Validation of Student Attitudes toward STEM (S-STEM) Survey in Secondary Schools in Tanzania
Corresponding Author(s) : florence kyaruzi
Journal of Humanities & Social Science (JHSS),
Vol. 10 No. 5 (2021)
Abstract
Tanzania has been taking various initiatives to promote positive attitudes towards, and interest in science, technology, engineering and mathematics (STEM) among students. Such initiatives call for systematic tools to be used to assess students’ attitudes towards STEM so as to inform appropriate interventions. This study investigated the extent to which S-STEM is a valid psychometric instrument for measuring secondary school students’ attitudes towards STEM in the Tanzanian context. Additionally, the study sought to establish whether S-STEM is a valid measure of students’ attitudes towards STEM across gender. Data was gathered from 16 secondary schools. A total of 1,382 Form 2 (Grade 10) students were sampled from four districts in Northern (N = 659) and North-eastern (N = 723) Tanzania. The study adopted four scales from the S-STEM survey that measures students’ attitudes toward STEM, and the 21st century skills. Structural equation modelling and measurement invariance techniques were used to assess the validity of the S-STEM survey in the sampled schools. A structural equation model with 31 out of 37 initially validated items in the S-STEM survey attained moderate to good fit, hence it was valid for measuring both girls’ and boys’ attitudes towards STEM and 21st century skills in Tanzanian secondary schools. Finally, implications for future practices and further research are discussed.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- Anderson, J. C. & D. W. Gerbing. 1988. Structural Equation Modeling in Practice: A Review and Recommended 2–Step Approach. Psychological Bulletin, 103(3): 411–423. https: //doi.org/ 10.1037/0033–2909.103.3.411. Ary, D., L. C. Jacobs, C. K. Sorensen & D. A. Walker. 2010. Introduction to Research in Education (8th Ed.). Betmont, Carfornia: Wadsworth Cengage Learning. Brown, G. T. L., L. R. Harris, C. O’Quin & K. E. Lane. 2015. Using Multi-Group Confirmatory Factor Analysis to Evaluate Cross-Cultural Research: Identifying and Understanding Non-Invariance. International Journal of Research & Method in Education, 40(1): 66–90. https: //doi.org/ 10.1080/ 1743727x.2015.1070823. Brown, G. T. L., L. R. Harris, C. O’Quin & K. E. Lane. 2017. Using Multi-Group Confirmatory Factor Analysis to Evaluate Cross-Cultural Research: Identifying and Understanding Non-Invariance. International Journal of Research & Method in Education, 40(1): 66–90. https: //doi.org/10. 1080/1743727x.2015.1070823. Byrne, B. M. 2010. Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming. London: Routledge. Chachashvili-Bolotin, S., M. Milner-Bolotin & S. Lissitsa. 2016. Examination of Factors Predicting Secondary Students’ Interest in Tertiary STEM Education. International Journal of Science Education, 38(3): 366–390, https: //doi.org/ 10.1080/09500693.2016.1143137. Cohen, L., L. Manion & K. Marrison. 2020. Research Methods in Education (8th Ed). London: Routledge. Contini, D., M. L. D. Tommaso & S. Mendolia. 2017. The Gender Gap in Mathematics Achievement: Evidence from Italian Data. Economics of Education Review, 58: 32–42. Dabney, K. P., R. H. Tai, J. T. Almarode, J. L. Miller- Friedmann, G. Sonnert, P. M. Sadler & Z. Hazari. 2012. Out-of-school Time Science Activities and Their Association with Career Interest in STEM. International Journal of Science Education, Part B: Communication and Public Engagement, 2(1): 63– 79, https: //doi.org/10.1080/21548455.2011.629455. Dasgupta, N & J. G. Stout. 2014. Girls and Women in Science, Technology, Engineering & Mathematics: Stemming the Tide and Broadening Participation in STEM Careers. Policy Insights from the Behavioral and Brain Sciences, 1(1): 21–29. Dickerson, A., S. Mcintosh & C. Valente. 2015. Do the Maths: An Analysis of the Gender Gap in Mathematics in Africa. Economics of Education Review, 46: 1–22. Fan, X. & S. A. Sivo. 2007. Sensitivity of Fit Indices to Model Misspecification and Model Types. Multivariate Behavioral Research, 42(3): 509–529. Francis, B., L. Archer, J. Moote, J. Dewitt, E. Macleod & L. Yeomans. 