Developing a Scale to Measure Students’ Attitudes toward Science

Yıl 2019, Cilt: 6 Sayı: 4, 706 - 720, 05.01.2020

Adem Akkuş

https://doi.org/10.21449/ijate.548516

Cited By: 19

Öz

The
aim of this study is to develop a science attitude scale (SAS). For that
purpose, the literature review has been done for suggestions for creating
scales and a new draft scale developed. The draft scale was analyzed by
specialists and a pilot study is done after its approval by experts. The SAS is
prepared with 21 items and among these, 11 items are reverse-coded. The SAS
consists of Likert-type items. The sample of the study consists of 154 college
students studying at the Faculty of Education, Elementary Science Education,
and Elementary Education departments. Principal axis factoring with orthogonal
rotation (varimax) was used for exploratory factor analysis. Factor eigenvalues
were checked with respect to parallel analysis and numbers of the factors were
determined with respect to the analysis. Items that did not serve the purpose
of the scale were omitted from the SAS. The finalized SAS’ Cronbach alpha value
is .953. For confirmatory factor analysis data were collected from a different
sample which consists of university students who were studying at elementary
science education, elementary education, and electric electronic engineering
departments. Number of sample is 201. Confirmatory factor analyses run through
Amos 24.0 software. It is believed that SAS is a valuable contribution to the
science education field since it has unidimensional structure and proved its
item discrimination power, and alongside with an excellent internal consistency.
SAS also offers opportunity to develop multidimensional science attitude scale.
For that purpose, original SAS and English version of it are provided in
appendixes.

Anahtar Kelimeler

science attitude, scale, scale development, science

Developing a Scale to Measure Students’ Attitudes toward Science

Öz

The aim of this study is to develop a science attitude scale (SAS). For that purpose, the literature review has been done for suggestions for creating scales and a new draft scale developed. The draft scale was analyzed by specialists and a pilot study is done after its approval by experts. The SAS is prepared with 21 items and among these, 11 items are reverse-coded. The SAS consists of Likert-type items. The sample of the study consists of 154 college students studying at the Faculty of Education, Elementary Science Education, and Elementary Education departments. Principal axis factoring with orthogonal rotation (varimax) was used for exploratory factor analysis. Factor eigenvalues were checked with respect to parallel analysis and numbers of the factors were determined with respect to the analysis. Items that did not serve the purpose of the scale were omitted from the SAS. The finalized SAS’ Cronbach alpha value is .953. For confirmatory factor analysis data were collected from a different sample which consists of university students who were studying at elementary science education, elementary education, and electric electronic engineering departments. Number of sample is 201. Confirmatory factor analyses run through Amos 24.0 software. It is believed that SAS is a valuable contribution to the science education field since it has unidimensional structure and proved its item discrimination power, and alongside with an excellent internal consistency. SAS also offers opportunity to develop multidimensional science attitude scale. For that purpose, original SAS and English version of it are provided in appendixes.

