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Journals / International Journal of Assessment Tools in Education / 2021 / Cilt: 8 - Sayı: 3

Examining the Invariance of a Measurement Model of Teachers’ Awareness and Exposure Levels to Nanoscience by Using the Covariance Structure Approach

Şeref TAN
Zeki IPEK
Ali Derya ATİK
Figen ERKOÇ
Pages
487–508
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it was concluded that the NSTAS scale was not biased for teacher branches, school types or gender.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The main aim of this study is to examine the measurement invariance of the structural equating model constructed on the Awareness and Exposure subscales of Nanoscience and Nanotechnology Awareness Scale (NSTAS) test for three teacher branches, three school types, and two genders by using the covariance structural analysis to test configural and metric invariances.
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Key Findings & Data:The other aim of this study is showing how to use the IBM AMOS-24 software package with examples to address the issue of measurement invariance using the covariance structural analysis approach.
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Conclusion & Impact:As a result, it was concluded that the NSTAS scale was not biased for teacher branches, school types or gender.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:Configural invariance · Factorial structure invariance · Structural covariance invariance

Abstract

The main aim of this study is to examine the measurement invariance of the structural equating model constructed on the Awareness and Exposure subscales of Nanoscience and Nanotechnology Awareness Scale (NSTAS) test for three teacher branches, three school types, and two genders by using the covariance structural analysis to test configural and metric invariances. The other aim of this study is showing how to use the IBM AMOS-24 software package with examples to address the issue of measurement invariance using the covariance structural analysis approach. Study sample was 1039 complete records gathered from science teachers with convenience sampling. Research data were collected in two stages. In the first stage, data were obtained from 624 teachers who participated to the study in the 2015-16 academic year. In the second stage, data were obtained in 2019 from 415 teachers via a link to access to the scale and all the instructions for the NSTAS in 2019. The covariance structures analysis was used to examine the measurement invariance of the scale. The comparative fit index was used to compare the measurement invariance in the measurement model. The study revealed that configural, measurement weight and structural covariance invariances were ensured for branches, school types and genders. Residual invariance was ensured only for gender. As a result, it was concluded that the NSTAS scale was not biased for teacher branches, school types or gender. NSTAS scale is recommended for the purposes of comparing branch, school type and gender groups.

Özet

The main aim of this study is to examine the measurement invariance of the structural equating model constructed on the Awareness and Exposure subscales of Nanoscience and Nanotechnology Awareness Scale (NSTAS) test for three teacher branches, three school types, and two genders by using the covariance structural analysis to test configural and metric invariances. The other aim of this study is showing how to use the IBM AMOS-24 software package with examples to address the issue of measurement invariance using the covariance structural analysis approach. Study sample was 1039 complete records gathered from science teachers with convenience sampling. Research data were collected in two stages. In the first stage, data were obtained from 624 teachers who participated to the study in the 2015-16 academic year. In the second stage, data were obtained in 2019 from 415 teachers via a link to access to the scale and all the instructions for the NSTAS in 2019. The covariance structures analysis was used to examine the measurement invariance of the scale. The comparative fit index was used to compare the measurement invariance in the measurement model. The study revealed that configural, measurement weight and structural covariance invariances were ensured for branches, school types and genders. Residual invariance was ensured only for gender. As a result, it was concluded that the NSTAS scale was not biased for teacher branches, school types or gender. NSTAS scale is recommended for the purposes of comparing branch, school type and gender groups.

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Cite This Article

TAN, Ş., IPEK, Z., ATİK, A. D., ERKOÇ, F. (2022). Examining the Invariance of a Measurement Model of Teachers’ Awareness and Exposure Levels to Nanoscience by Using the Covariance Structure Approach. International Journal of Assessment Tools in Education, Cilt: 8 - Sayı: 3, 487–508. https://ajindex.com/articles/540097

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References (8)

  1. AERA, APA, & NCME. (2014). Standards for educational and psychological testing. Washington, DC: American Psychological Association. Arana, F. G., Rice, K. G., & Ashby, J. S. (2018). Perfectionism in Argentina and the United States: Measurement structure, invariance, and implications for depression. Journal of Personality Assessment, 100(2), 219-230. https://doi: 10.1080/00223891.2017.1296845 Bayda, S., Adeel, M., Tuccinardi, T., Cordani, M., & Flavio Rizzolio, F. (2020). The history of nanoscience and nanotechnology: From chemical–physical applications to nanomedicine. Molecules, 25(1), 112. https://doi.org/10.3390/molecules25010112
  2. Blonder, R., Parchmann, I., Akaygun, S., & Albe, V. (2014). Nanoeducation: Zooming into teacher professional development programmes in nanoscience and technology. In C. Bruguière., A, Tiberghien., & P. Clément. (Eds.)., Topics and Trends in Current Science Education (pp. 159–174). 9th ESERA Conference Selected Contributions. New York: Springer. Braeken, J., & Blömeke, S. (2016). Comparing future teachers’ beliefs across countries: Approximate measurement invariance with Bayesian elastic constraints for local item dependence and differential item functioning. Assessment & Evaluation in Higher Education, 41(5), 733–749. http://dx.doi.org/10.1080/02602938.2016.1161005
  3. Bryan, L. A., Sederberg, D., Daly, S., Sears, D., & Giordano, N. (2012). Facilitating teachers’ development of nanoscale science, engineering, and technology content knowledge. Nanotechnology Reviews, 1(1), 85-95. https://doi.org/10.1515/ntrev-2011-0015
  4. Boholm, A., & Larsson, S. (2019). What is the problem? A literature review on challenges facing the communication of nanotechnology to the public. Journal of Nanoparticle Research, 21(86), 1-21. https://doi.org/10.1007/s11051-019-4524-3
  5. Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press.
  6. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge. Camilli, G. (2006). Test fairness. In R. L. Brennan (Ed.), Educational measurement (pp. 221–256).
  7. Praeger. Camerota, M., Willoughby, M. T., Kuhn, L. J., & Blair, C. B. (2018). The childhood executive functioning inventory (CHEXI): Factor structure, measurement invariance, and correlates in US preschoolers. Child Neuropsychology, 24(3), 322 337. http://doi:10.1080/09297049.2016.1247795
  8. Caputo, A. (2017). A brief scale on at