• Agudo-Peregrina, Á. F., Hernández-García, Á. & Pascual-Miguel, F. J. (2013). Behavioural intention, use behaviour and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behaviour, 34, 301-314. doi:10.1016/j.chb.2013.10.035
• Al-Adwan, Am., Al-Adwan, Ah. & Smedley, J. (2013). Exploring students’ acceptance of e-learning using Technology Acceptance, Model in Jordanian universities. International Journal of Education and Development using Information and Communication Technology, (IJEDICT), 9(2), 4-18.
• Adams, W.C., Lind Infeld, D. & Wulff, C. M. (2013). Statistical Software for Curriculum and Careers. Journal of Public Affairs Education, 19(1), 173–188.
• Anderson, J. C. & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-423.
• Antonius, N., Xu, J. & Gao, X. (2015). Factors influencing the adoption of Enterprise Social Software in Australia. Knowledge-Based Systems, 73, 32–43. doi:10.1016/j.knosys.2014.09.003
• Baloğlu, M. (2003). Individual differences in statistics anxiety among college students. Personality and Individual Differences, 34(5), 855–865. doi: 10.1016/S0191-8869(02)00076-4
• Bastürk, R. (2005). The effectiveness of computer-assisted instruction in teaching introductory statistics. Journal of Educational Technology & Society, 8, 170-178.
• Beaujean, A. A. (2014). Latent Variable Modeling Using R. A Step-by-Step Guide. New York, USA: Taylor & Francis.
• Bisoux, T. (2002). The e-learning equation. BizEd, Jul/Aug2002, 40–45.
• Brezavšček, A., Šparl, P. & Žnidaršič, A. (2014). Extended Technology Acceptance Model for SPSS Acceptance among Slovenian Students of Social Sciences. Organizacija, 47(2), 116-127. doi: 10.2478/orga-2014-0009
• Budé, L., Imbos, T., v. d. Wiel, M. W. J., Broers, N. J. & Berger, M. P. F. (2009). The effect of directive tutor guidance in problem-based learning of statistics on students’ perceptions and achievement. Higher Education, 57(1), 23-36. doi: 10.1007/s10734-008-9130-8
• Chatzisarantis, N. L. D. & Williams, C. (2006). The Impact of Schmidt’s teaching Method on Motivation to Study Statistics. University of Exeter, School of Sport & Health Sciences. Research project supported by Higher Education Academy Network for Hospitality, Leisure, Sport and Tourism. Retrieved 29.08.14, from http://wwwnew1.heacademy.ac.uk/assets/hlst/documents/projects/round_5/r5_chatzisarantis_report.pdf.
• Cheung, R. & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. doi: 10.1016/j.compedu.2012.12.003
• Chuttur, M. Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Working Papers on Information Systems, 9(37), 9-37.
• Cleary, T. J. (2006), ―The Development and Validation of The Self-Regulation Strategy Inventory-selfreport. Journal of School Psychology, 44, 307-322.
• Cruise, R.J., Cash, R. W. & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety. Paper presented at the annual meeting of the Statistical Education Section. Proceedings of the American Statistical Association.
• Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results (Doctoral dissertation). Massachusetts Institute of Technology, Sloan School of Management.
• Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-34. doi: 10.2307/249008
• DeVaney, T. A. (2010). Anxiety and Attitude of Graduate Students in On-Campus vs. Online Statistics Courses. Journal of Statistics Education, 18(1), 1-15.
• Dunn, D. S. (Ed.), Smith, R. A. (Ed.) & Beins, B. C. (Ed.). (2007). Best Practices in Teaching Statistics and Research Methods in the Behavioural Sciences. Mahwah, NJ: Lawrence Erlbaum Associates.
• Ertuğ, Z. K., Girginer, N. & Yilmaz, V. (2014). Student attitudes towards statistical software usage: a structural equation modelling. Pakistan Journal of Statistics, 30(6), 1161-1167.
• Escalera-Chávez, M. E., García-Santillán, A. & Venegas-Martínez, F. (2014). Modeling Attitude toward Statistics by a Structural Equation. Eurasia Journal of mathematics, Science & Technology Education, 10(1), 23-31. doi: 0.12973/eurasia.2014.1019a.
• Escobar-Rodriguez, T. & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers & Education, 58, 1085–1093. doi: 10.1016/j.compedu.2011.11.012
• Everson, M., Gundlach, E. & Miller, J. (2013). Social media and the introductory statistics course. Computers in Human Behavior, 29, A69–A81. doi:10.1016/j.chb.2012.12.033
• Ferligoj A. (2015). How to Improve Statistical Literacy? Metodološki zvezki, 12 (1), 1-10.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. London: SAGE.
• Fornell, C. & Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Errors. Journal of Marketing Research, 18 (1), 39-5. doi: 10.2307/3151312
• Froiland, J. M., Oros, E., Smith, L. & Hirchert, T. (2012). Intrinsic Motivation to Learn: The Nexus between Psychological Health and Academic Success. Contemporary School Psychology, 16, 91- 100.
• Garfield, J. (1995). How students learn statistics. International Statistical Review, 63(1), 25-34.
• Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372-396. doi: 10.1111/j.1751-5823.2007.00029.x
• Garfield, J. & Everson, M. (2009). Preparing Teachers of Statistics: A Graduate Course for Future Teachers. Journal of Statistics Education, 17(2), 1-16.
• Garcia-Santillan, A., Moreno-Garcia, E., Carlos-Castro, J., Zamudio-Abdala, J., & Garduno-Trejo, J. (2012). Cognitive, Affective and Behavioural Components That Explain Attitude toward Statistics. Journal of Mathematics Research, 4(5), 8-16. doi:10.5539/jmr.v4n5p8