Anahtar Kelimeler

Science Attitude, Scale Development, Scale validation

Kaynakça

• Adesoji, F. A. (2008). Managing students’ attitude towards science through problem–solving instructional strategy. The Anthropologist, 10 (1), 21-24.
• Ajzen, I. (2005a). Behavioral interventions based on the theory of planned behavior: Brief description of the theory of planned behavior. Retrieved from http://people.umass.edu/aizen/pdf/tpb.intervention.pdf (accessed on 5 February 2019)
• Ajzen, I. (2005b). Constructing a theory of planned behavior questionnaire: Brief description of the theory of planned behavior. Retrieved from http://people.umass.edu/aizen/pdf/tpb.measurement.pdf (accessed on 5 February 2019)
• Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155–173. DOI: 10.1007/bf02294170
• Anthoine, E., Moret, L., Regnault, A., Sébille, V., & Hardouin, J.-B. (2014). Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures. Health and Quality of Life Outcomes, 12, 2. DOI:10.1186/s12955-014-0176-2
• Bernardez, R.Q. (1982). Factors affecting attitudes to laboratory work. Unpublished Master Thesis, Saint Louis University, Baguio, Philippines. [Abstract]
• Brinkman, W-P. (2009). Design of a questionnaire instrument, handbook of mobile technology research methods. ISBN 978-1-60692-767-0, pp. 31-57 Netherlands: Nova Publisher
• Browne, M.W. & Cudeck, R., 1993. Alternative ways of assessing model fit. In: Bollen, K.A. and Long, J. S. (Eds.) Testing structural equation models, Beverly Hills, CA: Sage
• Byrne, B. M. &Campbell, T. L. (1999). Cross-cultural comparisons and the presumption of equivalent measurement and theoretical structure: A look beneath the surface. Journal of Cross Cultural Psychology, 30, 557 576. DOI: https://doi.org/10.1177/0022022199030005001
• Cabrera-Nguyen, P. (2010). Author guidelines for reporting scale development and validation results in the Journal of the Society for Social Work and Research. Journal of the Society for Social Work and Research, 1(2), 99-103.
• Can, M., & Şahin, Ç. (2015). Okul öncesi öğretmen adaylarının fene ve fen öğretimine yönelik tutumlarının incelenmesi [Investigating Prospective Kindergarten Teachers' Science and Science Teaching Attitudes]. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 15 (2), 13-26. DOI: 10.17240/aibuefd.2015.15.2-5000161311
• Carlback, J. & Wong, A. (2018). A study on factors influencing acceptance of using mobile electronic identification applications in Sweden. Retrieved from http://www.diva-portal.org/smash/get/diva2:1214313/FULLTEXT01.pdf (accessed on 03 April 2019)
• Cheung, D. (2007). Confirmatory factor analysis of the attitude toward chemistry lessons scale. Paper presented at 2nd NICE symposium, Taipei, Taiwan, July 30-31, 2007.
• Chinda, T., Techapreechawong, S., & Teeraprasert, S. (2012). An investigation of relationships between employees’ safety and productivity. Retrieved from http://www.ppml.url.tw/EPPM/conferences/2012/download/SESSON4_A/10%20E145.pdf (accessed on 12 October, 2019)
• Coll, R.L., Dalgety, J. & Salter, D. (2002). The development of the chemistry attitudes and experiences questionnaire (CAEQ). Chemistry Education Research and Practice in Europe, 3(1), 19-32.
• Demirbaş, M. (2009). The relationships between the scientist perception and scientific attitudes of science teacher candidates in Turkey: A case study. Scientific Research and Essays, 4(6), 565-576.
• Deshpande, L. (2004). Challenges in measurement of scientific attitude. Paper presented at epiSTEME-1: An International Conference to Review Research on Science Technology and Mathematics Education (137-138), Goa, India, December 13-17, 2004.
• Dönmez, F., & Azizoğlu, N. (2010). Investigation of the students science process skill levels in vocational schools: a case of Balıkesir. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 4 (2), 79-109.
• Ellis, J.E. (2017). Factor analysis and item analysis. Applying Statistics in Behavioural Research (pp. 11 59). Retrieved from https://www.applyingstatisticsinbehaviouralresearch.com/documenten/factor_analysis_and_item_analysis_version_11_.pdf (accessed on 22 December 2018)
• Evrekli, E., İnel, D., Balım, A. G., & Kesercioğlu, T. (2009). The attitude scale of constructivist approach for prospective science teachers: a study of validity and reliability. Journal of Turkish Science Education, 6(2), 134-148.
• Field, A. (2013). Discovering statistics using ibm spss statistics (4th Edition). London: SAGE
• Finn, A.N. (2012) Teacher use of prosocial and antisocial power bases and students’ perceived instructor understanding and misunderstanding in the college classroom. Communication Education, 61(1), 67-79, DOI: 10.1080/03634523.2011.636450
• Foley, B., & McPhee, C. (2008). Students’ attitudes towards science in classes using hands-on or textbook based curriculum. AERA, 1-12.
• Fraser, B.J. (1981). Test of science-related attitudes (TOSRA) handbook. Victoria: Allanby
• Garson, G.D., 2006. Structural equation modelling. North Carolina: G. David Garson and Statistical Associates Publishing
• Hayton, J.C., Allen, D.G & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191-205. DOI: 10.1177/1094428104263675
• Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 2 (1), 104 121. DOI:10.1177/109442819800100106
• Hinkin, T. R., Tracey, J. B., & Enz, C. A. (1997). Scale construction: Developing reliable and valid measurement instruments. Journal of Hospitality & Tourism Research, 21(1), 100-120. DOI:10.1177/109634809702100108
• Hof, M.W. (2012). Questionnaire Evaluation with Factor Analysis and Cronbach’ s Alpha: An Example. Retrieved from http://www.let.rug.nl/nerbonne/teach/rema stats meth seminar/student papers/MHof-QuestionnaireEvaluation-2012-Cronbach-FactAnalysis.pdf (accessed on 02 May 2016).
