Examining the Invariance of a Measurement Model of Teachers’ Awareness and Exposure Levels to Nanoscience by Using the Covariance Structure Approach
Year 2021,
, 487 - 508, 05.09.2021
Şeref Tan
,
Zeki Ipek
,
Ali Derya Atik
,
Figen Erkoç
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.
References
- 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
- 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
- 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
- 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
- Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press.
- 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). 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
- Caputo, A. (2017). A brief scale on attitude toward learning of scientific subjects (ATLoSS) for middle school students. Journal of Educational, Cultural and Psychological Studies, 16, 56-76. http://dx.doi.org/10.7358/ecps-2017-016-capu
- Casas, Y., & Blanco-Blanco, A. (2017). Testing Social Cognitive Career Theory in Colombian adolescent secondary students: a study in the field of mathematics and science. Revista Complutense de Educación, 28(4) 1173-1192. http://dx.doi.org/10.5209/RCED.52572
- 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
- Chung H., Kim, J., Park R., Bamer A. M., Bocell, F. D., & Amtmann D. (2016). Testing the measurement invariance of the University of Washington Self-Efficacy Scale short form across four diagnostic subgroups. Qual Life Res, 25(10), 2559-2564. http://doi: 10.1007/s11136-016-1300-z
- Dyehouse, M. A., Diefes-Dux, H. A., Bennett, D. E., & Imbrie, P. K. (2008). Development of an instrument to measure undergraduates’ nanotechnology awareness, exposure, motivation and knowledge. Journal of Science Education and Technology, 17(5), 500-510. https://doi.org/10.1007/s10956-008-9117-3
- Enil, G., & Köseoğlu, Y. (2016). Investigation of nanotechnology awareness, interests, and attitudes of pre-service science (Physics, Chemistry and Biology) teachers. International Journal of Social Sciences and Education Research, 2(1), 50 63. https://doi.org/10.24289/ijsser.279084
- Greenberg, A. (2009). Integrating nanoscience into the classroom: Perspectives on nanoscience education projects. ACS Nano, 3(4), 762-769. https://doi: 10.1021/nn900335r
- Hingant, B., & Albe, V. (2010). Nanosciences and nanotechnologies learning and teaching in secondary education: A review of literature. Studies in Science Education, 46(2), 121-152. https://doi.org/10.1080/03057267.2010.504543
- Holland, L. A., Carver, J. S., Veltri, L. M., Henderson, R. J., & Quedado, K. D. (2018). Enhancing research for undergraduates through a nanotechnology training program that utilizes analytical and bioanalytical tools. Analytical and Bioanalytical Chemistry, 410, 6041-6050. http://doi: 10.1007/s00216-018-1274-5
- İpek, Z. (2017). Research on awareness levels of physics, chemistry, and biology teachers about nanoscience and nanotechnology. [Doctoral Dissertation, Gazi University, Ankara]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
- İpek, Z., Atik, A. D., Tan, Ş., & Erkoç, F. (2020). Study of the validity and reliability of Nanotechnology Awareness Scale in Turkish Culture. International Journal of Assessment Tools in Education, 7(4), 674-689. https://doi.org/10.21449/ijate.708169
- Jones, M. G., Blonder, R., Gardner, G. E., Albe, V., Falvo, M., & Chevrier, J. (2013). Nanotechnology and nanoscale science: Educational challenges. International Journal of Science Education, 35(9), 1490–1512. http://doi: 10.1080/09500693.2013.771828
- Laherto, A. (2010). An analysis of the educational significance of nanoscience and nanotechnology in scientific and technological literacy. Science Education International, 21(3), 160-175.
- Luo, W., Wei, H.-R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM survey to evaluate a middle school robotics learning environment: validity evidence in a different context. Journal of Science Education and Technology, 28, 429-443. https://doi.org/10.1007/s10956-019-09773-z
- Maier, M. F., Greenfield D. B., & Bulotsky-Shearer R. J. (2013). Development and validation of a preschool teachers’ attitudes and beliefs toward science teaching questionnaire. Early Childhood Research Quarterly 28, 366– 378. https://doi.org/10.1016/j.ecresq.2012.09.003
- Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525-543. http://dx.doi.org/10.1007/BF02294825
- Millsap, R. E., & Yun-Tein, J. (2004) Assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research, 39(3), 479-515. http://doi:10.1207/ S15327906MBR3903_4
- Rocabado, G. A., Kilpatrick, N. A., Mooring, S. R., & Lewis J. E. (2019). Can we compare attitude scores among diverse populations? An exploration of measurement invariance testing to support valid comparisons between black female students and their peers in an organic chemistry course. Journal of Chemical Education, 96, 2371-2382. http://doi:10.1021/acs.jchemed.9b00516
- Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
- Tan, Ş., & Pektaş, S. (2020). Examining the invariance of a measurement model by using the covariance structure approach. International Journal of Contemporary Educational Research, 7(2), 27-39. https://doi.org/10.33200/ijcer.756865
- 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. http://doi:10.1177/109442810031002
- Wagler, A., & Wagler, R. (2013). Addressing the lack of measurement invariance for the measure of acceptance of the theory of evolution. International Journal of Science Education, 35(13), 2278-2298. http://dx.doi.org/10.1080/09500693.2013.808779.
