Abstract
The development of attitudes toward science instruments has recently emerged in science education research. However, a comprehensive review of their psychometric properties, using currently accepted assessment standards, has not yet been completed. Consequently, this review discusses the validity and reliability of 18 measures published between 2005 and 2019 in leading science education journals. Findings showed that construct validity and internal consistency reliability was reported for all instruments; however, evidence for predictive validity and temporal stability reliability was rather scarce, which could limit their use in intervention and correlational type of studies. Similarly, content validity was found to be underreported. Consequently, the relevance, comprehensiveness, and comprehensibility of the items in some instruments are currently unknown and yet to be established in future studies. Finally, there is a gap in the literature regarding instruments that can be used across different countries and scientific disciplines, which could restrict accumulative and comparative results worldwide. Since the use of valid and reliable measurement instruments is a crucial aspect of educational research, the findings of this study could be useful in assisting researchers and practitioners in selecting the most appropriate measure for different research designs.
Similar content being viewed by others
Data Availability
All data used for this study are reported in the manuscript and parent supplementary material.
Notes
It should be noted that Blalock et al. (2008) used a different taxonomy to refer to these psychometric properties. Specifically, “construct validity” was named “factor analysis validity,” “discriminative validity” was referred to as “contrasting groups validity,” and “temporal stability reliability” was mentioned as “test-retest reliability.”
References
AERA, APA, & NCME. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.
Aikenhead, G. S., & Ryan, A. G. (1992). The development of a new instrument: ‘views on science-technology-society’ (VOSTS). Science Education, 76(5), 477–491.
Aydeniz, M., & Kotowski, M. R. (2014). Conceptual and methodological issues in the measurement of attitudes toward science. Electronic Journal of Science Education, 18(3), 1–24.
Besley, J. C. (2016). The National Science Foundation’s science and technology survey and support for science funding, 2006–2014. Public Understanding of Science, 27, 94–109.
Blalock, C. L., Lichtenstein, M. J., Owen, S., Pruski, L., Marshall, C., & Toepperwein, M. A. (2008). In pursuit of validity: a comprehensive review of science attitude instruments 1935-2005. International Journal of Science Education, 30(7), 961–977. https://doi.org/10.1080/09500690701344578.
Boone, W. J., Townsend, J. S., & Staver, J. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: an exemplar utilizing STEBI self-efficacy data. Science Education, 95(2), 258–280. https://doi.org/10.1002/sce.20413.
Bybee, R., & McCrae, B. (2011). Scientific literacy and student attitudes: perspectives from PISA 2006 science. International Journal of Science Education, 33(1), 7–26. https://doi.org/10.1080/09500693.2010.518644.
DeVellis, R. F. (2017). Scale development. Theory and applications. Los Angeles: SAGE.
Eagly, A. H., & Chaiken, S. (1995). Attitude strength, attitude structure, and resistance to change. In R. E. Petty & J. A. Krosnich (Eds.), Ogio State University series on attitudes and persuasion, Attitude strength: antecedents and consequences (Vol. 4, pp. 413–432). New York: Lawrence Erlbaum Associates, Inc..
Gardner, P. L. (1975). Attitudes to science: a review. Studies in Science Education, 2, 1–41.
Khine, S. M. (2015). Attitude measurements in science education: classic and contemporary approaches. Charlotte: Information Age Publishing, INC..
Kline, P. (2014). An easy guide to factor analysis. New York: Routledge.
Klopfer, L. E. (1971). Evaluation of learning in science. In B. S. Bloom, J. T. Hastings, & G. F. Madaus (Eds.), Handbook of formative and summative evaluation of student learning (pp. 559–642). New York: McGraw-Hill.
Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada (Exploratory item factor analysis: a practical guide revised and updated). Anales de Psicología, 30(3), 1151–1169. https://doi.org/10.6018/analesps.30.3.199361.
Munby, H. (1997). Issues of validity in science attitude measurement. Journal of Research in Science Teaching, 34(4), 337–341.
Newell, A. D., Tharp, B. Z., Vogt, G. L., Moreno, N. P., & Zientek, L. R. (2015). Students’ attitudes toward science as predictors of gains on student content knowledge: benefits of an after-school program. School Science and Mathematics, 115(5), 216–225. https://doi.org/10.1111/ssm.12125.
Osborne, J., Simon, S., & Collins, S. (2003). Attitudes toward science: a review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079. https://doi.org/10.1080/0950069032000032199.
Petticrew, M., & Roberts, R. (2006). Systematic reviews in the social sciences: a practical guide. Oxford: Blackwell.
Potvin, P., & Hasni, A. (2014). Interest, motivation and attitudes toward science and technology at K-12 levels: a systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85–129. https://doi.org/10.1080/03057267.2014.881626.
Schibeci, R. A. (1983). Selecting appropriate attitudinal objectives for school science. Science Education, 67(5), 595–603. https://doi.org/10.1002/sce.3730670508.
Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29(4), 304–321. https://doi.org/10.1177/0734282911406653.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296. https://doi.org/10.1007/s11165-016-9602-2.
Trochim, W. M., & Donnelly, J. P. (2006). The research methods knowledge base. Cincinnati: Atomic Dog Publishing Inc..
Tytler, R., & Osborne, J. (2012). Student attitudes and aspirations towards science. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second International Handbook of Science Education (pp. 597-625). Berlin: Springer Science & Business Media.
Watkins, M. W. (2017). The reliability of multidimensional neuropsychological measures: from alpha to omega. The Clinical Neuropsychologist, 31(6–7), 1113–1126. https://doi.org/10.1080/13854046.2017.1317364.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical Approval
This study does not involve human participants.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
ESM 1
(PDF 157 kb).
Rights and permissions
About this article
Cite this article
Toma, R.B., Lederman, N.G. A Comprehensive Review of Instruments Measuring Attitudes Toward Science. Res Sci Educ 52, 567–582 (2022). https://doi.org/10.1007/s11165-020-09967-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11165-020-09967-1