Araştırma Makalesi
BibTex RIS Kaynak Göster

Validity Evidence for the Perceptions of Secondary School Students of ‘What Research is’ Scale and Measurement Invariance

Yıl 2021, , 684 - 703, 05.09.2021
https://doi.org/10.21449/ijate.866764

Öz

Research is a concrete action in academia which has uplifted societies’ prosperity. Although researchers have given particular attention to student perceptions about what research is in a higher education context, little attention has been given to secondary school students’ perceptions about this issue. To fill this gap, Yeoman et al. (2016) qualitatively developed an instrument measuring secondary school students’ perceptions of what research is. The present study quantitatively validates this scale using the dataset originally used to qualitatively validate it. The factor structure of the ‘what research is’ scale and measurement invariance across gender, school type, and key stage was examined. The sample is composed of 2634 secondary school students in seven schools located in East Anglia, UK. The data from this original sample showed a relatively acceptable fit to the four-factor structure after omitting some items. The result also highlighted that whilst there was evidence on configural and metric level invariance (i.e. the factor structures and the factor loadings of the scale are equivalent across gender, school type, and key stage), scalar level invariance was not met (i.e. the item intercepts of the scale are not equivalent across gender, school type, and key stage). Recommendations for future studies and future directions for research are discussed.

Kaynakça

  • Åkerlind, G. S. (2008). An academic perspective on research and being a researcher: An integration of the literature. Studies in Higher Education, 33(1), 17 31. https://doi.org/10.1080/03075070701794775
  • Archer, L., Osborne, J., DeWitt, J., Dillon, J., Wong, B., & Willis, B. (2013). ASPIRES: Young People’s Science and Career Aspirations, age 10 14. https://www.kcl.ac.uk/ecs/research/aspires/aspires-final-report-december-2013.pdf
  • Archer, L., Moote, J., MacLeod, E., Francis, B., & DeWitt, J. (2020). ASPIRES 2: Young people’s science and career aspirations, age 10-19. UCL Institute of Education. https://discovery.ucl.ac.uk/id/eprint/10092041/15/Moote_9538%20UCL%20Aspires%202%20report%20full%20online%20version.pdf
  • Bandura, A. (2006). Adolescent development from an agentic perspective. In: Pajares F and Urdan T (eds) Self-Efficacy Beliefs of Adolescents. Information Age Publishing, pp. 1–43.
  • Bazley, S. (2019). Ensuring Societal Advancement through Science and Technology: Pathways to Scientific Integration. CUSPE Communications https://doi.org/10.17863/CAM.38893
  • Bills, D. (2004). Supervisors' conceptions of research and the implications for supervisor development. International Journal for Academic Development, 9(1), 85-97. https://doi.org/10.1080/1360144042000296099
  • Brew, A. (2001). Conceptions of research: A phenomenographic study. Studies in higher education, 26(3), 271-285. https://doi.org/10.1080/03075070120076255
  • Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485 499. https://doi.org/10.1002/tea.20131
  • Butz, A. R., & Usher, E. L. (2015). Salient sources of self-efficacy in reading and mathematics. Contemporary Educational Psychology, 42, 49 61. https://doi.org/10.1016/j.cedpsych.2015.04.001
  • Buuren, S. V., & Groothuis-Oudshoorn, K. (2010). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1 68. https://doi.org/10.18637/jss.v045.i03
  • Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834
  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modelling, 9(2), 233 255. https://doi.org/10.1207/S15328007SEM0902_5
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Earlbaum Associates.
  • Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
  • Çaparlar, C. Ö., & Dönmez, A. (2016). What is scientific research and how can it be done?. Turkish Journal of Anaesthesiology and Reanimation, 44(4), 212. https://doi.org/10.5152/TJAR.2016.34711
  • DeWitt, J., & Archer, L. (2015). Who aspires to a science career? A comparison of survey responses from primary and secondary school students. International Journal of Science Education, 37(13), 2170-2192. https://doi.org/10.1080/09500693.2015.1071899
  • Donghong, C., & Shunke, S. (2008). The more, the earlier, the better: Science communication supports science education. In Communicating science in social contexts (pp. 151-163). Springer, Dordrecht.
