Factorial Structure and Measurement Invariance of the General Atitudes Toward Artificial Intelligence Scale for University Students

Authors

  • Mohammed Mousa Al-Shumrani Department of Psychology, College of Education, Taif University, Al-Hawiyah, Taif 21944, Saudi Arabia.

DOI:

https://doi.org/10.48161/qaj.v5n2a1672

Abstract

The study aims to investigate the factorial structure (FS) and measurement invariance (MI) of the General Attitudes Toward Artificial Intelligence Scale (GAAIS) on a sample of students from Taif University. The instrument used was the GAAIS developed by [1] and translated by the researcher. The scale was administered to a sample of 461 university students. The study results indicated that the scale's structure aligned with the confirmatory factor analysis model, with fit indices demonstrating good fit: (χ^2⁄df=611.77⁄169=3.62), Good of fit Index (GFI) = 0.855, Tucker-Lewis index (TLI) = 0.837, and Root Mean square of Approximation (RMSEA) = 0.075. These results confirm the construct validity and the scale's suitability for the data. The composite reliability coefficients were 0.828 and 0.869, and Cronbach’s alpha reliability coefficients were 0.832 and 0.869 for the positive sub-dimensions (opportunities, benefits, societal and personal utility, and positive emotions) and the negative sub-dimensions (fears and negative emotions), respectively. Additionally, the study confirms configural, metric, scalar, and residual invariance across the male and female study groups. The results confirmed that the scale has high reliability and validity and is unbiased regarding the gender variable. The study also confirms the suitability of the instrument for measuring general attitudes toward artificial intelligence (AI) among university students.

Downloads

Download data is not yet available.

References

Schepman, A., & Rodway, P. (2020). Initial validation of the General Attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports. https://doi.org/10.1016/j.chbr.2020.100014

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Verma, M. (2018). Artificial intelligence: Its scope in different areas with special reference to the field of education. Online Submission, 3(1), 5–10.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007

Chai, C., King, R., & Zho, Y. (2024). Development and validation of the Artificial Intelligence Learning Intention Scale (AILIS) for university. SAGE Open, 14(2). https://doi.org/10.1177/21582440241242188

Cai, J., Xu, Z., Sun, X., Guo, X., & Fu, X. (2023). Validity and reliability of the Chinese version of Threats of Artificial Intelligence Scale (TAI) in Chinese adults. Psicologia: Reflexão e Crítica, 36(5). https://doi.org/10.1186/s41155-023-00247-1

Chen, F. F., Sousa, K. H., & West, S. G. (2005). Testing measurement invariance of second-order factor models. Structural Equation Modeling: A Multidisciplinary Journal, 12(3), 471–492. https://doi.org/10.1207/s15328007sem1203_7

Venkatesh, V., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Stein, J.-P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with personality. Scientific Reports, 14, 2909. https://doi.org/10.1038/s41598-024-53335-2

Le, M. T. (2024). Exploring undergraduate students’ general attitudes towards Artificial Intelligence: A perspective from Vietnam. Journal of Language and Cultural Education, 12(3). https://doi.org/10.2478/jolace-2024-0014

Petrič, G., & Atanasova, S. (2024). Validation of the extended e-health literacy scale: Structural validity, construct validity and measurement invariance. BMC Public Health, 24, 1991. https://doi.org/10.1186/s12889-024-19431-8

Protzko, J. (2022). Invariance: What does measurement invariance allow us to claim? Educational and Psychological Measurement. https://doi.org/10.1177/00131644241282982

Clawson, R. E., Bean, R. A., Dyer, W. J., Bradford, A. B., Anderson, S. R., & Lee, C.-T. P. (2023). Examining a key measure of youth disclosure to parents for measurement invariance across time and reporters. Journal of Child and Family Studies, 32, 1765–1775. https://doi.org/10.1007/s10826-022-02388-w

Kline, R. B. (2015). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002.

Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400

Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730

Wei, J. (2018). Research progress and application of computer artificial intelligence technology. MATEC Web of Conferences, 176, 01043. https://doi.org/10.1051/matecconf/201817601043

Kieslich, K., Lünich, M., & Marcinkowski, F. (2021). The Threats of Artificial Intelligence Scale (TAI): Development, measurement and test over three application domains. International Journal of Social Robotics, 13, 1563–1577. https://doi.org/10.1007/s12369-020-00734-w

Saklaki, A., & Gardikiotis, A. (2024). Exploring Greek students’ attitudes toward artificial intelligence: Relationships with AI ethics, media, and digital literacy. Societies, 14, 248. https://doi.org/10.3390/soc14120248

Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9(50). https://doi.org/10.1038/s41539-024-00264-4

Lee, J. (2018). Human attitudes toward artificial intelligence: Understanding the role of perceived risks and benefits. AI & Society, 33(1), 1–10. https://doi.org/10.1007/s00146-017-0730-8

Sun, P., Zhang, Y., & Wang, Y. (2020). The evolution of public attitudes toward AI technologies: A longitudinal study. Computers in Human Behavior, 112, 106446. https://doi.org/10.1016/j.chb.2020.106446

Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.

Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Press.

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

Ding, Z., Ng, F., & Wang, J. (2014). Testing trust scale measurement invariance in project teams. Journal of Engineering, Design and Technology, 12(2), 209–222. https://doi.org/10.1108/JEDT-04-2012-0017

Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58(4), 525–543. https://doi.org/10.1007/BF02294825

Doğruyol, S., Uzun, N. B., Aygar, B. B., & Yücedağlar, A. (2024). Examining measurement invariance of different SWLS measurement models according to gender. Journal on Educational Psychology, 17(3). https://doi.org/10.26634/jpsy.17.3.19823

Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29, 4785–4808. https://doi.org/10.1007/s10639-023-12015-w

Zaraa, N., & Al-Eidan, K. (2024). Measurement invariance in the Cattell intelligence test among adults based on gender using multigroup confirmatory factor analysis. Journal of Arts for Psychological and Educational Studies, 6(1), 9–45.

Şahin, M. G., & Yıldırım, Y. (2024). The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study. International Journal of Assessment Tools in Education, 11(2), 303–319. https://doi.org/10.21449/ijate.1369023

Al-Nuaimi, M., & Al-Emran, M. (2024). Development and validation of ICT unethical behavior scale among undergraduate students. Current Psychology, 43, 8760–8776. https://doi.org/10.1007/s12144-023-05038-6

Weyland, S., Kaushal, N., Fritsch, J., Strauch, U., & Jekauc, D. (2024). Validation and invariance testing of the English short physical activity enjoyment scale. PLOS ONE, 19(11), e0313626. https://doi.org/10.1371/journal.pone.0313626

Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Adaptation and psychometric properties of an Attitude toward Artificial Intelligence Scale (AIAS-4) among Peruvian nurses. Behavioral Sciences, 14(5), 437. https://doi.org/10.3390/bs14060437

Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14(5), 100–120. https://doi.org/10.3389/fpsyg.2023.1191628

Atia, A. (2020). Factor structure of the Barkley executive function disorder scale and its measurement invariance based on item response theory and multigroup confirmatory factor analysis among university students. Educational Sciences, 28(4), 327–438.

Al-Ajmi, M. A. (2020). Confirmatory factor analysis of the smartphone addiction scale among a sample of high school students in Kuwait. Scientific Journal of the Faculty of Education, Assiut University, 20(5), 90–123.

Shalabi, S. (2015). Factor structure and measurement invariance of the Raven’s progressive matrices test among middle and high school students based on the structural equation model. Educational Sciences, 23(4), 17–45.

Bollen, K. A. (1989). Structural equation with latent variables. John Wiley. https://doi.org/10.1002/9781118619179

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. Structural Equation Modeling, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2). https://doi.org/10.1177/0049124192021002005

Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications (2nd ed.). SPSS Inc.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

Published

2025-04-25

How to Cite

AlShamrani, M. M. (2025). Factorial Structure and Measurement Invariance of the General Atitudes Toward Artificial Intelligence Scale for University Students. Qubahan Academic Journal, 5(2), 34–48. https://doi.org/10.48161/qaj.v5n2a1672

Issue

Section

Articles