STRUCTURAL EQUATION MODELING OF DETERMINANTS OF SATISFACTION AMONG SAUDI FEMALE ENGINEERING STUDENTS
Keywords:
Structural Equation Modeling (SEM), Confirmatory Factor Analysis (CFA), Exploratory Factor Analysis (EFA), reliability, validity, student satisfaction, engineering, higher educationDOI:
https://doi.org/10.17654/0972361725067Abstract
Quality of education emerged as an important factor that fosters sustainable development and long-term national progress. One way to assess is through student satisfaction, which has been a subject of interest for researchers and educational organizations for decades, as it has been considered as a true reflection of the quality of education. This study utilized Structural Equation Modeling (SEM) to investigate the factors influencing student satisfaction at the College of Engineering Female Campus at Saudi University. The study instrument was used to collect data from 2017 to 2022 resulting in 5577 responses. The participants were requested to provide feedback using a five-point Likert scale. The exploratory factor analysis revealed four factors: teaching quality, assessment and course tools, practical sessions, and satisfaction. The application of SEM indicates a positive relation between the practical session and teaching effectiveness. In addition, a positive relationship exists between teaching effectiveness and an indirect relationship between the practical session and satisfaction, mediated by teaching effectiveness.
Received: August 29, 2025
Revised: September 17, 2025
Accepted: September 23, 2025
References
[1] J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks and S. Ray, An Introduction to Structural Equation Modeling, Springer, 2021, pp. 1-29.
doi: 10.1007/978-3-030-80519-7_1.
[2] N. Pavlović, Practical problems of the SEM application in social sciences-reporting research results, Romanian Journal of Sociological Studies 1 (2021), 81-93.
[3] D. D. Suhr, SEM for health, business and education, Proceedings from the 27th Annual SAS® Users Group International Conference, 2002, Paper 243-27.
[4] Albert Surya Wanasida, I. Bernarto, N. Sudibjo and A. Purwanto, The role of business capabilities in supporting organization agility and performance during the COVID-19 pandemic: an empirical study in Indonesia, Agus PURWANTO/Journal of Asian Finance 8(5) (2021), 897-0911. doi: 10.13106/jafeb.2021.vol8.no5.0897.
[5] M. S. Talukder, G. Sorwar, Y. Bao, J. U. Ahmed and M. A. S. Palash, Predicting antecedents of wearable healthcare technology acceptance by elderly: a combined SEM-neural network approach, Technological Forecasting and Social Change, Vol. 150, 2020, pp. 1-37.
[6] M. Madhavan, M. A. Sharafuddin and T. Chaichana, Impact of business model innovation on sustainable performance of processed marine food product SMEs in Thailand-a PLS-SEM approach, Sustainability (Switzerland) 14(15) 2022, 1-29. doi: 10.3390/su14159673.
[7] M. H. Asif, T. Zhongfu, B. Ahmad, M. Irfan, A. Razzaq and W. Ameer, Influencing factors of consumers’ buying intention of solar energy: a structural equation modeling approach, Environmental Science and Pollution Research 30(11) (2023), 30017-30032. doi: 10.1007/s11356-022-24286-w.
[8] A. Waqar, I. Othman and J. C. Pomares, Impact of 3D printing on the overall project success of residential construction projects using structural equation modelling, Int. J. Environ. Res. Public Health 20(5) (2023), 1-25. doi: 10.3390/ijerph20053800.
[9] F. Sudirjo, L. K. Candra Dewi, W. Desty Febrian, I. Sani and D. Dharmawan, The measurement analysis of online service quality toward state banking customers using structural equation modeling, Jurnal Informasi Dan Teknologi 6(1) (2024), 50-56. doi: 10.60083/jidt.v6i1.471.
[10] I. M. Salinda Weerasinghe, R. Lalitha and S. Fernando, Students satisfaction in higher education literature review, Am. J. Educ. Res. 5(5) (2017), 533-539. doi: 10.12691/education-5-5-9.