- Wicherts, J. M. (2016). The importance of measurement invariance in neurocognitive ability test ing. The Clinical Neuropsychologist, 30(7), 1006 1016. https://doi.org/10.1080/13854046.2016.1205136
Examining the Invariance of a Measurement Model of Teachers’ Awareness and Exposure Levels to Nanoscience by Using the Covariance Structure Approach
Year 2021,
, 487 - 508, 05.09.2021
Şeref Tan
,
Zeki Ipek
,
Ali Derya Atik
,
Figen Erkoç
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.
References
- 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
- 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
- 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
- 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
- Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press.
- 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). 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
- Caputo, A. (2017). A brief scale on attitude toward learning of scientific subjects (ATLoSS) for middle school students. Journal of Educational, Cultural and Psychological Studies, 16, 56-76. http://dx.doi.org/10.7358/ecps-2017-016-capu
- Casas, Y., & Blanco-Blanco, A. (2017). Testing Social Cognitive Career Theory in Colombian adolescent secondary students: a study in the field of mathematics and science. Revista Complutense de Educación, 28(4) 1173-1192. http://dx.doi.org/10.5209/RCED.52572
- 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
- Chung H., Kim, J., Park R., Bamer A. M., Bocell, F. D., & Amtmann D. (2016). Testing the measurement invariance of the University of Washington Self-Efficacy Scale short form across four diagnostic subgroups. Qual Life Res, 25(10), 2559-2564. http://doi: 10.1007/s11136-016-1300-z
- Dyehouse, M. A., Diefes-Dux, H. A., Bennett, D. E., & Imbrie, P. K. (2008). Development of an instrument to measure undergraduates’ nanotechnology awareness, exposure, motivation and knowledge. Journal of Science Education and Technology, 17(5), 500-510. https://doi.org/10.1007/s10956-008-9117-3
- Enil, G., & Köseoğlu, Y. (2016). Investigation of nanotechnology awareness, interests, and attitudes of pre-service science (Physics, Chemistry and Biology) teachers. International Journal of Social Sciences and Education Research, 2(1), 50 63. https://doi.org/10.24289/ijsser.279084
- Greenberg, A. (2009). Integrating nanoscience into the classroom: Perspectives on nanoscience education projects. ACS Nano, 3(4), 762-769. https://doi: 10.1021/nn900335r
- Hingant, B., & Albe, V. (2010). Nanosciences and nanotechnologies learning and teaching in secondary education: A review of literature. Studies in Science Education, 46(2), 121-152. https://doi.org/10.1080/03057267.2010.504543
- Holland, L. A., Carver, J. S., Veltri, L. M., Henderson, R. J., & Quedado, K. D. (2018). Enhancing research for undergraduates through a nanotechnology training program that utilizes analytical and bioanalytical tools. Analytical and Bioanalytical Chemistry, 410, 6041-6050. http://doi: 10.1007/s00216-018-1274-5
- İpek, Z. (2017). Research on awareness levels of physics, chemistry, and biology teachers about nanoscience and nanotechnology. [Doctoral Dissertation, Gazi University, Ankara]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
- İpek, Z., Atik, A. D., Tan, Ş., & Erkoç, F. (2020). Study of the validity and reliability of Nanotechnology Awareness Scale in Turkish Culture. International Journal of Assessment Tools in Education, 7(4), 674-689. https://doi.org/10.21449/ijate.708169
- Jones, M. G., Blonder, R., Gardner, G. E., Albe, V., Falvo, M., & Chevrier, J. (2013). Nanotechnology and nanoscale science: Educational challenges. International Journal of Science Education, 35(9), 1490–1512. http://doi: 10.1080/09500693.2013.771828
- Laherto, A. (2010). An analysis of the educational significance of nanoscience and nanotechnology in scientific and technological literacy. Science Education International, 21(3), 160-175.
- Luo, W., Wei, H.-R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM survey to evaluate a middle school robotics learning environment: validity evidence in a different context. Journal of Science Education and Technology, 28, 429-443. https://doi.org/10.1007/s10956-019-09773-z
- Maier, M. F., Greenfield D. B., & Bulotsky-Shearer R. J. (2013). Development and validation of a preschool teachers’ attitudes and beliefs toward science teaching questionnaire. Early Childhood Research Quarterly 28, 366– 378. https://doi.org/10.1016/j.ecresq.2012.09.003
- Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525-543. http://dx.doi.org/10.1007/BF02294825
- Millsap, R. E., & Yun-Tein, J. (2004) Assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research, 39(3), 479-515. http://doi:10.1207/ S15327906MBR3903_4
- Rocabado, G. A., Kilpatrick, N. A., Mooring, S. R., & Lewis J. E. (2019). Can we compare attitude scores among diverse populations? An exploration of measurement invariance testing to support valid comparisons between black female students and their peers in an organic chemistry course. Journal of Chemical Education, 96, 2371-2382. http://doi:10.1021/acs.jchemed.9b00516
- Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
- Tan, Ş., & Pektaş, S. (2020). Examining the invariance of a measurement model by using the covariance structure approach. International Journal of Contemporary Educational Research, 7(2), 27-39. https://doi.org/10.33200/ijcer.756865
- 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. http://doi:10.1177/109442810031002
- Wagler, A., & Wagler, R. (2013). Addressing the lack of measurement invariance for the measure of acceptance of the theory of evolution. International Journal of Science Education, 35(13), 2278-2298. http://dx.doi.org/10.1080/09500693.2013.808779.
- Wicherts, J. M. (2016). The importance of measurement invariance in neurocognitive ability test ing. The Clinical Neuropsychologist, 30(7), 1006 1016. https://doi.org/10.1080/13854046.2016.1205136