• García-Santillán, A., Venegas-Martínez, F., Escalera-Chávez, M. & Córdova-Rangel, A. (2013). Attitude towards statistics in engineering college: An empirical study in public university (UPA). Journal of Statistical and Econometric Methods, 2(1), 43-60.
• García-Santillán, A., Venegas-Martínez, F. & EscaleraChávez, M. (2013a). An exploratory factorial analysis to measure attitude toward statistics: Empirical study in undergraduate students. International Journal of Research and Reviews in Applied Sciences, 14(2), 356-366.
• Gould, R. (2010). Statistics and the Modern Student. International Statistical Review, 78(2), 297–315. doi: 10.1111/j.1751-5823.2010.00117.x
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006). Multivariate Data Analysis (6th Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
• Hernández, B. Jiménez, J. & Martín, M. J. (2008). Extending the technology acceptance model to include the IT decision-maker: A study of business management software. Technovation, 28(3), 112–121. doi:10.1016/j.technovation.2007.11.002
• Hall, S., Vance, E. A. & Tech, V. (2010). Improving Self-efficacy in Statistics: Role of Self-explanation & Feedback. Journal of Statistics Education, 18(3), 1-22.
• Hilton, S. C., Schau, C. & Olsen, J. A. (2004). Survey of Attitudes Toward Statistics: Factor structure invariance by gender and by administration time. Structural Equation Modeling, 11(1), 92-109. doi: 10.1207/S15328007SEM1101_7
• Hsu, M. K., Wang S. W. & Chiu K. K. (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behavior, 25, 412–42. doi: 10.1016/j.chb.2008.10.003
• Hulsizer, M. R. & Woolf, L.M. (2009). A Guide to Teaching Statistics: Innovations and Best Practices. Suithern gate: Wiley-Blackwell.
• Jatnika, R. (2015). The Effect of SPSS Course to Students Attitudes toward Statistics and Achievement in Statistics. International Journal of Information and Education Technology, 5(11), 818-821. doi: 10.7763/IJIET.2015.V5.618
• Judi, H. M., Ashaari, N. S., Mohamed, H., & Tengku Wook, T. M. (2011). Students profile based on attitude towards statistics. In Procedia - Social and Behavioral Sciences, 18, 266-272. doi: 10.1016/j.sbspro.2011.05.038
• Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd Ed.). New York: The Guliford Press.
• Koufteros, X. (1999). Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. Journal of Operations Management, 17, 467–488. doi: 10.1016/S0272- 6963(99)00002-9
• Larwin, K. & Larwin, D. (2011). A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education40 Years of Research. Journal of Research on Technology in Education, 43(3), 253-278. doi:10.1080/15391523.2011.10782572
• Lavasani, M. G., Weisani, M. & Shariati, F. (2014). The Role of Achievement Goals, Academic Motivation in Statistics Anxiety: Testing a Causal Model. Procedia - Social and Behavioral Sciences, 114, 933–938. doi:10.1016/j.sbspro.2013.12.810
• Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. doi:10.1016/j.elerap.2008.11.006
• Lee, D. Y. & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. doi: 10.1016/j.compedu.2012.10.001
• Legris, P., Inghamb, J. & Collerettec, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191–204. doi: 10.1016/S0378-7206(01)00143-4
• Li, Y., Qi, J. & Shu. H., (2007), A Review on the Relationship Between New Variables and Classical TAM Structure. In IFTP International Federation for Information Processing, Volume 254. Research and Practical Issues of Enterprise Information Systems, 11(I), eds. L. Xu, Tjoa A. Chaudhry S. Boston: Springer, 53-63.
• Li, Y., Tan, C. H., Xu, H. & Teo, H. H. (2011). Open source software adoption: motivations of adopters and amotivations of non-adopters, ACM SIGMIS Database archive, 42(2), 76-94. doi: 10.1145/1989098.1989103
• Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D. & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54 (2), 600-61.
• MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods, 1(2), 130-149.
• Murtonen, M., Olkinuora, E., Tynjälä, P. & Lehtinen, E. (2008). “Do I need research skills in working life?”University students’ motivation and difficulties in quantitative methods courses. Higher Education, 56(5), 599-612. doi: 10.1007/s10734-008-9113-9
• Nolan, V. T. & Swart, A. J. (2015). Undergraduate Student Perceptions Regarding the Use of Educational Technology – A Case Study in a Statistics Service Course. Eurasia Journal of Mathematics, Science & Technology Education, 11(4), 817-825. doi: 10.12973/eurasia.2015.1441a.
• Onwuegbuzie, A. J. (2004). Academic procrastination and statistics anxiety. Assessment & Evaluation in Higher Education, 29(1), 3-19. doi: 10.1080/0260293042000160384
• Onwuegbuzie, A. J. & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments – A comprehensive review of the literature. Teaching in Higher Education, 8(2), 195-209. doi: 10.1080/1356251032000052447
• Padilla-Meléndez, A., Del Aguila-Obra, A. R. & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317. doi: 10.1016/j.compedu.2012.12.014
• Pan, W. & Tang, M. (2004). Examining the Effectiveness of Innovative Instructional Methods on Reducing Statistics Anxiety for Graduate Students in the Social Sciences. Journal of Instructional Psychology, 31(2), 149-159.