  • Epskamp, S. (2015). semPlot: Unified visualizations of structural equation models. Structural Equation Modeling: a Multidisciplinary Journal, 22(3), 474 483. https://doi.org/10.1080/10705511.2014.937847
  • Fennema, E., & Sherman, J.A. (1976). Fennema-Sherman Mathematics Attitudes Scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal Research Mathematics Education, 7(5), 324 326. https://doi.org/10.2307/748467
  • Georghiou, L. (2015). Value of research. Policy Paper by the Research, Innovation, and Science Policy Experts (RISE), European Commission. https://ec.europa.eu/research/innovation-union/pdf/expert-groups/rise/georghiou-value_research.pdf
  • Grever, M., Haydn, T., & Ribbens, K. (2008). Identity and school history: The perspective of young people from the Netherlands and England. British Journal of Educational Studies, 56(1), 76-94. https://doi.org/10.1111/j.1467-8527.2008.00396.x
  • Griffioen, D. M. (2020). A questionnaire to compare lecturers’ and students’ higher education research integration experiences. Teaching in Higher Education, AHEAD-OF-PRINT, 1-16. https://doi.org/10.1080/13562517.2019.1706162
  • Griffioen, D. M. (2019). The influence of undergraduate students’ research attitudes on their intentions for research usage in their future professional practice. Innovations in Education and Teaching International, 56(2), 162 172. https://doi.org/10.1080/14703297.2018.1425152
  • Griffioen, D. M. (2020). Differences in students’ experiences of research involvement: study years and disciplines compared. Journal of Further and Higher Education, 44(4), 454-466. https://doi.org/10.1080/0309877X.2019.1579894
  • Griffioen, D. M., & de Jong, U. (2015). Implementing research in professional higher education: Factors that influence lecturers’ perceptions. Educational Management Administration & Leadership, 43(4), 626 645. https://doi.org/10.1177/1741143214523008
  • He, J., & van de Vijver, F. (2012). Bias and equivalence in cross-cultural research. Online Readings in Psychology and Culture, 2(2), 2307-0919. https://doi.org/10.9707/2307-0919.1111
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hughes, R. A., White, I. R., Seaman, S. R., Carpenter, J. R., Tilling, K., & Sterne, J. A. (2014). Joint modelling rationale for chained equations. BMC Medical Research Methodology, 14(1), 28. https://doi.org/10.1186/1471-2288-14-28
  • Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426. https://doi.org/10.1007/BF02291366
  • Kelly, U., McNicoll, I., & White, J. (2014). The impact of universities on the UK economy. http://www.universitiesuk.ac.uk/highereducation/Documents/2014/TheImpactOfUniversitiesOnThe UkEconomy.pdf
  • Kiley, M., & Mullins, G. (2005). Supervisors' conceptions of research: What are they?. Scandinavian Journal of Educational Research, 49(3), 245 262. https://doi.org/10.1080/00313830500109550
  • Kline, R.B., (2011). Principles and Practices of Structural Equation Modelling. 3rd ed. The Guilford Press.
  • Mejía-Rodríguez, A. M., Luyten, H., & Meelissen, M. R. (2020). Gender Differences in Mathematics Self-concept Across the World: an Exploration of Student and Parent Data of TIMSS 2015. International Journal of Science and Mathematics Education, Advance online publication,1-22. https://doi.org/10.1007/s10763-020-10100-x
  • Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525-543. https://doi.org/10.1007/BF02294825
  • Meyer, J. H., Shanahan, M. P., & Laugksch, R. C. (2005). Students' Conceptions of Research. I: A qualitative and quantitative analysis. Scandinavian Journal of Educational Research, 49(3), 225-244. https://doi.org/10.1080/00313830500109535
  • Meyer, J. H., Shanahan, M. P., & Laugksch, R. C. (2007). Students' conceptions of research. 2: An exploration of contrasting patterns of variation. Scandinavian Journal of Educational Research, 51(4), 415-433. https://doi.org/10.1080/00313830701485627
  • Moore, N., & Hooley, T. (2012). Talking about career: the language used by and with young people to discuss life, learning and work. Derby: iCeGS, University of Derby. https://derby.openrepository.com/bitstream/handle/10545/220535/Final%20Talking%20about%20career%20iCeGS%20Occasional%20Paper%2015062012%20NPM.pdf?sequence=8&isAllowed=y
  • Mosher, D. A. (2018). The effect of mode of presentation, cognitive load, and individual differences on recall [Doctoral dissertation, University of Reading]. http://centaur.reading.ac.uk/84822/
