of ‎Statistical Software, 93(8), pp: 1–46. 2020. https://doi.org/10.18637/jss.v093.i08.
    27. Hu, L.T. & Bentler, P,M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New ‎Alternatives. Structural Equation Modeling, 6, pp:1-55. https://doi.org/10.1080/10705519909540118.
    28. Holland, P.W. & Thayer, D.T. (1988). Differential item performance and the Mantel-Haenszel procedure. In H. Wainer & H. I. Braun (Eds.), ‎Test validity (pp. 129–145). Lawrence Erlbaum Associates, Inc.‎
    29. Jain, T., Mukhopadhyay, A., Prakash, N.& Rakesh, R. (2022). Science Education and Labor Market Outcomes in a Developing Economy, ‎Economic Inquiry, 60(2). pp:741-763.‎ https://doi.org/10.1111/ecin.13044.
    30. ‎Kumar, R. & Singh, S. (2018).Aspects of science education in India: a synoptic review and possible directions for the future. Current ‎Science, 114(9), pp:1825-1828. https://doi.org/10.18520/cs/v114/i09/1825-1828.
    31. ‎Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, vol. 47, pp:263-291.‎ https://doi.org/10.2307/1914185.
    32. ‎Kivanju, C. (2014). Theoretical Perspectives of How Digital Natives Learn, International Journal of Higher Education, 3(1), pp: 94-109, ‎https://doi.org/10.5430/ijhe.v3n1p94.
    33. ‎Lee, P., Fyfee, S., Son, M., Jia, Z. & Yao, Z. (2023). A Paradigm Shift from “Human Writing” to “Machine Generation” in Personality Test ‎Development: an Application of State‑of‑the‑Art Natural Language Processing, Journal of Business and Psychology, 38, pp:163–190, ‎https://doi.org/10.1007/s10869-022-09864-6.‎
    34. Lazarsfeld, P.& Henry, N. (1968). Latent structure analysis. New York: Houghton Mifflin.‎
    35. ‎Lawshe, C.H. (1975). A Quantitative Approach to Content Validity. Personnel Psychology, 28, pp: 563-575.‎ https://doi.org/10.1111/j.1744-6570.1975.tb01393.x.
    36. ‎Lei, P..& Shiverdecker, L.K. (2019). Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data. ‎Structural Equation Modeling a Multidisciplinary Journal, 27(4), pp: 584–601. https://doi.org/10.1080/10705511.2019.1680292.‎
    37. Mischel, W. (1981). “Metacognition and rules of delay of gratification.” pp. 240-271 in Social cognitive development: Frontiers and possible ‎futures, edited by J. H. Flavell & L. Ross, New York: Cambridge University Press.‎
    38. Mischel, W. (1973), “Toward a cognitive social learning re-conceptualization of personality.” Psychological Review, 80(4), pp. 252-283.‎ https://doi.org/10.1037/h0035002.
    39. ‎Mischel, W. & Shoda,Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, ‎and in-variance in personality structure. Psychological Review, 102(2), pp.246-268, https://doi.org/10.1037/0033-295X.102.2.246.
    40. ‎Manizia, L., Borah, T., Bhattacharjee, R., Saikia, J., Dutta, A., & Boruah, J. (2023). Instant Gratification and The Digital Natives: A Pilot ‎Study, Educational Administration: Theory and Practice, 29(3), pp: 928-933, https://doi.org/10.53555/kuey.v29i3.7471.
    41. ‎Midgley, C., Feldlaufer, H., & Eccles, J.S. (1989). Student/teacher relations a attitudes toward mathematics before and after the transition to ‎junior high school. Child Development, 60, pp: 981– 992.‎ https://doi.org/10.2307/1131038.
    42. ‎Mahalanobis, P.C. (1936). On the generalized distance in statistics. Proc. Natn. Inst. Sci. India 2, pp: 49–55.‎
    43. ‎Metcalfe, J., & Mischel, W. (1999). A Hot/Cool-system analysis of delay of gratification: Dynamics of Willpower. Psychological Review, ‎‎106(1), pp:3-19.‎ https://doi.org/10.1037//0033-295X.106.1.3.
    44. ‎Nash, J.F. (1950). Equilibrium points in n -person games. Proceedings of the National Academy of Sciences, 36(1), pp: 48–49. ‎https://doi.org/10.1073/pnas.36.1.48.‎
    45. ‎Pitt, M.A. & Myung, I.J. (2002). When a good fit can be bad. Trends Cogn Sci. 6(10):421-425. ‎PMID: 12413575.‎ https://doi.org/10.1016/S1364-6613(02)01964-2.