• Perepiczka, M., Chandler, N. & Becerra, M. (2011). Relationship between Graduate Students’ Statistics Self-Efficacy, Statistics Anxiety, Attitude toward Statistics, and Social Support. The Professional Counselor, 1(2), 99–108. doi:10.15241/mpa.1.2.99
• Persico, D, Manca, S. & Pozzi, F. (2014). Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614-622. doi: 10.1016/j.chb.2013.07.045
• Ramos, M. & Carvalho, H. (2011). Perceptions of quantitative methods in higher education: mapping student profiles. Higher Education, 61(6), 629-647. doi: 10.1007/s10734-010-9353-3
• Reeinna, N.A. (2014). Student’s Attitude towards Introductory Statistics Course at Public Universities using Partial Least Square Analysis. Interdisciplinary Journal of Contemporary Research in Business, 6(4), 94-123.
• Rosseel, Y. (2015). The lavaan tutorial. Retrieved from: http://lavaan.ugent.be/tutorial/tutorial.pdf
• Rosseel, Y. (2012). Lavaan: An R Package for Structural Equation Modelling. Journal of Statistical Software. 48, 1–36. doi:10.18637/jss.v048.i02
• Shah, S. A. M., Iqbal, N., Janjua, S. Y. & Amjad, S. (2013). Employee Behavior Towards Adoption of Elearning Courses: Validating Technology Acceptance Model. Mediterranean Journal of Social Sciences, 4(14), 765-773. doi: 10.5901/mjss.2013.v4n14p765
• Schumacker, R. E. & Lomax, R. G. (2010). A Beginner's Guide to Structural Equation Modeling, (3rd Ed.). New York: Routledge.
• Sesé, A., Jiménez, R., Montaño, J. J. & Palmer, A. (2015). Can Attitudes Toward Statistics and Statistics Anxiety Explain Students’ Performance? Revista de Psicodidáctica, 20(2), 285-304. doi: 10.1387/RevPsicodidact.13080
• Sharma, S. K. & Chandel, J. K. (2013). Technology Acceptance Model for the Use of Learning through Websites among Students in Oman. The International Arab Journal of Information Technology, 3(1), 44-49.
• Shroff, R. H., Deneen, C. C. & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students' behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600-618.
• Siddiqui, K. (2013). Heuristics for Sample Size Determination in Multivariate Statistical Techniques. World Applied Sciences Journal, 27(2), 285-287.
• Sriwidharmanely, & Syafrudin, V. (2012). An Empirical Study of Accounting Software Acceptance among Bengkulu City Students. Asian Journal of Accounting and Governance, 3, 99-112.
• Stickels, J. W. & Dobbs, R. R. (2007). Helping Alleviate Statistical Anxiety with Computer Aided Statistical Classes. The Journal of Scholarship of Teaching and Learning, 7(1), 1-15.
• Tabak, F. & Nguyen, N. T. (2013). Technology Acceptance and Performance in Online Learning Environments: Impact of Self-Regulation. MERLOT Journal of Online Learning and Teaching, 9(1), 1-11.
• Tempelaar, D. T., Schim van der Loeff, S. & Gijselaers, W.H. (2007). A structural equation model analyzing the relationship of students’ attitudes toward statistics, prior reasoning abilities and course performance. Statistics Education Research Journal, 6(2), 78-102.
• Teo, T. K., Luan, W. S. & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: an application of the Technology Acceptance Model (TAM). Educational Technology and Society, 11(4), 265-280.
• Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52, 302–312. doi: 10.1016/j.compedu.2008.08.006
• Teo, T. (2010). An empirical study to validate the Technology Acceptance Model (TAM) in explaining the intention to use technology among educational users. International Journal of Information and Communication Technology Education, 6(4), 1-12. doi: 10.4018/jicte.2010100101
• Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57, 2432–244. doi: 10.1016/j.compedu.2011.06.008
• Teo, T. (2011a). Technology Acceptance in Education, Research and Issues, Rotterdam: Sense Publishers.
• Teo, T. & Zhou, (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124-142. doi: 10.1007/s12528-014-9080-3
• Tuan, H. L., Chin C. C. & Shieh S. H. (2005). The development of a questionnaire to measure students' motivation towards science learning. International Journal of Science Education, 27(6), 639–654. doi: 10.1080/0950069042000323737
• Turner, M., Kitchenham, B., Brereton, P., Charters, S. & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52, 463-479. doi: 10.1016/j.infsof.2009.11.005
• Un Jan, A. & Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27, 845-851. doi: 10.1016/j.chb.2010.11.009
• Wong, K. T, Osman, R. B. T., Goh, P. S. C. & Rahmat, M. K. (2013). Understanding Student Teachers' Behavioural Intention to Use Technology: Technology Acceptance Model (TAM) Validation and Testing. International Journal of Instruction, 6(1), 89-104.
• Yousafzai, S. Y., Foxall, G. R. & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251 – 28.
• Zapletal, D. & Pacakova, V. (2013). Statistical Software Packages as an Innovative and Motivational Tool for Teaching Statistics. In C. A. Long et al. (Eds.), Recent Advances in Education and Educational Technologies, Proceedings of the 2013 International Conference on Education and Educational Technologies (EET 2013), Rhodes Island, Greece, July 16-19, 2013. 120-122. Retrieved from http://www.inase.org/library/2013/rhodes/EET.pdf
• Zeidner, M. (1991). Statistics and mathematics anxiety in social science students: Some interesting parallels. British Journal of Educational Psychology, 61(3), 319–328.