• Hooper, D., Coughlan, J. & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. The Electronic Journal of Business Research Methods, 6 (1), 53 – 60. DOI: 10.21427/D7CF7R
• Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit: in Structural Equation Modeling Ed. Rick H. Hoyle. London: Sage Publications
• Hu, L.-T., & Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1 55. DOI: https://dx.doi.org/10.1080/10705519909540118
• Johanson, G.A., & Brooks, G.P. (2010). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394 400. DOI: 10.1177/0013164409355692
• Johnson, R.L. & Morgan, G.B. (2016). Survey scales: Investigating scale quality. New York, NY: The Guilford Press.
• Kalaycı, Ş. (2010). Spss uygulamalı çok değişkenli istatistik teknikleri. (5. Baskı) [SPSS applied various statistical techniques (5th Edition)]. Ankara: Asil Yayın Dağıtım Ltd. Şti.
• Kaya, Ç. & Altinkurt, Y. (2018). Öğretmenlerin psikolojik sermayeleri ile tükenmişlik düzeyleri arasındaki ilişkide psikolojik ve yapısal güçlendirmenin rolü [Role of Psychological and Structural Empowerment in the Relationship between Teachers’ Psychological Capital and Their Levels of Burnout]. Eğitim ve Bilim, 43 (193), 63-78, DOI: http://dx.doi.org/10.15390/EB.2018.6961
• Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd edition) New York: The Guilford Press
• Knekta, E., Runyon, C., & Eddy, S. (2019). One Size Doesn’t Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research. CBE—Life Sciences Education, 18 (1), 1-17. DOI: https://doi.org/10.1187/cbe.18-04-0064
• Kurnaz, M.A. & Yigit, N. (2010). Physics attitude scale: Development, validity and reliability. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 4 (1), 29-49.
• Korkmaz, Ö., Şahin, A. & Yeşil, R. (2011). Bilimsel araştırmaya yönelik tutum ölçeği geçerlik ve güvenirlik çalışması [Study of Validity and Reliability of Scale of Attitude towards Scientific Research]. Elementary Education Online, 10 (3), 961-973.
• Lovelace, M. & Brickman, P. (2013). Best practices for measuring students’ attitudes toward learning science. CBE-Life Sciences Education, 12(4), 606-617. DOI: 10.1187/cbe.12-11-0197
• Meyer, J. (n.d.). Correlated errors in confirmatory factor analysis. Retrieved from https://www.theanalysisfactor.com/correlated-errors-in-confirmatory-factor-analysis (accessed on 17 September 2019).
• Moore, R.W. & Foy, R.L.H. (1997). The scientific attitude inventory: A revision (SAI II). Journal of Research in Science Teaching, 34(4), 327-336.
• Nayir, F. (2013). “Algılanan örgütsel destek ölçeğinin” kısa form geçerlik güvenirlik çalışması [“Perceived Organizational Support Scale”- Short Form Validity-Reliability Study]. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 28, 89-106.
• Nuhoglu, H. (2008). The development of an attitude scale for science and technology course. Elementary Education Online, 7(3), 627-639.
• Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor anlaysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43 (3), 37-40.
• Planing, P. (2014). Innovation Acceptance: The Case of Advanced Driver-Assistance Systems: Quantitative research approach (pp. 230-231). Stuttgart: Springer
• Sica, C. & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). New York: Nova
• Schrodt, P., Witt, P.L., Turman, P.D., Myers, S.A., Barton, M.H & Jernberg, K.A. (2009) Instructor credibility as a mediator of instructors’ prosocial communication behaviors and students’ learning outcomes. Communication Education, 58(3), 350-371, DOI: 10.1080/03634520902926851
• Serin, O., & Mohammadzadeh, B. (2008). The relationship between primary school students’ attitudes towards science and their science achievement (sampling: Izmir). Journal of Educational Sciences, 2 (6), 68-75.
• Shadfar, M. & Malekmohammadi, I. (2013). Application of Structural Equation Modeling (SEM) in restructuring state intervention strategies toward paddy production development. International Journal of Academic Research in Business and Social Sciences, 3 (12), 576-618. DOI: 10.6007/IJARBSS/v3-i12/472
• Shah, Z.A. & Mahmood, N. (2011). Developing a Scale to Measure Attitude towards Science Learning among School Students. Bulletin of Education and Research, 33 (1), 71-81.
• Shi, D., Lee, T., & Maydeu-Olivares, A. (2018). Understanding the model size effect on SEM fit indices. Educational and Psychological Measurement, 79(2), 310-334. DOI: https://doi.org/10.1177%2F0013164418783530
• Tortop, H. S. (2013). Bilimsel Alan Gezisi Tutum Ölçeği Adaptasyon Çalışması [Adaptation Study of Attitude Scale towards Scientific Field Trips]. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 2(1), 228.
• TTK. (2017, July 18). Müfredatta yenileme ve değişiklik çalışmalarımız üzerine [On curriculum revision and changes work]. Retrieved from https://ttkb.meb.gov.tr/meb_iys_dosyalar/2017_07/18160003_basin_aciklamasi-program.pdf (accessed on 25 August 2018).
• Watkins, M. W. (2000). Monte carlo PCA for parallel analysis [computer software]. State College, PA: Ed & Psych Associates.
• Wong., M. & Lian, S. (2003). Development of a self-efficacy scale for assessing secondary school students’ science self efficacy beliefs. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.472.8479 (accessed on 19 October 2014)
• Vassallo, M., & Saba, A. (2015). Does money for grocery expenditure sway Italian consumers’ motivational values in predicting Attitude towards eco-sustainable food products? Contemporary Management Research, 11(1), 3-22. DOI: doi:10.7903/cmr.13840