  • Nishimura H., Kanoshima E., Kono K. (2019). Advancement in Science and Technology and Human Societies. In: Abe S., Ozawa M., Kawata Y. (eds) Science of Societal Safety. Trust (Interdisciplinary Perspectives), vol 2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2775-9_2
  • OECD (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264239012-en
  • Ommering, B. W., Wijnen-Meijer, M., Dolmans, D. H., Dekker, F. W., & van Blankenstein, F. M. (2020). Promoting positive perceptions of and motivation for research among undergraduate medical students to stimulate future research involvement: a grounded theory study. BMC Medical Education, 20(1), 1-12. https://doi.org/10.1186/s12909-020-02112-6
  • Pearson, R. C., Crandall, K. J., Dispennette, K., & Maples, J. M. (2017). Students’ Perceptions of an Applied Research Experience in an Undergraduate Exercise Science Course. International Journal of Exercise Science, 10(7), 926-941.
  • Pitcher, R. (2011). Doctoral students’ conceptions of research. The Qualitative Report, 16(4), 971-983. Retrieved from http://www.nova.edu/ssss/QR/QR16-4/pitcher.pdf.
  • Pitcher, R., & Åkerlind, G. S. (2009). Postdoctoral researchers’ conceptions of research: A metaphor analysis. The International Journal for Researcher Development, 1, 42-56.
  • R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/
  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5 12 (BETA). Journal of Statistical Software, 48(2), 1 36. https://doi.org/10.1108/1759751X201100009
  • Saleem, M. A., Eagle, L., Akhtar, N., & Wasaya, A. (2020). What do prospective students look for in higher degrees by research? A scale development study. Journal of Marketing for Higher Education, 30(1), 45-65. https://doi.org/10.1080/08841241.2019.1678548
  • Salter, A. J., & Martin, B. R. (2001). The economic benefits of publicly funded basic research: a critical review. Research Policy, 30(3), 509-532. https://doi.org/10.1016/S0048-7333(00)00091-3
  • Santos, M. S., Martins, J. V., Silva, A. P. F., Paula, F. G., Domingos, Á., & dos Santos, W. J. (2017). Analysis of the Influence of Undergraduate Research on the Engineering Formation from the Point of View of Students. International Journal of Science and Engineering Investigations, 66(6), 45-51.
  • Schmidt, J. A., Kafkas, S. S., Maier, K. S., Shumow, L., & Kackar-Cam, H. Z. (2019). Why are we learning this? Using mixed methods to understand teachers’ relevance statements and how they shape middle school students’ perceptions of science utility. Contemporary Educational Psychology, 57, 9-31. https://doi.org/10.1016/j.cedpsych.2018.08.005
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Allyn & Bacon.
  • Toma, R. B., & Greca, I. M. (2018). The effect of integrative STEM instruction on elementary students’ attitudes toward science. Eurasia Journal of Mathematics, Science and Technology Education, 14(4), 1383-1395. https://doi.org/10.29333/ejmste/83676
  • Vanderberg, 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. https://doi.org/10.1177/109442810031002
  • Verburgh, A., & Elen, J. (2011). The role of experienced research integration into teaching upon students' appreciation of research aspects in the learning environment. International Journal of University Teaching and Faculty Development, 1(4), 1-14.
  • Visser-Wijnveen, G. J., van der Rijst, R. M., & van Driel, J. H. (2016). A questionnaire to capture students’ perceptions of research integration in their courses. Higher Education, 71(4), 473-488. https://doi.org/10.1007/s10734-015-9918-2
  • Webb-Williams, J. (2018). Science self-efficacy in the primary classroom: Using mixed methods to investigate sources of self-efficacy. Research in Science Education, 48(5), 939-961. https://doi.org/10.1007/s11165-016-9592-0
  • Wikoff, R.L., & Buchalter, B.D. (1986). Factor analysis of four Fennema-Sherman mathematics attitude scales. International Journal Mathematics Education Science Technology, 17(6), 703-706. https://doi.org/10.1080/0020739860170605
  • Yeoman, K., Bowater, L., & Nardi, E. (2016). The representation of scientific research in the national curriculum and secondary school pupils’ perceptions of research, its function, usefulness and value to their lives [version 2; peer review: 2 approved]. F1000Research, 4, 1442. https://doi.org/10.12688/f1000research.7449.2
  • Yeoman, K., Nardi, E., Bowater, L., & Nguyen, H. (2017). ‘Just Google It?’: Pupils’ Perceptions and Experience of Research in the Secondary Classroom. British Journal of Educational Studies, 65(3), 281-305. https://doi.org/10.1080/00071005.2017.1310179