2017. The Construction of Physics as a Quintessentially Masculine Subject?: Young People’s Perceptions of Genderissues in Access to Physics. Sex Roles, 76: 156–174. Grimmon, A. S., J. Cramer, D. Yazilitas, I. Smeets & P. De Bruyckere. 2020. Interest in STEM Among Children with a Low Socio-economic Status: Further Support for the STEM-CIS-Instrument Through the Adapted Dutch STEM-LIT Measuring Instrument. Cogent Education, 7(1): 1745541, https: //doi.org/10.1080/2331186x.2020.1745541. Hermida, R. 2015. The Problem of Allowing Correlated Errors in Structural Equation Modeling: Concerns and Considerations. Computational Methods in Social Sciences, 3(1): 5–17. Hudson, M., Y. Baek, Y. Ching & K. Rice. 2020. Using a Multifaceted Robotics-Based Intervention to Increase Student Interest in STEM Subjects and Careers. Journal for STEM Education Research. https: //doi.org/10.1007/s41979–020–00032–0. Hu, L. & P. M. Bentler. 1999. Cut-off Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1): 1–55. https: //doi.org/10.1080/10705519909540118. Falk, J. H., N. Staus, L. D. Dierking, W. Penuel, J. Wyld & D. Bailey. 2015: Understanding Youth STEM Interest Pathways Within a Single Community: The Synergies Project, International Journal of Science Education, Part B, https: //doi.org/10.1080/21548455.2015.1093670. Finney, S. J. & C. Distefano. 2013. Non-Normal and Categorical Data in Structural Equation Modeling. In G. R. Hancock and R. O. Mueller (Eds.) Structural Equation Modeling: A Second Course, 2nd Edn. Charlotte, NC: Information Age Publishing, 439–492. Kabote, S.J., E. Niboye & C. I. Nombo. 2014. Performance in Mathematics and Science Subjects: A Gender Perspective for Selected Primary Schools in Rural and Urban Tanzania. International Journal of Gender and Women’s Studies, 2(3): 87–105. Kier, M.W., M. R. Blanchard, J. W. Osborne & J. L. Albert. 2014. The Development of the STEM Career Interest Survey (STEM-CIS). Research in Science Education, 44: 461–481. Kitta, S. & F. Tilya. 2010. The Status of Learner-Centred Learning and Assessment in Tanzania: The Context of the Competence-Based Curriculum. Papers in Education and Development, 29: 77–90. Kolne, K. & S. Lindsay. 2020. A Systematic Review of Programs and Interventions for Increasing the Interest and Participation of Children and Youth with Disabilities in STEM Education Or Careers. Journal of Occupational Science. https: //doi.org/10.1080/14427591.2019.1692692. Komba, W. & Nkumbi, E. 2008. Teacher Professional Development in Tanzania: Perceptions and Practices. Journal of International Cooperation in Education, 11(3): 67–83. Krapp, A. & M. Prenzel. 2011. Research on Interest in Science: Theories, Methods & Findings. International Journal of Science Education, 33(1): 27–50. Kulas, J. T. & A. A. Stachowski. 2009. Middle Category Endorsement in Odd-Numbered Likert Response Scales: Associated Item Characteristics, Cognitive Demands & Preferred Meanings. Journal of Research in Personality, 43(3): 489–493. http: //dx.doi.org/10.1016/j.jrp.2008.12.005. Kyaruzi, F., J. W. Strijbos, S. Ufer & G. T. L. Brown. 2018. Teacher AFL Perceptions and Feedback Practices in Mathematics Education Among Secondary Schools in Tanzania. Studies in Educational Evaluation, 59: 1–9. https: //doi.org/10.1016/j.stueduc.2018.01.004. Kyaruzi, F., J. W. Strijbos, S. Ufer & G. T. Brown. 2019. Students’ Formative Assessment Perceptions, Feedback Use and Mathematics Performance in Secondary Schools in Tanzania. Assessment in Education: Principles, Policy & Practice, 26(3): 278–302. https: //doi.org/ 10.1080/ 0969594x.2019.1593103. Kyaruzi, F. 2021. Impact of Gender on Sources of Students’ Self-Efficacy in Mathematics in Tanzanian Secondary Schools. International Journal of School & Educational Psychology. https: //doi.org/10.1080/21683603.2021.1945512. Legewie, J. & T. A. Diprete. 2014. The High School Environment and the Gender Gap in Science and Engineering. Sociology of Education, 87(4): 259–280. Lent, R. W., A. M. Lopez, F. G. Lopez & H. B. Sheu. 2008. Social Cognitive Career Theory and the Prediction of Interests and Choice Goals in the Computing Disciplines. Journal of Vocational Behavior, 73(1): 52–62. Miller, K., G. Sonnert & P. Sadler. 