    46. ‎Pijls,R. Groen, B.H., Galetzka,M. & Pruyn, A. T. (2017). Measuring the experience of hospitality: scale development and validation. ‎International Journal of Hospitality Management, 67(1) , pp:125–133.‎ https://doi.org/10.1016/j.ijhm.2017.07.008.
    47. Pe, M.L., Kircanski, K., Thompson, R.J., Bringmann, L., Tuerlinckx, F., Mestdagh, M., & Gotlib. I.H. (2015). Emotion-Network Density ‎inMajor Depressive Disorder. Clinical Psychological Science, 3(2), pp: 292-300.‎ https://doi.org/10.1177/2167702614540645.
    48. ‎Peetsma, T.T.D. (1997). Decline in pupils’ motivation during secondary education. Paper presented at the 7th European Conference for ‎Research on Learning and Instruction, Athens. ‎
    49. ‎Peetsma, T.T.D., Hascher, T., Van der Veen, I. & Roede, E. (2005). Relations between adolescents’ self-evaluations, time perspectives, ‎motivation for school and their achievement in different countries and at different ages. European Journal of Psychology of Education, 20, ‎pp: 209–225.‎ https://doi.org/10.1007/BF03173553.
    50. ‎[50] Peetsma, T.T.D., Schuitema, J. & Van Der Veen, I. (2012). A longitudinal study on time perspectives: Relations with academic delay of ‎gratification and learning environment, Japanese Psychological Research, 54(3), pp:242-252, https://doi.org/10.1111/j.1468-5884.2012.00526.x.
    51. ‎Podobnik, B., Crawford, G.C., Lichtenstein, B., Lipic, B.T., Wild, D., Zhang, X. & Stanley, H.E. (2020). The new wealth of nations: How ‎STEM fields generate the prosperity and inequality of individuals, companies, and countries. Chaos Solitons & Fractals, 141, 110323. ‎ https://doi.org/10.1016/j.chaos.2020.110323.
    52. ‎Prensky, M. (2001). Digital natives, digital immigrants part 1. On The Horizon, 9(5), pp:3 – 6, https://doi.org/10.1108/10748120110424816
    53. ‎Rudner, L. (2010). Implementing the Graduate Management Admission Test computerized adaptive test. In W. van der Linden & C. Glas ‎‎(Eds.), Elements of adaptive testing (pp. 151–165). Springer.‎ https://doi.org/10.1007/978-0-387-85461-8_8.
    54. ‎Ranjeeta, (2016). Attitude towards Science Education among Secondary School Students, International Journal of Research in Economics and ‎Social Sciences (IJRESS), 6 (12), pp: 393-401.‎
    55. ‎RStudio Team, (2019). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/.‎
    56. ‎Revelle, W. (2017). psych: Procedures for Personality and Psychological Research, Northwestern University.‎
    57. ‎Revelle, W. (2022). psychTools:Tools to Accompany the 'psych; Package for Psychological Research . Northwestern University, Evanston, ‎Illinois. R package version 2.2.5, https://CRAN.R-project.org/package=psychTools.‎
    58. ‎Romer,D., Duckworth, A.L., Sznitman,S. & Park, S. (2010). Can Adolescents Learn Self-control? Delay of Gratification in the Development ‎of Control over Risk Taking, Prev Sci., 11(3), pp: 319-330, https://doi.org/10.1007/s11121-010-0171-8.
    59. Scrucca, L. (2013).“GA: A Package for Genetic Algorithms in R.” Journal of Statistical Software, 53(4), pp:1–37.‎ https://doi.org/10.18637/jss.v053.i04.
    60. ‎Schroeders, U., Wilhelm, O. & Olaru,G. (2016). Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic ‎Algorithm. PLoS ONE, 11(11): e0167110. https://doi.org/10.1371/journal.pone.0167110.
    61. ‎Schmittmann, V.D., Cramer, A.O.J., Waldrop, L.J., Epskamp, S., Kievit, R.A. & Borsboom, D. (2011). Deconstructing the construct: A ‎network perspective on psychological phenomena, New Ideas in Psychology, https://doi.org/10.1016/j.newideapsych.2011.02.007.‎
    62. ‎Sinclair, S. & Rockwell, G. (2016). Voyant Tools. Web. http://voyant-tools.org/.‎
    63. ‎Soltani, A. & Askarizadeh, G. (2021). How students’ conceptions of learning science are related to their motivational beliefs and self-‎regulation. Learning and Motivation, 73, 101707. https://doi.org/10.1016/j.lmot.2021.101707.