Validity Evidence for the Perceptions of Secondary School Students of ‘What Research is’ Scale and Measurement Invariance

Yıl 2021, , 684 - 703, 05.09.2021
https://doi.org/10.21449/ijate.866764

Öz

Research is a concrete action in academia which has uplifted societies’ prosperity. Although researchers have given particular attention to student perceptions about what research is in a higher education context, little attention has been given to secondary school students’ perceptions about this issue. To fill this gap, Yeoman et al. (2016) qualitatively developed an instrument measuring secondary school students’ perceptions of what research is. The present study quantitatively validates this scale using the dataset originally used to qualitatively validate it. The factor structure of the ‘what research is’ scale and measurement invariance across gender, school type, and key stage was examined. The sample is composed of 2634 secondary school students in seven schools located in East Anglia, UK. The data from this original sample showed a relatively acceptable fit to the four-factor structure after omitting some items. The result also highlighted that whilst there was evidence on configural and metric level invariance (i.e. the factor structures and the factor loadings of the scale are equivalent across gender, school type, and key stage), scalar level invariance was not met (i.e. the item intercepts of the scale are not equivalent across gender, school type, and key stage). Recommendations for future studies and future directions for research are discussed.

Kaynakça

  • Åkerlind, G. S. (2008). An academic perspective on research and being a researcher: An integration of the literature. Studies in Higher Education, 33(1), 17 31. https://doi.org/10.1080/03075070701794775
  • Archer, L., Osborne, J., DeWitt, J., Dillon, J., Wong, B., & Willis, B. (2013). ASPIRES: Young People’s Science and Career Aspirations, age 10 14. https://www.kcl.ac.uk/ecs/research/aspires/aspires-final-report-december-2013.pdf
  • Archer, L., Moote, J., MacLeod, E., Francis, B., & DeWitt, J. (2020). ASPIRES 2: Young people’s science and career aspirations, age 10-19. UCL Institute of Education. https://discovery.ucl.ac.uk/id/eprint/10092041/15/Moote_9538%20UCL%20Aspires%202%20report%20full%20online%20version.pdf
  • Bandura, A. (2006). Adolescent development from an agentic perspective. In: Pajares F and Urdan T (eds) Self-Efficacy Beliefs of Adolescents. Information Age Publishing, pp. 1–43.
  • Bazley, S. (2019). Ensuring Societal Advancement through Science and Technology: Pathways to Scientific Integration. CUSPE Communications https://doi.org/10.17863/CAM.38893
  • Bills, D. (2004). Supervisors' conceptions of research and the implications for supervisor development. International Journal for Academic Development, 9(1), 85-97. https://doi.org/10.1080/1360144042000296099
  • Brew, A. (2001). Conceptions of research: A phenomenographic study. Studies in higher education, 26(3), 271-285. https://doi.org/10.1080/03075070120076255
  • Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485 499. https://doi.org/10.1002/tea.20131
  • Butz, A. R., & Usher, E. L. (2015). Salient sources of self-efficacy in reading and mathematics. Contemporary Educational Psychology, 42, 49 61. https://doi.org/10.1016/j.cedpsych.2015.04.001
  • Buuren, S. V., & Groothuis-Oudshoorn, K. (2010). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1 68. https://doi.org/10.18637/jss.v045.i03
  • Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834
  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modelling, 9(2), 233 255. https://doi.org/10.1207/S15328007SEM0902_5
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Earlbaum Associates.
  • Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
  • Çaparlar, C. Ö., & Dönmez, A. (2016). What is scientific research and how can it be done?. Turkish Journal of Anaesthesiology and Reanimation, 44(4), 212. https://doi.org/10.5152/TJAR.2016.34711
  • DeWitt, J., & Archer, L. (2015). Who aspires to a science career? A comparison of survey responses from primary and secondary school students. International Journal of Science Education, 37(13), 2170-2192. https://doi.org/10.1080/09500693.2015.1071899
  • Donghong, C., & Shunke, S. (2008). The more, the earlier, the better: Science communication supports science education. In Communicating science in social contexts (pp. 151-163). Springer, Dordrecht.
  • Epskamp, S. (2015). semPlot: Unified visualizations of structural equation models. Structural Equation Modeling: a Multidisciplinary Journal, 22(3), 474 483. https://doi.org/10.1080/10705511.2014.937847
  • Fennema, E., & Sherman, J.A. (1976). Fennema-Sherman Mathematics Attitudes Scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal Research Mathematics Education, 7(5), 324 326. https://doi.org/10.2307/748467
  • Georghiou, L. (2015). Value of research. Policy Paper by the Research, Innovation, and Science Policy Experts (RISE), European Commission. https://ec.europa.eu/research/innovation-union/pdf/expert-groups/rise/georghiou-value_research.pdf
  • Grever, M., Haydn, T., & Ribbens, K. (2008). Identity and school history: The perspective of young people from the Netherlands and England. British Journal of Educational Studies, 56(1), 76-94. https://doi.org/10.1111/j.1467-8527.2008.00396.x
  • Griffioen, D. M. (2020). A questionnaire to compare lecturers’ and students’ higher education research integration experiences. Teaching in Higher Education, AHEAD-OF-PRINT, 1-16. https://doi.org/10.1080/13562517.2019.1706162
  • Griffioen, D. M. (2019). The influence of undergraduate students’ research attitudes on their intentions for research usage in their future professional practice. Innovations in Education and Teaching International, 56(2), 162 172. https://doi.org/10.1080/14703297.2018.1425152
  • Griffioen, D. M. (2020). Differences in students’ experiences of research involvement: study years and disciplines compared. Journal of Further and Higher Education, 44(4), 454-466. https://doi.org/10.1080/0309877X.2019.1579894
  • Griffioen, D. M., & de Jong, U. (2015). Implementing research in professional higher education: Factors that influence lecturers’ perceptions. Educational Management Administration & Leadership, 43(4), 626 645. https://doi.org/10.1177/1741143214523008
  • He, J., & van de Vijver, F. (2012). Bias and equivalence in cross-cultural research. Online Readings in Psychology and Culture, 2(2), 2307-0919. https://doi.org/10.9707/2307-0919.1111
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hughes, R. A., White, I. R., Seaman, S. R., Carpenter, J. R., Tilling, K., & Sterne, J. A. (2014). Joint modelling rationale for chained equations. BMC Medical Research Methodology, 14(1), 28. https://doi.org/10.1186/1471-2288-14-28
  • Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426. https://doi.org/10.1007/BF02291366