[11] P. Rahmatpour, H. Peyrovi and H. Sharif Nia, Development and psychometric evaluation of postgraduate nursing student academic satisfaction scale, Nurs. Open 8(3) (2021), 1145-1156. doi: 10.1002/nop2.727.
[12] H. W. Marsh, Students evaluations of university teaching: research findings, methodological issues, and directions for future research. International Journal of Educational Research 11(3) (1987), 253-388.
[13] H. K. Wachtel, Student evaluation of college teaching effectiveness: a brief review, Assessment & Evaluation in Higher Education 23(2) (1998), 191-212. doi: 10.1080/0260293980230207.
[14] H. Dodeen, Student evaluations of instructors in higher education: a structural equation modeling, 2016, 1-15. [Online].
Available: http://www.aabri.com/copyright.html
[15] S. Kaur, G. Singh and A. Garg, Evaluating the relationship between the course experience questionnaire and student satisfaction: a case from India, J. Public Aff. 22(3) (2022), 1-13. doi: 10.1002/pa.2471.
[16] A. K. Paswan and J. A. Young, Student evaluation of instructor: a nomological investigation using structural equation modeling, Journal of Marketing Education 24(3) (2002), 193-202.
[17] W. H. Wong and E. Chapman, Student satisfaction and interaction in higher education, High Educ. (Dordr) 85(5) (2023), 957-978.
doi: 10.1007/s10734-022-00874-0.
[18] A. A. Gora, S. C. Ştefan, Ş. C. Popa and C. F. Albu, Students’ perspective on quality assurance in higher education in the context of sustainability: a PLS-SEM approach, Sustainability (Switzerland) 11(17) (2019), 1-21.
doi: 10.3390/su11174793.
[19] A. S. Neto, M. J. P. Dantas and R. L. Machado, Structural equation modeling applied to assess industrial engineering students’ satisfaction according to ENADE 2011, Production 27 (2017), 1-14. doi: 10.1590/0103-6513.219116.
[20] B. Sojkin, P. Bartkowiak and A. Skuza, Determinants of higher education choices and student satisfaction: the case of Poland, High Educ. (Dordr) 63(5) (2012), 565-581. doi: 10.1007/s10734-011-9459-2.
[21] F. de O. Santini, W. J. Ladeira, C. H. Sampaio and G. da Silva Costa, Student satisfaction in higher education: a meta-analytic study, Journal of Marketing for Higher Education 27(1) (2017), 1-18. doi: 10.1080/08841241.2017.1311980.
[22] J. J. E. Morales-Cervantes, A. B. Urbina-Najera and L. F. Olachea-Parra, Factors influencing the satisfaction and persistence of university engineering students, Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 1-5. doi: 10.1109/EIRCON52903.2021.9613689.
[23] I. Borch, R. Sandvoll and T. Risør, Discrepancies in purposes of student course evaluations: what does it mean to be satisfied? Educ. Assess Eval. Account 32(1) (2020), 83-102. doi: 10.1007/s11092-020-09315-x.
[24] R. D. Abbott, V. A. Ropp, D. H. Wulff, J. D. Nyquist and C. W. Hess, Satisfaction with processes of collecting student opinions about instruction: the student perspective, Journal of Educational Psychology 82(2) (1990), 201-206.
[25] N. C. Hai, Factors affecting student satisfaction with higher education service quality in Vietnam, European Journal of Educational Research 11(1) (2022), 339-351. doi: 10.12973/EU-JER.11.1.339.
[26] T. Abdalla Mohammed and S. Muhammed Pandhiani, Analysis of factors affecting student evaluation of teaching effectiveness in Saudi higher education: the case of Jubail University College, Am. J. Educ. Res. 5(5) (2017), 464-475. doi: 10.12691/education-5-5-2.
[27] H. W. Marsh and L. A. Roche, Making students evaluations of teaching effectiveness effective the critical issues of validity, bias, and utility, American Psychologist 52(11) (1997), 1-11.
