Development, validation, and factorial comparison of the McGill Self-Efficacy of Learners For Inquiry Engagement (McSELFIE) survey in natural science disciplines

References

• Ashton, M. C., & Lee, K. (2001). A theoretical basis for the major dimensions of personality. European Journal of Personality, 15, 327–353. doi:10.1002/per.417
   Web of Science ®Google Scholar
• Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO Model of personality structure. Personality and Social Psychology Review, 11, 150–166. doi:10.1177/1088868306294907
   PubMed Web of Science ®Google Scholar
• Aulls, M. W., & Shore, B. M. (2008). Inquiry in education (Vol. 1): The conceptual foundations for research as a curricular imperative. New York, NY: Erlbaum.
   Google Scholar
• Baldwin, J. A., Ebert-May, D., & Burns, D. J. (1999). The development of college biology self-efficacy instrument for nonmajors. Science Education, 83, 397–408. doi:10.1002/(SICI)1098-237X(199907)83:4<397::AID-SCE1>3.0.CO;2-#
   Web of Science ®Google Scholar
• Bandalos, D. L., & Finney, S. J. (2010). Factor analysis: Exploratory and confirmatory. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 93–114). New York, NY: Routledge.
   Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. doi:10.1037/0033-295x.84.2.191
   PubMed Web of Science ®Google Scholar
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147. doi:10.1037/0003-066x.37.2.122
   Web of Science ®Google Scholar
• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175–1184. doi:10.1037/0003-066x.44.9.1175
   PubMed Web of Science ®Google Scholar
• Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287. doi:10.1016/0749-5978(91)90022-L
   Web of Science ®Google Scholar
• Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.
   Google Scholar
• Bandura, A. (2006). Guide to constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age.
   Google Scholar
• Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Multifaceted impact of self-efficacy beliefs on academic functioning. Child Development, 67, 1206–1222. doi:10.2307/1131888
   PubMed Web of Science ®Google Scholar
• Bartimote-Aufflick, K., Bridgeman, A., Walker, R., Sharma, M., & Smith, L. (2015). The study, evaluation, and improvement of university student self-efficacy. Studies in Higher Education, 1–25. doi:10.1080/03075079.2014.999319
   Web of Science ®Google Scholar
• Becher, T. (1994). The significance of disciplinary differences. Studies in Higher Education, 19, 151–161. doi:10.1080/03075079412331382007
   Web of Science ®Google Scholar
• Bieschke, K. J., Bishop, R. M., & Garcia, V. L. (1996). The utility of the Research Self-Efficacy Scale. Journal of Career Assessment, 4, 59–75. doi:10.1177/106907279600400104
   Web of Science ®Google Scholar
• Bong, M. (2006). Asking the right question: How confident are you that you could successfully perform these tasks? In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 287–303). Greenwich, CT: Information Age.
   Google Scholar
• Bransford, J. D., Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school. Washington, DC: National Academies Press.
   Google Scholar
• Broudy, H. S. (1977). Types of knowledge and purposes of education. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the acquisition of knowledge (pp. 1–17). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
   Google Scholar
• Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: Guilford Press.
   Google Scholar
• Bunterm, T., Lee, K., Ng Lan Kong, J., Srikoon, S., Vangpoomyai, P., Rattanavongsa, J., & Rachahoon, G. (2014). Do different levels of inquiry lead to different learning outcomes? A comparison between guided and structured inquiry. International Journal of Science Education, 36, 1937–1959. doi:10.1080/09500693.2014.886347
   Web of Science ®Google Scholar
• Buss, D. M. (1991). Evolutionary personality psychology. Annual Review of Psychology, 42, 459–491. doi: 10.1146/annurev.ps.42.020191.002331
   PubMed Web of Science ®Google Scholar
• Buss, D. M. (1996). Social adaptation and five major factors of personality. In J. S. Wiggins (Ed.), The five factor model of personality: Theoretical perspectives (pp. 180–207). New York, NY: Guilford Press.
   Google Scholar
• Carberry, A. R., Lee, H. S., & Ohland, M. W. (2010). Measuring engineering design self-efficacy. Journal of Engineering Education, 99, 71–79. doi:10.1002/j.2168-9830.2010.tb01043.x
   Web of Science ®Google Scholar
• Cavallo, A. M. L., Potter, W. H., & Rozman, M. (2004). Gender differences in learning constructs, shifts in learning constructs, and their relationship to course achievement in a structured inquiry, yearlong college physics course for life science majors. School Science and Mathematics, 104, 288–300. doi:10.1111/j.1949-8594.2004.tb18000.x
   Google Scholar
• Chen, J. A., Metcalf, S. J., & Tutwiler, M. S. (2014). Motivation and beliefs about the nature of scientific knowledge within an immersive virtual ecosystems environment. Contemporary Educational Psychology, 39, 112–123. doi:10.1016/j.cedpsych.2014.02.004
   Web of Science ®Google Scholar
• Committee on Integrated STEM Education, Honey, M., Pearson, G., & Schweingruber, H. A. (2014). STEM integration on K-12 education. Washington, DC: National Academies Press.
