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The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study

Year 2024, Volume: 11 Issue: 2, 303 - 319, 20.06.2024
https://doi.org/10.21449/ijate.1369023

Abstract

This study aims to generalize the reliability of the GAAIS, which is known to perform valid and reliable measurements, is frequently used in the literature, aims to measure one of today's popular topics, and is one of the first examples developed in the field. Within the meta-analytic reliability generalization study, moderator analyses were also conducted on some categorical and continuous variables. Cronbach's α values for the overall scale and the positive and negative subscales, and McDonald's ω coefficients for positive and negative subscales were generalized. Google Scholar, WOS, Taylor & Francis, Science Direct, and EBSCO databases were searched to obtain primary studies. As a result of the screening, 132 studies were found, and these studies were reviewed according to the inclusion criteria. Reliability coefficients obtained from 19 studies that met the criteria were included in the meta-analysis. While meta-analytic reliability generalization was performed according to the random effects model, moderator analyses were performed according to the mixed effect model based on both categorical variables and continuous variables. As a result of the research pooled, Cronbach's α was 0.881, 0.828, and 0.863 for total, the negative, and positive subscales respectively. Also, McDonald's ω was 0.873 and 0.923 for negative and positive subscales respectively. It was found that there were no significant differences between the reliability coefficients for all categorical variables. On the other hand, all continuous moderator variables (mean age, standard deviation age, and rate of female) had a significant effect.

References

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  • Beretvas, S.N., Suizzo, M.A., Durham, J.A., & Yarnell, L.M. (2008). A reliability generalization study of scores on Rotter's and Nowicki-Strickland's locus of control scales. Educational and Psychological Measurement, 68(1), 97 119. https://doi.org/10.1177/0013164407301529
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  • *Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: Development of the Attitudes Toward AI in Defense (AAID) Scale. Frontiers in Psychology, 14, 1164810. https://doi.org/ 10.3389/fpsyg.2023.1164810
  • Hedges, L.V., & Pigott, T.D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), 426 445. https://doi.org/10.1037/1082 989x.9.4.426
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  • Henson, R.K., & Thompson, B. (2002). Characterizing measurement error in scores across studies: Some recommendations for conducting “reliability generalization” studies. Measurement and Evaluation in Counseling and Development, 35(2), 113-127. https://doi.org/10.1080/07481756.2002.12069054
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The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study

Year 2024, Volume: 11 Issue: 2, 303 - 319, 20.06.2024
https://doi.org/10.21449/ijate.1369023

Abstract

This study aims to generalize the reliability of the GAAIS, which is known to perform valid and reliable measurements, is frequently used in the literature, aims to measure one of today's popular topics, and is one of the first examples developed in the field. Within the meta-analytic reliability generalization study, moderator analyses were also conducted on some categorical and continuous variables. Cronbach's α values for the overall scale and the positive and negative subscales, and McDonald's ω coefficients for positive and negative subscales were generalized. Google Scholar, WOS, Taylor & Francis, Science Direct, and EBSCO databases were searched to obtain primary studies. As a result of the screening, 132 studies were found, and these studies were reviewed according to the inclusion criteria. Reliability coefficients obtained from 19 studies that met the criteria were included in the meta-analysis. While meta-analytic reliability generalization was performed according to the random effects model, moderator analyses were performed according to the mixed effect model based on both categorical variables and continuous variables. As a result of the research pooled, Cronbach's α was 0.881, 0.828, and 0.863 for total, the negative, and positive subscales respectively. Also, McDonald's ω was 0.873 and 0.923 for negative and positive subscales respectively. It was found that there were no significant differences between the reliability coefficients for all categorical variables. On the other hand, all continuous moderator variables (mean age, standard deviation age, and rate of female) had a significant effect.