2017. The Influence of Students’ Participation in STEM Competitions on Their Interest in STEM Careers. International Journal of Science Education, Part B. https: //doi.org/10.1080/21548455.2017.1397298. Ministry of Education and Vocational Training, (MoEVT). 2014. Sera ya Elimu na Mafunzo [Education Training and Policy]. Dar es Salaam: MoEVT. Ministry of Education, Science and Technology (MoEST). 2016. Science and Technology Syllabus for Basic Education for Standard III-VI. Dar es Salaam: Tanzania Institute of Education. —. 2019. National Basic Education Statistics in Tanzania (BEST). Dodoma: Government Press. Mkimbili, S.T. 2019. Meaningful Science Learning by the Use of an Additional Language: A Tanzanian Perspective. African Journal of Research in Mathematics, Science and Technology Education, https: //doi.org/10.1080/18117295.2019.1654212. Moosa, D. 2013. Challenges to Anonymity and Representation in Educational Qualitative Research in a Small Community: A Reflection on My Research Journey. Compare: A Journal of Comparative and International Education, 43(4): 483–495. https: //doi.org/10.1080/03057925.2013.797733. Musil, C. M., C. B. Warner, P. K. Yobas & S. L. Jones. 2002. A Comparison of Imputation Techniques for Handling Missing Data. Western Journal of Nursing Research, 24(7): 815–829. https: //doi.org/10.1177/019394502762477004. Ndalichako, J. L. & A. A. Komba. 2014. Students’ Subject Choice in Secondary Schools in Tanzania: A Matter of Students’ Ability and Interests Or Forced Circumstances? Open Journal of Social Sciences, 2: 49–56. https: //doi.org/ http: //dx.doi.org/10.4236/jss.2014.28008. Peugh, L. J. & C.K. Enders. 2004. Missing Data in Educational Research: A Review of Reporting Practices and Suggestions for Improvement. Review of Educational Research, 74(4): 525–556. https: //doi.org/10.3102/00346543074004525. Price, C. A., F. Kares, G. Segovia & A. B. Loyd. 2018. Staff Matter: Gender Differences in Science, Technology, Engineering Or Math (STEM) Career Interest Development in Adolescent Youth. Applied Developmental Science, https: //doi.org/ 10.1080/10888691.2017.1398090. Reddy, L. 2017. Gender Differences in Attitudes to Learning Science in Grade 7. African Journal of Research in Mathematics, Science and Technology Education, 21(1): 26–36, https: //doi. org/ 10.1080/ 18117295.2017.1279450. Renninger, K. A. & S. Hidi. 2011. Revisiting the Conceptualization, Measurement & Generation of Interest. Educational Psychologist, 46(3): 168–184. Sax, L. J., M. A. Kanny, T. A. Riggers-Piehl, H. Whang & L. N. Paulson. 2015. “But I’m Not Good at Math”: The Changing Salience of Mathematical Self-Concept in Shaping Women’s and Men’s STEM Aspirations. Research in Higher Education, 56: 813–842. Schoenfeld, J., G. Segal & D. Borgia. 2017. Social Cognitive Career Theory and the Goal of Becoming a Certified Public Accountant. Accounting Education. https: //doi.org/ 10.1080 /09639284. 2016.1274909. Semali, L & K. Mehta. 2012. Science Education in Tanzania: Challenges and Policy Responses. International Journal of Educational Research, 53: 225–239. Tzu-Ling, H. 2019. Gender Differences in High-School Learning Experiences, Motivation, SelfEfficacy & Career Aspirations Among Taiwanese STEM College Students. International Journal of Science Education, 41(13): 1870–1884. https: //doi.org/ 10.1080/09500693.2019.1645963. Unfried, A., M. Faber, D.S. Stanhope & E. Wiebe. 2015. The Development and Validation of a Measure of Student Attitudes Toward Science, Technology, Engineering & Math (S STEM). Journal of Psychoeducational Assessment, 33(7): 622–639. Vandenberg, R. J. & C. E. Lance. 2000. A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices & Recommendations for Organizational Research. Organizational Research Methods, 3(4): 4–70. https: //doi.org/10.1177/109442810031002. Wang, M. & J. Degol. 2013. Motivational Pathways to STEM Career Choices: Using Expectancy – Value Perspective to Understand Individual and Gender Differences in STEM Fields. Developmental Review, 33(4): 304–340. Zuo, H., K. A. Ferris, M. Laforce. 2019. Reducing Racial and Gender Gaps in Mathematics Attitudes: Investigating the Use of Instructional Strategies in Inclusive STEM High Schools. Journal for STEM Education Research, https: //doi.org/10.1007/s41979–019–00021–y.