[28] M. Stringer and P. Irwing, Students evaluations of teaching effectiveness: a structural modelling approach, British Journal of Educational Psychology 68(3) (1998), 409-426. doi: 10.1111/j.2044-8279.1998.tb01301.x.
[29] H. S. Lukman, A. Setiani and N. Muhassanah, Structural equation modelling of teaching quality on students’ satisfaction, Journal of Physics: Conference Series, IOP Publishing Ltd 1657(1) (2020), 1-7. doi: 10.1088/1742-6596/1657/1/012083.
[30] H. H. Turhangil Erenler, A structural equation model to evaluate students learning and satisfaction, Computer Applications in Engineering Education 28(2) (2020), 254-267. doi: 10.1002/cae.22189.
[31] N. S. Edward, The role of laboratory work in engineering education: student and staff perceptions, International Journal of Electrical Engineering & Education 39(1) (2002), 11-19.
[32] R. S. Lange, T. Pascarella and P. Terenzin, How college affects students, a third decade of research, Journal of Student Affairs in Africa 2(2) (2006), 1-4. doi: 10.14426/jsaa.v2i2.70.
[33] H. Matusovich, R. Streveler, R. Miller and B. Olds, I’m graduating this year! So what is an engineer anyway? Paper presented at 2009 Annual Conference & Exposition, 2009, pp. 1-4. doi: 10.18260/1-2--5142.
[34] S. K. Parahoo, H. L. Harvey and R. M. Tamim, Factors influencing student satisfaction in universities in the Gulf region: does gender of students matter? Journal of Marketing for Higher Education 23 (2013), 135-154. doi: 10.1080/08841241.2013.860940.
[35] F. Silva and P. O. Fernandes, Empirical study on the student satisfaction in higher education: importance-satisfaction analysis, Management 293(2) (2012), 306-315. doi: 10.5539/ijbm.v3n9p46.
[36] H. Dodeen, College students’ evaluation of effective teaching: developing an instrument and assessing its psychometric properties, Research in Higher Education (2013), 1-12.
[37] S. Farahmandian, Perceived service quality and student satisfaction in higher education, IOSR Journal of Business and Management 12 (2013), 65-74. doi: 10.9790/487x-1246574.
[38] S. Wilkins and M. S. Balakrishnan, Assessing student satisfaction in transnational higher education, International Journal of Educational Management 27(2) (2013), 143-156. doi: 10.1108/09513541311297568.
[39] C. Darawong and M. Sandmaung, Service quality enhancing student satisfaction in international programs of higher education institutions: a local student perspective, Journal of Marketing for Higher Education 29(2) (2019), 268-283. doi: 10.1080/08841241.2019.1647483.
[40] S. Khan, S. I. Zaman and M. Rais, Measuring student satisfaction through overall quality at business schools: a structural equation modeling, South Asian Journal of Social Review (2022), 34-55. doi: 10.57044/sajsr.2022.1.2.2210.
[41] T. González-Ramírez and A. García-Hernández, Design and validation of a questionnaire to assess student satisfaction with mathematics study materials1, International Journal of Instruction 15(1) (2022), 1-20.
doi: 10.29333/iji.2022.1511a.
[42] A. Rizwan, M. Hammouda, I. Alvi and M. M. I. Hammouda, Analysis of factors affecting the satisfaction level of engineering students, International Journal of Engineering Education 24(4) (2008), 811-816.
[Online]. Available: https://www.researchgate.net/publication/263602682.
[43] M. Daumiller, S. Janke, J. Hein, R. Rinas, O. Dickhäuser and M. Dresel, Teaching quality in higher education: agreement between teacher self-reports and student evaluations, European Journal of Psychological Assessment 39(3) (2023), 176-181, doi: 10.1027/1015-5759/a000700.
[44] S. Culver and V. Tech, Course grades, quality of student engagement, and students’ evaluation of instructor, International Journal of Teaching and Learning in Higher Education 22(3) (2010), 331-336.