References

  • Alcocer‐Bruno, C., Ferrer‐Cascales, R., Rubio‐Aparicio, M., & Ruiz‐Robledillo, N. (2020). The medical outcome study‐HIV health survey: A systematic review and reliability generalization meta‐analysis. Research in Nursing & Health, 43(6), 610-620. https://doi.org/10.1002/nur.22070
  • Arslan, K. (2020). Eğitimde yapay zekâ ve uygulamaları [Artificial intelligence and applications in education]. The Western Anatolia Journal of Educational Sciences, 11(1), 71-88. https://dergipark.org.tr/tr/pub/baebd/issue/55426/690058
  • Aslan, Ö.S., Gocen, S., & Sen, S. (2022). Reliability generalization meta-analysis of mathematics anxiety scale for primary school students. Journal of Measurement and Evaluation in Education and Psychology, 13(2), 117 133. https://doi.org/10.21031/epod.1119308
  • Begg, C.B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088. https://doi.org/10.2307/2533446
  • *Bellaiche, L., Shahi, R., Turpin, M.H., Ragnhildstveit, A., Sprockett, S., Barr, N., ... & Seli, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8(1), 1-22. https://doi.org/10.1186/s41235-023-00499-6
  • Beretvas, S.N., Meyers, J.L., & Leite, W.L. (2002). A reliability generalization study of the Marlowe Crowne Social Desirability Scale. Educational and Psychological Measurement, 62(4), 570-589. https://doi.org/10.1177/0013164402062004003
  • Beretvas, S.N., Suizzo, M.A., Durham, J.A., & Yarnell, L.M. (2008). A reliability generalization study of scores on Rotter's and Nowicki-Strickland's locus of control scales. Educational and Psychological Measurement, 68(1), 97 119. https://doi.org/10.1177/0013164407301529
  • *Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Mantere, E.S., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. https://doi.org/10.1016/j.tele.2023.102013
  • Borenstein, M., Hedges, L.V., Higgins, J.P., & Rothstein, H.R. (2009). Introduction to meta-analysis. John Wiley & Sons.
  • Breazeal, C. (2004). Designing sociable robots. MIT.
  • Card, N. (2012). Applied meta-analysis for social science research. Guilford.
  • *Carolus, A., Koch, M., Straka, S., Latoschik, M.E., & Wienrich, C. (2023). MAILS-Meta AI Literacy Scale: Development and testing of an AI Literacy Questionnaire based on well-founded competency models and psychological change-and meta-competencies. arXiv preprint. https://doi.org/10.48550/arXiv.2302.09319
  • Caruso, J.C., & Edwards, S. (2001). Reliability generalization of the Junior Eysenck Personality Questionnaire. Personality and Individual Differences, 31, 173 184. https://doi.org/10.1016/S0191-8869(00)00126-4
  • Cochran, W.G. (1954). The combination of estimates from different experiments. Biometrics, 10, 101–129. https://doi.org/10.2307/3001666
  • *Cruz, J.P., Sembekova, A., Omirzakova, D., Bolla, S.R., & Balay-odao, E.M. (2023). General attitudes towards and readiness for medical artificial intelligence among medical and health sciences students in Kazakhstan. https://doi.org/10.2196/preprints.49536.
  • *Darda, K., Carre, M., & Cross, E. (2023). Value attributed to text-based archives generated by artificial intelligence. Royal Society Open Science, 10: 220915. https://doi.org/10.1098/rsos.220915
  • DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled clinical trials, 7(3), 177 188. https://www.biostat.jhsph.edu/~fdominic/teaching/bio656/references/sdarticle.pdf
  • Eser, M.T., & Dogan, N. (2023). Life Satisfaction Scale: A meta-analytic reliability generalization study in Turkey sample. Turkish Psychological Counseling and Guidance Journal, 13(69), 224-239. https://doi.org/10.17066/tpdrd.1223320mn
  • *Gabbiadini, A., Dimitri, O., Cristina, B., & Anna, M. (2023). Does ChatGPT pose a threat to human identity. SSRN, 4377900. https://doi.org/10.2139/ssrn.4377900
  • *Gozzo, M., Woldendorp, M.K., & De Rooij, A. (2021, December). Creative collaboration with the “brain” of a search engine: Effects on cognitive stimulation and evaluation apprehension. In International Conference on ArtsIT, Interactivity and Game Creation (pp. 209-223). Springer International Publishing.
  • 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: 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
  • *Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: Development of the Attitudes Toward AI in Defense (AAID) Scale. Frontiers in Psychology, 14, 1164810. https://doi.org/ 10.3389/fpsyg.2023.1164810
  • Hedges, L.V., & Pigott, T.D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), 426 445. https://doi.org/10.1037/1082 989x.9.4.426
  • *Heim, S., & Chan-Olmsted, S. (2023). Consumer trust in AI–human news collaborative continuum: preferences and influencing factors by news production phases. Journalism and Media, 4(3), 946-965. https://doi.org/10.3390/journalmedia4030061
  • Henson, R.K., & Thompson, B. (2002). Characterizing measurement error in scores across studies: Some recommendations for conducting “reliability generalization” studies. Measurement and Evaluation in Counseling and Development, 35(2), 113-127. https://doi.org/10.1080/07481756.2002.12069054
  • Hess, T.J., McNab, A.L., & Basoglu, K.S. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS Quarterly, 38, 1-28. https://doi.org/10.25300/MISQ/2014/38.1.01
  • Higgins, J.P.T., & Thompson, S.G. (2002), Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539-1558. https://doi.org/10.1002/sim.1186
  • Hopcan, S., Turkmen, G., & Polat, E. (2023). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 1-21. https://doi.org/10.1007/s10639-023-12086-9
  • Huang, S.P. (2018). Effects of using artificial intelligence teaching system for environmental education on environmental knowledge and attitude. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 3277 3284. https://doi.org/10.29333/ejmste/91248
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There are 70 citations in total.

Details

Primary Language English
Subjects Measurement Theories and Applications in Education and Psychology
Journal Section Articles
Authors

Melek Gülşah Şahin 0000-0001-5139-9777

Yıldız Yıldırım 0000-0001-8434-5062

Early Pub Date May 22, 2024
Publication Date June 20, 2024
Submission Date September 30, 2023
Published in Issue Year 2024 Volume: 11 Issue: 2

Cite

APA 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

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