    64. ‎Santos, L.S.Z., Souza, J.R., Alves De Cuna, J.V. & Avelino, B.C. (2018). Academic Delay of Gratification and Its Relation with Learning ‎Self-Regulation Strategies, Journal of Education and Research in Accounting, 12(2), pp: 229-249, https://doi.org/10.17524/repec.v12i2.1693.
    65. ‎Steinberg, L.A. (2008). Social Neuroscience Perspective on Adolescent Risk-Taking. Dev Rev. 28(1), pp:78-106, PMID: 18509515; PMCID: PMC2396566.‎ https://doi.org/10.1016/j.dr.2007.08.002.
    66. ‎Steidtmann, L., Kleickmann, T., & Steffensky, M. (2023). Declining interest in science in lower secondary school classes: Quasi-‎experimental and longitudinal evidence on the role of teaching and teaching quality. Journal of Research in Science Teaching, 60(1), 164–‎‎195, https://doi.org/10.1002/tea.21794.
    67. Testa, M.G. (2006). A influência das preferências por contato social e da autoregulação dos recursos de aprendizagem do estudante na ‎efetividade dos cursos desenvolvidos na Internet. Tese de doutorado, Universidade Federal do rio Grande do Sul, Porto Alegre, RS, ‎BrasilJenkins, L.(2011). Using citizen science beyond teaching science content: A strategy for making science relevant to students’ lives. ‎Cultural Studies of Science Educa-tion, 6, pp: 501–508. https://doi.org/10.1007/s11422-010-9304-4.‎
    68. Taranto, D. & Buchanan, M.T. (2020). Sustaining Lifelong Learning: A Self-Regulated Learning (SRL) Approach, Discourse and ‎Communication for Sustainable Education. 11(1), 5-15. https://doi.org/10.2478/dcse-2020-0002.
    69. ‎Vandekerckhove, J., Matzke, D., & Wagenmakers, E.J. (2015). Model comparison and the principle of parsimony. In J. R. Busemeyer, Z. ‎Wang, J. T. Townsend, & A. Eidels (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 300–319). Oxford ‎University Press.‎ https://doi.org/10.1093/oxfordhb/9780199957996.013.14.
    70. van Borkulo, C.D., van Bork, R., Boschloo, L., Kossakowski, J.J., Tio, P., Schoevers, R.A., Borsboom, D. & Waldorp, L. (2021). ‎‎“Comparing net-work structures on three aspects: A permutation test.” Psychological Methods. https://doi.org/10.1037/met0000476.
    71. ‎Wigman, J. T. W., van Os, J., Borsboom, D., Wardenaar, K. J., Epskamp, S., Klippel, A., & Wichers, M. (2015). Exploring the underlying ‎structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-‎up approach. Psychological Medicine, 45(11), pp: 2375-2387.‎ https://doi.org/10.1017/S0033291715000331.
    72. ‎Wohner, F. & Franzen, A. (2024). The Impact of Answer Scale Orientation on the Measurement of Life Satisfaction, Journal of Happiness ‎Studies, 25(78), https://doi.org/10.1007/s10902-024-00798-9.
    73. Wieland, A., Durach, C.F., Kembro, J. & Treiblmaier, H. (2017). Statistical and judgmental criteria for scale purification. Supply Chain ‎Management: An International Journal, 22(4), pp: 321–328, https://doi.org/10.1108/SCM-07-2016-0230.
    74. ‎Xie, Y., Fang, M., & Shauman, K. (2015). STEM Education. Annu Rev Sociol. 1;41:331-357. ‎Epub 2015 May 4. PMID: 26778893; PMCID: PMC4712712.‎ https://doi.org/10.1146/annurev-soc-071312-145659.
    75. Zickar, M.J. (2020). Measurement development and evaluation. Annual Review of Organizational Psychology and Organizational Behavior, ‎‎7, pp: 213–232, 2020.‎ https://doi.org/10.1146/annurev-orgpsych-012119-044957.

  • Downloads

  • How to Cite

    Chakraborty, R., & Singh, P. P. (2025). Development of Science Delay of Gratification Scale (SDOGS): Novel Amalgamation of Automated Item Generation, Genetic Algorithm, Nash equilibrium and Network Approaches in ‎Psychometrics. International Journal of Basic and Applied Sciences, 14(3), 317-331. https://doi.org/10.14419/tex0pd96