   Google Scholar
• Denissen, J. J. A., & Penke, L. (2008). Motivational individual reaction norms underlying the five-factor model of personality: First steps towards a theory-based conceptual framework. Journal of Research in Personality, 42, 1285–1302. doi:10.1016/j.jrp.2008.04.002
   Web of Science ®Google Scholar
• Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417–440. doi:10.1146/annurev.ps.41.020190.002221
   Web of Science ®Google Scholar
• Erduran, S., & Dagher, Z. (2014). Reconceptualizing the nature of science education: Scientific knowledge, practices, and other family categories. Dordrecht: Springer.
   Google Scholar
• Fencl, H. S., & Scheel, K. R. (2004). Pedagogical approaches, contextual variables, and the development of student self-efficacy in undergraduate physics courses. In J. Marx, S. Franklin, & K. Cummings (Eds.), Proceedings of the 2003 physics education research conference (Vol. 720, pp. 173–176). College Park, MD: American Institute of Physics.
   Google Scholar
• Ferguson, G. A., & Takane, Y. (1989). Statistical analysis in psychology and education (6th ed.). New York, NY: McGraw-Hill.
   Google Scholar
• Ford, M. J. (2015). Educational implications of choosing ‘practice’ to describe science in the next generation science standards. Science Education, 99, 1041–1048. doi:10.1002/sce.21188
   Web of Science ®Google Scholar
• Geiser, C. (2012). Data analysis with Mplus. New York, NY: Guilford Press.
   Google Scholar
• Gogus, A. (2012). Outcomes of learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 2534–2539). Boston, MA: Springer.
   Google Scholar
• Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48, 26–34. doi:10.1037/0003-066X.48.1.26
   PubMed Web of Science ®Google Scholar
• Gorin, J. S. (2007). Reconsidering issues in validity theory. Educational Researcher, 36, 456–462. doi:10.3102/0013189X07311607
   Google Scholar
• Griffiths, R. (2004). Knowledge production and the research-teaching nexus: The case of the built environment disciplines. Studies in Higher Education, 29, 709–726. doi:10.1080/0307507042000287212
   Web of Science ®Google Scholar
• Healey, M. (2005a). Linking research and teaching: Disciplinary spaces. In R. Barnett (Ed.), Reshaping the university: New relationships between research, scholarship and teaching (pp. 30–42). Maidenhead: Open University Press.
   Google Scholar
• Healey, M. (2005b). Linking research and teaching to benefit student learning. Journal of Geography in Higher Education, 29, 183–201. doi:10.1080/03098260500130387
   Web of Science ®Google Scholar
• Healey, M., Jordan, F., Pell, B., & Short, C. (2010). The research-teaching nexus: A case study of students’ awareness, experiences and perceptions of research. Innovations in Education and Teaching International, 47, 235–246. doi:10.1080/14703291003718968
   Web of Science ®Google Scholar
• Holden, G., Barker, K., Meenaghan, T., & Rosenberg, G. (1999). Research self-efficacy: A new possibility for educational outcomes assessment. Journal of Social Work Education, 35, 463–476. Retrieved from http://www.jstor.org/stable/23043572.
   Web of Science ®Google Scholar
• Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84. doi:10.1016/j.edurev.2015.11.002
   Web of Science ®Google Scholar
• Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453. doi:10.1037/1082-989x.3.4.424
   Web of Science ®Google Scholar
• Ibrahim, A., Aulls, M. W., & Shore, B. M. (2016). Teachers' roles, students' personalities, inquiry learning outcomes, and practices of science and engineering: The development and validation of the McGill Attainment Value for Inquiry Engagement Survey in STEM disciplines. International Journal of Science and Mathematics Education. Advance online publication. doi:10.1007/s10763-016-9733-y
   Google Scholar
• Jansen, M., Scherer, R., & Schroeders, U. (2015). Students’ self-concept and self-efficacy in the sciences: Differential relations to antecedents and educational outcomes. Contemporary Educational Psychology, 41, 13–24. doi:10.1016/j.cedpsych.2014.11.002
   Web of Science ®Google Scholar
• John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the integrative big five trait taxonomy: History, measurement, and conceptual issues. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research ( 3rd ed., pp. 114–158). New York, NY: Guilford Press.