[Online]. Available: http://www.isetl.org/ijtlhe/.
[45] Ó. Martín Rodríguez, F. González-Gómez and J. Guardiola, Do course evaluation systems have an influence on e-learning student satisfaction? Higher Education Evaluation and Development 13(1) (2019), 18-32.
doi: 10.1108/heed-09-2018-0022.
[46] J. Wei et al., Design and validation of an instrument to measure students’ interactions and satisfaction in undergraduate chemistry laboratory classes, Res. Sci. Educ. 51(4) (2021), 1039-1053. doi: 10.1007/s11165-020-09933-x.
[47] N. Zakaria, R. Umar, W. H. A. W. Deraman and S. S. S. A. Mutalib, Regression analysis on factors influencing students’ satisfaction towards program courses, Indian J. Sci. Technol. 9(17) (2016), 1-5. doi: 10.17485/ijst/2016/v9i17/88720.
[48] B. A. Altaf, Multiple linear regression of factors influencing female engineering students’ satisfaction: an application in Saudi Arabia, Adv. Appl. Stat. 91(12) (2024), 1531-1554. doi: 10.17654/0972361724078.
[49] D. Dragan and D. Topolšek, Introduction to structural equation modeling: review, methodology and practical applications, The International Conference on Logistics & Sustainable Transport, Vol. 6, 2014, pp. 1-27.
[50] S. Alotaibi and D. Roussinov, User acceptance of M-government services in Saudi Arabia: an SEM approach, Conference Paper, 2017, pp. 1-12.
[51] M. A. Almaiah et al., Investigating the effect of perceived security, perceived trust, and information quality on mobile payment usage through near-field communication (NFC) in Saudi Arabia, Electronics (Switzerland) 11(23) (2022), 1-22. doi: 10.3390/electronics11233926.
[52] A. S. Alshebami and A. H. A. Seraj, Exploring the influence of potential entrepreneurs’ personality traits on small venture creation: the case of Saudi Arabia, Front Psychol. 13 (2022), 1-9. doi: 10.3389/fpsyg.2022.885980.
[53] M. Wasiq, M. Kamal and N. Ali, Factors influencing green innovation adoption and its impact on the sustainability performance of small- and medium-sized enterprises in Saudi Arabia, Sustainability (Switzerland) 15(3) (2023), 1-22. doi: 10.3390/su15032447.
[54] U. Alturki and A. Aldraiweesh, An empirical investigation into students’ actual use of MOOCs in Saudi Arabia higher education, Sustainability (Switzerland) 15(8) (2023), 1-23. doi: 10.3390/su15086918.
[55] I. A. Alsaggaf and N. Abdulgabar, The impact of motivation on mathematics achievement of Saudi students using structure equation method and mediation analysis, International Journal of Advanced and Applied Sciences 11(3) (2024), 251-264. doi: 10.21833/ijaas.2024.03.024.
[56] A. A. Naji, B. Altaf and A. Alkhouli, Modeling cognitive and non-cognitive factors that influence students’ reading achievement in Saudi Arabia: a structural equation modeling analysis of PISA (2018), International Journal of Advanced and Applied Sciences 11(8) (2024), 1-18. doi: 10.21833/ijaas.2024.08.001.
[57] A. Almutairi and B. Altaf, A structural equation modeling of the impact of teacher self-efficacy on teachers’ teaching practice in Saudi Arabia: evidence from TALIS 2018, European Journal of Pure and Applied Mathematics 17(3) (2024), 2001-2027. doi: 10.29020/nybg.ejpam.v17i3.5278.
[58] J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson and R. L. Tatham, Multivariate Data Analysis, 6th ed., Upper Saddle River, NJ: Pearson Prentice Hall, 2006.