APA

Brezavšček, A., Šparl, P., & Žnidaršič, A. (2017). Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences. Eurasia Journal of Mathematics, Science and Technology Education, 13(3), 953-986. https://doi.org/10.12973/eurasia.2017.00652a

Vancouver

Brezavšček A, Šparl P, Žnidaršič A. Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences. EURASIA J Math Sci Tech Ed. 2017;13(3):953-86. https://doi.org/10.12973/eurasia.2017.00652a

AMA

Brezavšček A, Šparl P, Žnidaršič A. Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences. EURASIA J Math Sci Tech Ed. 2017;13(3), 953-986. https://doi.org/10.12973/eurasia.2017.00652a

Chicago

Brezavšček, Alenka, Petra Šparl, and Anja Žnidaršič. "Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences". Eurasia Journal of Mathematics, Science and Technology Education 2017 13 no. 3 (2017): 953-986. https://doi.org/10.12973/eurasia.2017.00652a

Harvard

Brezavšček, A., Šparl, P., and Žnidaršič, A. (2017). Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences. Eurasia Journal of Mathematics, Science and Technology Education, 13(3), pp. 953-986. https://doi.org/10.12973/eurasia.2017.00652a

MLA

Brezavšček, Alenka et al. "Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences". Eurasia Journal of Mathematics, Science and Technology Education, vol. 13, no. 3, 2017, pp. 953-986. https://doi.org/10.12973/eurasia.2017.00652a