  • Kelly, U., McNicoll, I., & White, J. (2014). The impact of universities on the UK economy. http://www.universitiesuk.ac.uk/highereducation/Documents/2014/TheImpactOfUniversitiesOnThe UkEconomy.pdf
  • Kiley, M., & Mullins, G. (2005). Supervisors' conceptions of research: What are they?. Scandinavian Journal of Educational Research, 49(3), 245 262. https://doi.org/10.1080/00313830500109550
  • Kline, R.B., (2011). Principles and Practices of Structural Equation Modelling. 3rd ed. The Guilford Press.
  • Mejía-Rodríguez, A. M., Luyten, H., & Meelissen, M. R. (2020). Gender Differences in Mathematics Self-concept Across the World: an Exploration of Student and Parent Data of TIMSS 2015. International Journal of Science and Mathematics Education, Advance online publication,1-22. https://doi.org/10.1007/s10763-020-10100-x
  • Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525-543. https://doi.org/10.1007/BF02294825
  • Meyer, J. H., Shanahan, M. P., & Laugksch, R. C. (2005). Students' Conceptions of Research. I: A qualitative and quantitative analysis. Scandinavian Journal of Educational Research, 49(3), 225-244. https://doi.org/10.1080/00313830500109535
  • Meyer, J. H., Shanahan, M. P., & Laugksch, R. C. (2007). Students' conceptions of research. 2: An exploration of contrasting patterns of variation. Scandinavian Journal of Educational Research, 51(4), 415-433. https://doi.org/10.1080/00313830701485627
  • Moore, N., & Hooley, T. (2012). Talking about career: the language used by and with young people to discuss life, learning and work. Derby: iCeGS, University of Derby. https://derby.openrepository.com/bitstream/handle/10545/220535/Final%20Talking%20about%20career%20iCeGS%20Occasional%20Paper%2015062012%20NPM.pdf?sequence=8&isAllowed=y
  • Mosher, D. A. (2018). The effect of mode of presentation, cognitive load, and individual differences on recall [Doctoral dissertation, University of Reading]. http://centaur.reading.ac.uk/84822/
  • Nishimura H., Kanoshima E., Kono K. (2019). Advancement in Science and Technology and Human Societies. In: Abe S., Ozawa M., Kawata Y. (eds) Science of Societal Safety. Trust (Interdisciplinary Perspectives), vol 2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2775-9_2
  • OECD (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264239012-en
  • Ommering, B. W., Wijnen-Meijer, M., Dolmans, D. H., Dekker, F. W., & van Blankenstein, F. M. (2020). Promoting positive perceptions of and motivation for research among undergraduate medical students to stimulate future research involvement: a grounded theory study. BMC Medical Education, 20(1), 1-12. https://doi.org/10.1186/s12909-020-02112-6
  • Pearson, R. C., Crandall, K. J., Dispennette, K., & Maples, J. M. (2017). Students’ Perceptions of an Applied Research Experience in an Undergraduate Exercise Science Course. International Journal of Exercise Science, 10(7), 926-941.
  • Pitcher, R. (2011). Doctoral students’ conceptions of research. The Qualitative Report, 16(4), 971-983. Retrieved from http://www.nova.edu/ssss/QR/QR16-4/pitcher.pdf.
  • Pitcher, R., & Åkerlind, G. S. (2009). Postdoctoral researchers’ conceptions of research: A metaphor analysis. The International Journal for Researcher Development, 1, 42-56.
  • R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/
  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5 12 (BETA). Journal of Statistical Software, 48(2), 1 36. https://doi.org/10.1108/1759751X201100009