[59] A. S. Beavers, J. W. Lounsbury, J. K. Richards, S. W. Huck, G. J. Skolits and S. L. Esquivel, Practical considerations for using exploratory factor analysis in educational research, Tabachnick and Fidell 18(6) (2013), 1-13.
[60] L. R. Fabrigar, D. T. Wegener, R. C. Maccallum and E. J. Strahan, Evaluating the use of exploratory factor analysis in psychological research, Psychological Methods 4 (1999), 272-299.
[61] J. F. Hair, W. C. Black, B. J. Babin and R. E. Anderson, Multivariate Data Analysis, 7th ed., Pearson Education, 2010.
[62] H. W. Marsh et al., Exploratory structural equation modeling, integrating CFA and EFA: application to students’ evaluations of university teaching, Structural Equation Modeling 16(3) (2009), 439-476. doi: 10.1080/10705510903008220.
[63] A. Ponnam, D. Sahoo, A. Sarkar and S. N. Mohapatra, An exploratory study of factors affecting credit card brand and category selection in India, Journal of Financial Services Marketing 19(3) (2014), 221-233. doi: 10.1057/fsm.2014.17.
[64] F. Zeynivandnezhad, F. Rashed and A. Kanooni, Exploratory factor analysis for TPACK among mathematics teachers: why, what and how, Anatolian Journal of Education 4(1) (2019), 1-18. doi: 10.29333/aje.2019.416a.
[65] Demir, Comparison of normality tests in terms of sample sizes under different skewness and kurtosis coefficients, International Journal of Assessment Tools in Education 9(2) (2022), 397-409. doi: 10.21449/ijate.1101295.
[66] Marwan Ghaleb and Muhsin Murat Yaslioglu, Structural equation modeling (SEM) for social and behavioral sciences studies: steps sequence and explanation, Journal of Organizational Behavior Review 6(1) (2024), 69-108.
[67] K. A. Pituch and James P. Stevens, Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Routledge, 2016.
[68] B. Williams, A. Onsman and T. Brown, Exploratory factor analysis: a five-step guide for novices, Journal of Emergency Primary Health Care 8(3) (2010), 1-13. doi: 10.33151/ajp.8.3.93.
[69] A. Gie Yong and S. Pearce, A beginner’s guide to factor analysis: focusing on exploratory factor analysis, Tutorials in Quantitative Methods for Psychology 9 (2013), 79-94.
[70] L. Sürücü, İ. Yikilmaz and A. Maslakçi, Exploratory factor analysis (EFA) in quantitative researches and practical considerations, Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 13(2) (2022), 947-965. [Online]. Available: https://orcid.org/0000-0001-6820-4673.
[71] N. J. Hamed Taherdoost and Shamsul Sahibuddin, Advances in applied and pure mathematics, Proceedings of the 2nd International Conference on Mathematical, Computational and Statistical Sciences (MCSS’14); Proceedings of the 7th International Conference on Finite Differences, Finite Elements, Finite, Vol. 27, 2020, pp. 375-382.
[72] D. Iskamto, P. L. Ghazali, A. Aftanorhan and A. T. Bon, Exploratory factor analysis (EFA) to measure entrepreneur satisfaction, Proc. Int. Conf. Industrial Engineering and Operations Management, 2020, pp. 1-9.
[73] R. K. Henson and J. K. Roberts, Use of Exploratory Factor Analysis in Published Research: Common Errors and Some Comment on Improved Practice, SAGE Publications Inc., 2006, pp. 1-25. doi: 10.1177/0013164405282485.
[74] F. J. Floyd and K. F. Widaman, Factor analysis in the development and refinement of clinical assessment instruments, Psychological Assessment 7 (1995), 286-299.
[75] D. L. Streiner, Being inconsistent about consistency: when coefficient alpha does and doesn’t matter, Lawrence Erlbaum Associates Inc. 80(3) (2003), 217-222. doi: 10.1207/S15327752JPA8003_01.
[76] K. Bannigan and R. Watson, Reliability and validity in a nutshell, J. Clin. Nurs. 18(23) (2009), 3237-3243, doi: 10.1111/j.1365-2702.2009.02939.x.