References
Anderson, J. C. & D. W. Gerbing. 1988. Structural Equation Modeling in Practice: A Review and Recommended 2–Step Approach. Psychological Bulletin, 103(3): 411–423. https: //doi.org/ 10.1037/0033–2909.103.3.411. Ary, D., L. C. Jacobs, C. K. Sorensen & D. A. Walker. 2010. Introduction to Research in Education (8th Ed.). Betmont, Carfornia: Wadsworth Cengage Learning. Brown, G. T. L., L. R. Harris, C. O’Quin & K. E. Lane. 2015. Using Multi-Group Confirmatory Factor Analysis to Evaluate Cross-Cultural Research: Identifying and Understanding Non-Invariance. International Journal of Research & Method in Education, 40(1): 66–90. https: //doi.org/ 10.1080/ 1743727x.2015.1070823. Brown, G. T. L., L. R. Harris, C. O’Quin & K. E. Lane. 2017. Using Multi-Group Confirmatory Factor Analysis to Evaluate Cross-Cultural Research: Identifying and Understanding Non-Invariance. International Journal of Research & Method in Education, 40(1): 66–90. https: //doi.org/10. 1080/1743727x.2015.1070823. Byrne, B. M. 2010. Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming. London: Routledge. Chachashvili-Bolotin, S., M. Milner-Bolotin & S. Lissitsa. 2016. Examination of Factors Predicting Secondary Students’ Interest in Tertiary STEM Education. International Journal of Science Education, 38(3): 366–390, https: //doi.org/ 10.1080/09500693.2016.1143137. Cohen, L., L. Manion & K. Marrison. 2020. Research Methods in Education (8th Ed). London: Routledge. Contini, D., M. L. D. Tommaso & S. Mendolia. 2017. The Gender Gap in Mathematics Achievement: Evidence from Italian Data. Economics of Education Review, 58: 32–42. Dabney, K. P., R. H. Tai, J. T. Almarode, J. L. Miller- Friedmann, G. Sonnert, P. M. Sadler & Z. Hazari. 2012. Out-of-school Time Science Activities and Their Association with Career Interest in STEM. International Journal of Science Education, Part B: Communication and Public Engagement, 2(1): 63– 79, https: //doi.org/10.1080/21548455.2011.629455. Dasgupta, N & J. G. Stout. 2014. Girls and Women in Science, Technology, Engineering & Mathematics: Stemming the Tide and Broadening Participation in STEM Careers. Policy Insights from the Behavioral and Brain Sciences, 1(1): 21–29. Dickerson, A., S. Mcintosh & C. Valente. 2015. Do the Maths: An Analysis of the Gender Gap in Mathematics in Africa. Economics of Education Review, 46: 1–22. Fan, X. & S. A. Sivo. 2007. Sensitivity of Fit Indices to Model Misspecification and Model Types. Multivariate Behavioral Research, 42(3): 509–529. Francis, B., L. Archer, J. Moote, J. Dewitt, E. Macleod & L. Yeomans. 2017. The Construction of Physics as a Quintessentially Masculine Subject?: Young People’s Perceptions of Genderissues in Access to Physics. Sex Roles, 76: 156–174. Grimmon, A. S., J. Cramer, D. Yazilitas, I. Smeets & P. De Bruyckere. 2020. Interest in STEM Among Children with a Low Socio-economic Status: Further Support for the STEM-CIS-Instrument Through the Adapted Dutch STEM-LIT Measuring Instrument. Cogent Education, 7(1): 1745541, https: //doi.org/10.1080/2331186x.2020.1745541. Hermida, R. 2015. The Problem of Allowing Correlated Errors in Structural Equation Modeling: Concerns and Considerations. Computational Methods in Social Sciences, 3(1): 5–17. Hudson, M., Y. Baek, Y. Ching & K. Rice. 2020. Using a Multifaceted Robotics-Based Intervention to Increase Student Interest in STEM Subjects and Careers. Journal for STEM Education Research. https: //doi.org/10.1007/s41979–020–00032–0. Hu, L. & P. M. Bentler. 1999. Cut-off Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1): 1–55. https: //doi.org/10.1080/10705519909540118. Falk, J. H., N. Staus, L. D. Dierking, W. Penuel, J. Wyld & D. Bailey. 