  • Saleem, M. A., Eagle, L., Akhtar, N., & Wasaya, A. (2020). What do prospective students look for in higher degrees by research? A scale development study. Journal of Marketing for Higher Education, 30(1), 45-65. https://doi.org/10.1080/08841241.2019.1678548
  • Salter, A. J., & Martin, B. R. (2001). The economic benefits of publicly funded basic research: a critical review. Research Policy, 30(3), 509-532. https://doi.org/10.1016/S0048-7333(00)00091-3
  • Santos, M. S., Martins, J. V., Silva, A. P. F., Paula, F. G., Domingos, Á., & dos Santos, W. J. (2017). Analysis of the Influence of Undergraduate Research on the Engineering Formation from the Point of View of Students. International Journal of Science and Engineering Investigations, 66(6), 45-51.
  • Schmidt, J. A., Kafkas, S. S., Maier, K. S., Shumow, L., & Kackar-Cam, H. Z. (2019). Why are we learning this? Using mixed methods to understand teachers’ relevance statements and how they shape middle school students’ perceptions of science utility. Contemporary Educational Psychology, 57, 9-31. https://doi.org/10.1016/j.cedpsych.2018.08.005
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Allyn & Bacon.
  • Toma, R. B., & Greca, I. M. (2018). The effect of integrative STEM instruction on elementary students’ attitudes toward science. Eurasia Journal of Mathematics, Science and Technology Education, 14(4), 1383-1395. https://doi.org/10.29333/ejmste/83676
  • Vanderberg, 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. https://doi.org/10.1177/109442810031002
  • Verburgh, A., & Elen, J. (2011). The role of experienced research integration into teaching upon students' appreciation of research aspects in the learning environment. International Journal of University Teaching and Faculty Development, 1(4), 1-14.
  • Visser-Wijnveen, G. J., van der Rijst, R. M., & van Driel, J. H. (2016). A questionnaire to capture students’ perceptions of research integration in their courses. Higher Education, 71(4), 473-488. https://doi.org/10.1007/s10734-015-9918-2
  • Webb-Williams, J. (2018). Science self-efficacy in the primary classroom: Using mixed methods to investigate sources of self-efficacy. Research in Science Education, 48(5), 939-961. https://doi.org/10.1007/s11165-016-9592-0
  • Wikoff, R.L., & Buchalter, B.D. (1986). Factor analysis of four Fennema-Sherman mathematics attitude scales. International Journal Mathematics Education Science Technology, 17(6), 703-706. https://doi.org/10.1080/0020739860170605
  • Yeoman, K., Bowater, L., & Nardi, E. (2016). The representation of scientific research in the national curriculum and secondary school pupils’ perceptions of research, its function, usefulness and value to their lives [version 2; peer review: 2 approved]. F1000Research, 4, 1442. https://doi.org/10.12688/f1000research.7449.2
  • Yeoman, K., Nardi, E., Bowater, L., & Nguyen, H. (2017). ‘Just Google It?’: Pupils’ Perceptions and Experience of Research in the Secondary Classroom. British Journal of Educational Studies, 65(3), 281-305. https://doi.org/10.1080/00071005.2017.1310179
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eğitim Üzerine Çalışmalar
Bölüm Makaleler
Yazarlar

Nurullah Eryılmaz 0000-0003-1916-8295

Yayımlanma Tarihi 5 Eylül 2021
Gönderilme Tarihi 22 Ocak 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Eryılmaz, N. (2021). Validity Evidence for the Perceptions of Secondary School Students of ‘What Research is’ Scale and Measurement Invariance. International Journal of Assessment Tools in Education, 8(3), 684-703. https://doi.org/10.21449/ijate.866764

23823             23825             23824