[77] M. D. Manzar, H. A. Jahrami and A. S. Bahammam, Structural validity of the insomnia severity index: a systematic review and meta-analysis, Sleep Medicine Reviews 60 (2021), 101531. doi: 10.1016/j.smrv.2021.101531.
[78] T. Yu and J. C. Richardson, An exploratory factor analysis and reliability analysis of the student online learning readiness (SOLR) instrument, Online Learning Journal 19(5) (2015), 1-137. doi: 10.24059/olj.v19i5.593.
[79] J. M. Cortina, What is coefficient alpha? An examination of theory and applications, Journal of Applied Psychology 78(1) (1993), 98-104.
[80] M. Icen, A study of reliability and validity for citizenship knowledge and skill scale, International Journal of Assessment Tools in Education 7(4) (2020), 715-734. doi: 10.21449/ijate.747745.
[81] L. L. Chan and N. Idris, Validity and reliability of the instrument using exploratory factor analysis and Cronbach’s alpha, Int. J. Acad. Res. Bus. Soc. Sci. 7(10) (2017), 400-410. doi: 10.6007/ijarbss/v7-i10/3387.
[82] V. Swami, D. Barron, L. Weis, M. Voracek, S. Stieger and A. Furnham, An examination of the factorial and convergent validity of four measures of conspiracist ideation, with recommendations for researchers, PLoS One 12(2) (2017), 1-27. doi: 10.1371/journal.pone.0172617.
[83] M. R. Ab Hamid, W. Sami and M. H. Mohmad Sidek, Discriminant validity assessment: use of Fornell and Larcker criterion versus HTMT criterion, Journal of Physics: Conference Series, Institute of Physics Publishing 890(1) (2017), 1-6. doi: 10.1088/1742-6596/890/1/012163.
[84] C. K. Enders and D. L. Bandalos, The relative performance of full information maximum likelihood estimation for missing data in structural equation models, Educational Psychology Papers and Publications 8(3) (2001), 430-457. [Online]. Available: https://digitalcommons.unl.edu/edpsychpapers.
[85] C. K. Enders, Applying the Bollen-Stine bootstrap for goodness-of-fit measures to structural equation models with missing data, Multivariate Behav. Res. 37(3) (2002), 359-377. doi: 10.1207/S15327906MBR3703_3.
[86] J. L. Schafer and J. W. Graham, Missing data: our view of the state of the art, Psychol. Methods 7(2) (2002), 147-177. doi: 10.1037/1082- 989X.7.2.147.
[87] T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago and O. Tabona, A survey on missing data in machine learning, J. Big Data 8(1) (2021), 1-37. doi: 10.1186/s40537-021-00516-9.
[88] M. E. Civelek, Essentials of Structural Equation Modeling, Zea Books, 2018. doi: 10.13014/k2sj1hr5.
[89] N. Teistler, Validity evidence for the attitudes on person-centered behavior toward students questionnaire (APBS): internal structure and associations with external variables, Social Psychology of Education 27(5) (2024), 2637-2676. doi: 10.1007/s11218-024-09895-2.
[90] V. Heilala, M. Saarela, P. Jaaskela and T. Karkkainen, Course satisfaction in engineering education through the lens of student agency analytics, Proceedings-Frontiers in Education Conference, FIE, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 1-9. doi: 10.1109/FIE44824.2020.9274141.
[91] J. X. Pan and K. T. Fang, Maximum likelihood estimation, Growth Curve Models and Statistical Diagnostics, New York, NY, USA, Springer Series in Statistics, 2002, 77-158.
[92] H. Sujati, Sajidan, M. Akhyar and Gunarhadi, Testing the construct validity and reliability of curiosity scale using confirmatory factor analysis, Journal of Educational and Social Research 10(4) (2020), 229-237.
doi: 10.36941/JESR-2020-0080.