2015: Understanding Youth STEM Interest Pathways Within a Single Community: The Synergies Project, International Journal of Science Education, Part B, https: //doi.org/10.1080/21548455.2015.1093670. Finney, S. J. & C. Distefano. 2013. Non-Normal and Categorical Data in Structural Equation Modeling. In G. R. Hancock and R. O. Mueller (Eds.) Structural Equation Modeling: A Second Course, 2nd Edn. Charlotte, NC: Information Age Publishing, 439–492. Kabote, S.J., E. Niboye & C. I. Nombo. 2014. Performance in Mathematics and Science Subjects: A Gender Perspective for Selected Primary Schools in Rural and Urban Tanzania. International Journal of Gender and Women’s Studies, 2(3): 87–105. Kier, M.W., M. R. Blanchard, J. W. Osborne & J. L. Albert. 2014. The Development of the STEM Career Interest Survey (STEM-CIS). Research in Science Education, 44: 461–481. Kitta, S. & F. Tilya. 2010. The Status of Learner-Centred Learning and Assessment in Tanzania: The Context of the Competence-Based Curriculum. Papers in Education and Development, 29: 77–90. Kolne, K. & S. Lindsay. 2020. A Systematic Review of Programs and Interventions for Increasing the Interest and Participation of Children and Youth with Disabilities in STEM Education Or Careers. Journal of Occupational Science. https: //doi.org/10.1080/14427591.2019.1692692. Komba, W. & Nkumbi, E. 2008. Teacher Professional Development in Tanzania: Perceptions and Practices. Journal of International Cooperation in Education, 11(3): 67–83. Krapp, A. & M. Prenzel. 2011. Research on Interest in Science: Theories, Methods & Findings. International Journal of Science Education, 33(1): 27–50. Kulas, J. T. & A. A. Stachowski. 2009. Middle Category Endorsement in Odd-Numbered Likert Response Scales: Associated Item Characteristics, Cognitive Demands & Preferred Meanings. Journal of Research in Personality, 43(3): 489–493. http: //dx.doi.org/10.1016/j.jrp.2008.12.005. Kyaruzi, F., J. W. Strijbos, S. Ufer & G. T. L. Brown. 2018. Teacher AFL Perceptions and Feedback Practices in Mathematics Education Among Secondary Schools in Tanzania. Studies in Educational Evaluation, 59: 1–9. https: //doi.org/10.1016/j.stueduc.2018.01.004. Kyaruzi, F., J. W. Strijbos, S. Ufer & G. T. Brown. 2019. Students’ Formative Assessment Perceptions, Feedback Use and Mathematics Performance in Secondary Schools in Tanzania. Assessment in Education: Principles, Policy & Practice, 26(3): 278–302. https: //doi.org/ 10.1080/ 0969594x.2019.1593103. Kyaruzi, F. 2021. Impact of Gender on Sources of Students’ Self-Efficacy in Mathematics in Tanzanian Secondary Schools. International Journal of School & Educational Psychology. https: //doi.org/10.1080/21683603.2021.1945512. Legewie, J. & T. A. Diprete. 2014. The High School Environment and the Gender Gap in Science and Engineering. Sociology of Education, 87(4): 259–280. Lent, R. W., A. M. Lopez, F. G. Lopez & H. B. Sheu. 2008. Social Cognitive Career Theory and the Prediction of Interests and Choice Goals in the Computing Disciplines. Journal of Vocational Behavior, 73(1): 52–62. Miller, K., G. Sonnert & P. Sadler. 2017. The Influence of Students’ Participation in STEM Competitions on Their Interest in STEM Careers. International Journal of Science Education, Part B. https: //doi.org/10.1080/21548455.2017.1397298. Ministry of Education and Vocational Training, (MoEVT). 2014. Sera ya Elimu na Mafunzo [Education Training and Policy]. Dar es Salaam: MoEVT. Ministry of Education, Science and Technology (MoEST). 2016. Science and Technology Syllabus for Basic Education for Standard III-VI. Dar es Salaam: Tanzania Institute of Education. —. 