   Google Scholar
• Kane, M. T. (2008). Terminology, emphasis, and utility in validation. Educational Researcher, 37, 76–82. doi:10.3102/0013189X08315390
   Web of Science ®Google Scholar
• Ketelhut, D. J. (2007). The impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in ‘River City,’ a multi-user virtual environment. Journal of Science Education and Technology, 16, 99–111. doi:10.1007/sl0956-006-9038-y
   Google Scholar
• Ketelhut, D. J. (2010). Assessing gaming, computer and scientific inquiry self-efficacy in a virtual environment. In L. Annetta & S. Bronsak (Eds.), Serious educational game assessment: Practical methods and models for educational games, simulations and virtual worlds (pp. 1–18). New York, NY: Sense.
   Google Scholar
• Knapp, T. R., & Mueller, R. O. (2010). Reliability and validity of instruments. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences (pp. 337–342). New York, NY: Routledge.
   Google Scholar
• Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011). The big five personality traits, learning styles, and academic achievement. Personality and Individual Differences, 51, 472–477. doi:10.1016/j.paid.2011.04.019
   Web of Science ®Google Scholar
• Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41, 212–218. doi:10.1207/s15430421tip4104_2
   Web of Science ®Google Scholar
• Kuzel, A. J. (1999). Sampling in qualitative inquiry. In B. F. Crabtree & W. L. Miller (Eds.), Doing qualitative research ( 2nd ed., pp. 33–45). Thousand Oaks, CA: Sage.
   Google Scholar
• Lamb, R. L., Vallett, D., & Annetta, L. (2014). Development of a short-form measure of science and technology self-efficacy using Rasch analysis. Journal of Science Education and Technology, 23, 641–657. doi:10.1007/S10956-014-9491-Y
   Web of Science ®Google Scholar
• Latour, B., & Woolgar, S. (1986). Laboratory life: The social construction of scientific facts. Princeton, NJ: Princeton University Press.
   Google Scholar
• Lederman, N. G., & Lederman, J. S. (2013). Is it STEM or ‘S & M’ that we truly love? Journal of Science Teacher Education, 24, 1237–1240. doi:10.1007/s10972-013-9370-z
   Google Scholar
• Lei, P. W., & Wu, Q. (2012). Estimation in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 164–180). New York, NY: Guilford Press.
   Google Scholar
• Lent, R. W., Brown, S. D., & Larkin, K. C. (1984). Relation of self-efficacy expectations to academic achievement and persistence. Journal of Counseling Psychology, 31, 356–362. doi:10.1037/0022-0167.31.3.356
   Web of Science ®Google Scholar
• McCrae, R. R. (1993). Openness to experience as a basic dimension of personality. Imagination, Cognition and Personality, 13, 39–55. doi:10.2190/h8h6-qykr-keu8-gaq0
   Google Scholar
• McCrae, R. R., & Costa, P. T. (1996). Toward a new generation of personality theories: Theoretical contexts for the Five-Factor Model. In J. S. Wiggins (Ed.), The Five Factor Model of personality: Theoretical perspectives (pp. 51–87). New York, NY: Guilford Press.
   Google Scholar
• McCrae, R. R., & Costa, P. T. (1997). Conceptions and correlates of openness to experience. In R. Hogan, J. A. Johnson, & S. Briggs (Eds.), Handbook of personality psychology (pp. 825–847). San Diego, CA: Academic Press.
   Google Scholar
• McCrae, R. R., & John, O. P. (1992). An introduction to the Five-Factor Model and its applications. Journal of Personality, 60, 175–215. doi:10.1111/j.1467-6494.1992.tb00970.x
   PubMed Web of Science ®Google Scholar
• Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage.
   Google Scholar
• Morizot, J. (2014). Construct validity of adolescents’ self-reported big five personality traits: Importance of conceptual breadth and initial validation of a short measure. Assessment, 21, 580–606. doi:10.1177/1073191114524015
   PubMed Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O. (2012). Special modeling issues. In L. K. Muthén & B. O. Muthén (Eds.), Mplus user’s guide (pp. 459–502). Los Angeles, CA: Authors.
   Google Scholar
• National Research Council. (1996). National science education standards. Washington, DC: National Academies Press.