[93] J. Henseler, C. M. Ringle and M. Sarstedt, A new criterion for assessing discriminant validity in variance-based structural equation modeling, J. Acad. Mark Sci. 43(1) (2015), 115-135. doi: 10.1007/s11747-014-0403-8.
[94] Y. Haji-Othman and M. S. S. Yusuff, Assessing reliability and validity of attitude construct using partial least squares structural equation modeling (PLS-SEM), International Journal of Academic Research in Business and Social Sciences 12(5) (2022), 1-17. https://doi.org/10.6007/ijarbss/v12-i5/13289.
[95] A. Harerimana and N. G. Mtshali, Using exploratory and confirmatory factor analysis to understand the role of technology in nursing education, Nurse Educ. Today 92 (2020, 1-9. doi: 10.1016/j.nedt.2020.104490.
[96] H. Baharum et al., Validating an instrument for measuring newly graduated nurses’ adaptation, Int. J. Environ Res. Public Health 20(4) (2023), 1-16. doi: 10.3390/ijerph20042860.
[97] B. Mustapha and Y. Bolaji, Measuring lecturers commitment scales: a second order confirmatory factor analysis (CFA), International Journal of Education and Research 3(3) (2015), 1-12. [Online]. Available: www.ijern.com.
[98] D. Cheung, Evidence of a single second-order factor in student ratings of teaching effectiveness, Structural Equation Modeling 7(3) (2000), 442-460. doi: 10.1207/S15328007SEM0703_5.
[99] M. Vieira, A. K. S. Carvalho, L. L. Klein and B. A. D. Pereira, Student self- assessment of the graduate course: a multidimensional model proposal, Ensaio 32(122) (2024), 1-29. doi: 10.1590/S0104-40362023003204118.
[100] L. T. Hu and P. M. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives, Structural Equation Modeling 6(1) (1999), 1-55. doi: 10.1080/10705519909540118.
[101] B. Xiong, M. Skitmore and B. Xia, A Critical Review of Structural Equation Modeling Applications in Construction Research, Elsevier, 2015, pp. 1-34. doi: 10.1016/j.autcon.2014.09.006.
[102] R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling, Lawrence Erlbaum Associates Publishers, 2004.
[103] T. A. Brown and M. T. Moore, Confirmatory factor analysis, Handbook of Structural Equation Modeling, 2012.
[Online]. Available: https://www.researchgate.net/publication/251573889.
[104] K. Schermelleh-Engel, H. Moosbrugger and H. Müller, Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures, Methods of Psychological Research Online 8(2) (2003), 23-74.
[105] T. A. Brown, Confirmatory Factor Analysis for Applied Research, Guilford Press, 2015. doi: 10.1198/tas.2008.s98.
[106] W. Chan, Comparing indirect effects in SEM: a sequential model fitting method using covariance-equivalent specifications, Structural Equation Modeling 14(2) (2007), 326-346. doi: 10.1080/10705510709336749.
[107] T. Loeys, B. Moerkerke and S. Vansteelandt, A cautionary note on the power of the test for the indirect effect in mediation analysis, Front Psychol. 5 (2014), 1-8. doi: 10.3389/fpsyg.2014.01549.
[108] S. G. Pretorius, The implications of teacher effectiveness requirements for initial teacher education reform, Journal of Social Sciences 8(3) (2012), 310-317.
[109] D. Gijbels, F. Dochy, P. Van Den Bossche and M. Segers, Effects of problem-based learning: a meta-analysis from the angle of assessment, Review of Educational Research 75(1) (2005), 27-61.
[110] G. W. T. C. Kandamby, Effectiveness of laboratory practical for Students’ Learning, International Journal for Innovation Education and Research 7(3) (2019), 1-16.
[111] R. B. Marks, Determinants of student evaluations of global measures of instructor and course value, Journal of Marketing Education 22(2) (2000), 108-119.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Pushpa Publishing House, Prayagraj, India

This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 