2019. National Basic Education Statistics in Tanzania (BEST). Dodoma: Government Press. Mkimbili, S.T. 2019. Meaningful Science Learning by the Use of an Additional Language: A Tanzanian Perspective. African Journal of Research in Mathematics, Science and Technology Education, https: //doi.org/10.1080/18117295.2019.1654212. Moosa, D. 2013. Challenges to Anonymity and Representation in Educational Qualitative Research in a Small Community: A Reflection on My Research Journey. Compare: A Journal of Comparative and International Education, 43(4): 483–495. https: //doi.org/10.1080/03057925.2013.797733. Musil, C. M., C. B. Warner, P. K. Yobas & S. L. Jones. 2002. A Comparison of Imputation Techniques for Handling Missing Data. Western Journal of Nursing Research, 24(7): 815–829. https: //doi.org/10.1177/019394502762477004. Ndalichako, J. L. & A. A. Komba. 2014. Students’ Subject Choice in Secondary Schools in Tanzania: A Matter of Students’ Ability and Interests Or Forced Circumstances? Open Journal of Social Sciences, 2: 49–56. https: //doi.org/ http: //dx.doi.org/10.4236/jss.2014.28008. Peugh, L. J. & C.K. Enders. 2004. Missing Data in Educational Research: A Review of Reporting Practices and Suggestions for Improvement. Review of Educational Research, 74(4): 525–556. https: //doi.org/10.3102/00346543074004525. Price, C. A., F. Kares, G. Segovia & A. B. Loyd. 2018. Staff Matter: Gender Differences in Science, Technology, Engineering Or Math (STEM) Career Interest Development in Adolescent Youth. Applied Developmental Science, https: //doi.org/ 10.1080/10888691.2017.1398090. Reddy, L. 2017. Gender Differences in Attitudes to Learning Science in Grade 7. African Journal of Research in Mathematics, Science and Technology Education, 21(1): 26–36, https: //doi. org/ 10.1080/ 18117295.2017.1279450. Renninger, K. A. & S. Hidi. 2011. Revisiting the Conceptualization, Measurement & Generation of Interest. Educational Psychologist, 46(3): 168–184. Sax, L. J., M. A. Kanny, T. A. Riggers-Piehl, H. Whang & L. N. Paulson. 2015. “But I’m Not Good at Math”: The Changing Salience of Mathematical Self-Concept in Shaping Women’s and Men’s STEM Aspirations. Research in Higher Education, 56: 813–842. Schoenfeld, J., G. Segal & D. Borgia. 2017. Social Cognitive Career Theory and the Goal of Becoming a Certified Public Accountant. Accounting Education. https: //doi.org/ 10.1080 /09639284. 2016.1274909. Semali, L & K. Mehta. 2012. Science Education in Tanzania: Challenges and Policy Responses. International Journal of Educational Research, 53: 225–239. Tzu-Ling, H. 2019. Gender Differences in High-School Learning Experiences, Motivation, SelfEfficacy & Career Aspirations Among Taiwanese STEM College Students. International Journal of Science Education, 41(13): 1870–1884. https: //doi.org/ 10.1080/09500693.2019.1645963. Unfried, A., M. Faber, D.S. Stanhope & E. Wiebe. 2015. The Development and Validation of a Measure of Student Attitudes Toward Science, Technology, Engineering & Math (S STEM). Journal of Psychoeducational Assessment, 33(7): 622–639. Vandenberg, R. J. & C. E. Lance. 2000. A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices & Recommendations for Organizational Research. Organizational Research Methods, 3(4): 4–70. https: //doi.org/10.1177/109442810031002. Wang, M. & J. Degol. 2013. Motivational Pathways to STEM Career Choices: Using Expectancy – Value Perspective to Understand Individual and Gender Differences in STEM Fields. Developmental Review, 33(4): 304–340. Zuo, H., K. A. Ferris, M. Laforce. 2019. Reducing Racial and Gender Gaps in Mathematics Attitudes: Investigating the Use of Instructional Strategies in Inclusive STEM High Schools. Journal for STEM Education Research, https: //doi.org/10.1007/s41979–019–00021–y.