   Google Scholar
• National Research Council. (2000). Inquiry and the national science education standards: A guide for teaching and learning. Washington, DC: National Academies Press.
   Google Scholar
• National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.
   Google Scholar
• Nelson, B., & Ketelhut, D. (2008). Exploring embedded guidance and self-efficacy in educational multi-user virtual environments. International Journal of Computer-Supported Collaborative Learning, 3, 413–427. doi:10.1007/s11412-008-9049-1
   Web of Science ®Google Scholar
• NGSS Lead States. (2013). Next generation science standards: For states, by states (Vol. 1). Washington, DC: National Academies Press.
   Google Scholar
• Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66, 543–578. doi:10.3102/00346543066004543
   Web of Science ®Google Scholar
• Pajares, F., & Schunk, D. H. (2001). Self-beliefs and school success: Self-efficacy, self-concept, and school achievement. In R. Riding & S. Rayner (Eds.), Perception (pp. 239–266). London: Ablex.
   Google Scholar
• Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.
   Google Scholar
• Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.
   Google Scholar
• Redden, K. C., Simon, R. A., & Aulls, M. W. (2007). Alignment in constructivist-oriented teacher education: Identifying pre-service teacher characteristics and associated learning outcomes. Teacher Education Quarterly, 34, 149–164.
   Google Scholar
• Repko, A. F., & Szostak, R. (2017). Interdisciplinary research: Process and theory (3rd ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353–387. doi:10.1037/a0026838
   PubMed Web of Science ®Google Scholar
• Rosenberg, A. (2000). Philosophy of science (2nd ed.). New York, NY: Routledge.
   Google Scholar
• Saunders-Stewart, K. S., Gyles, P. D. T., & Shore, B. M. (2012). Student outcomes in inquiry instruction. Journal of Advanced Academics, 23, 5–31. doi:10.1177/1932202x11429860
   Google Scholar
• Saunders-Stewart, K. S., Gyles, P. D. T., Shore, B. M., Bracewell, R. J. (2015). Student outcomes in inquiry: Students' perspectives. Learning Environments Research, 18, 289–311. doi:10.1007/s10984-015-9185-2
   Google Scholar
• 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, 8, 23–74. Retrieved from http://www.dgps.de/fachgruppen/methoden/mpr-online/issue20/art2/mpr130_13.pdf.
   Google Scholar
• Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 35–53). New York, NY: Routledge.
   Google Scholar
• Schwartz, D. L., Bransford, J. D., & Sears, D. (2005). Efficiency and innovation in transfer. In J. P. Mestre (Ed.), Transfer of learning: From a modern multidisciplinary perspective (pp. 1–52). Greenwich, CT: Information Age.
   Google Scholar
• Seth, D., Ibrahim, A., & Tangorra, J. (2015). Measuring undergraduate students' self-efficacy in engineering design in a project-based design course. Proceedings of the IEEE Frontiers in Education 2015 conference, 1375–1382. Retrieved from http://fie2015.org/sites/file2015.fie-conference.org/files/FIE-2015_Proceedings_v11.pd
   Google Scholar
• Shore, B. M., Chichekian, T., Syer, C. A., Aulls, M. W., & Frederiksen, C. H. (2012). Planning, enactment, and reflection in inquir-based learning: Validating the McGill Strategic Demands of Inquiry Questionnaire. International Journal of Science and Mathematics Education, 10, 315–337. doi:10.1007/s10763-011-9301-4
   Web of Science ®Google Scholar
• Spronken-Smith, R., Walker, R., Batchelor, J., O’Steen, B., & Angelo, T. (2012). Evaluating student perceptions of learning processes and intended learning outcomes under inquiry approaches. Assessment and Evaluation in Higher Education, 37, 57–72. doi:10.1080/02602938.2010.496531
   Web of Science ®Google Scholar
• Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124, 240–261. doi:10.1037/0033-2909.124.2.240
   Web of Science ®Google Scholar
• Toulmin, S. (1972). Human understanding. Princeton, NJ: Princeton University Press.
   Google Scholar
• Watson, P. (2002). The role and integration of learning outcomes into the educational process. Active Learning in Higher Education, 3, 205–219. doi:10.1177/1469787402003003002
   Google Scholar
• West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York, NY: Guilford Press.
   Google Scholar
• Whitley, R. (1984). The intellectual and social organization of the sciences. New York, NY: Oxford University Press.
   Google Scholar
• Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. In M. E. Sobel & M. P. Becker (Eds.), Sociological methodology (pp. 165–200). Washington, DC: American Sociological Association.
   Google Scholar