References
- Al-Issa, A., & Sulieman, H. (2007). Student evaluations of teaching: Perceptions and biasing factors. Quality Assurance in Education, 15(3), 302–317. https://doi.org/https://doi.org/10.1108/09684880710773183
- American Educational Research Association [AERA], American Psychological Association [APA], & National Council on Measurement in Education [NCME]. (2014) . Standards for educational and psychological testing.
- Apkarian, N., Smith, W. M., Vroom, K., Voigt, M., Gehrtz, J., PtC Project Team, & SEMINAL Project Team. (2019). X-PIPS-M Survey Suite.Mathematical Association of America.
- Bostic, J. D., Carney, M., Casey, S., Engledowl, C., Folger, T., Gallagher, M., Howell, H., Smith, W., Tjoe, H., & Wilhelm, A. (2022, February). Choose your instruments wisely: Supporting mathematics teacher educators’ research and practice. Symposium presented at annual Association of Mathematics Teacher Educators Conference. Henderson, NV (AMTE).
- Bostic, J., & Krupa, E. (2021, October). Abstracts for Assessments: Describing a summary statement. In D. Olanoff, K. Johnson, & S. Spitzer (Eds)., Proceedings of the 43rd Annual Meeting of the North American chapter of the International Group for the Psychology of Mathematics Education. Philadelphia, PA (pp. 1854–1858). PME-NA.
- Braeken, J., & Assen, M. (2016). An Empirical Kaiser Criterion. Psychological Methods, 22(3), 450–466. https://doi.org/https://doi.org/10.1037/met0000074
- Bressoud, D., Mesa, V., & Rasmussen, C. (Eds.). (2015). Insights and recommendations from the MAA national study of college calculus. MAA Press.
- Carney, M., Bostic, J., Krupa, E., & Shih, J. (in press). Instruments and use statements for instruments in mathematics education. Journal for Research in Mathematics Education. Accepted for publication.
- Cattell, R. B. (1966). The Scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/https://doi.org/10.1207/s15327906mbr0102_10
- Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7). https://doi.org/https://doi.org/10.7275/jyj1-4868
- Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proc Nat Acad Sci U S A, 23, 8410–8415. https://doi.org/https://doi.org/10.1073/pnas.1319030111
- Goodchild, S., Apkarian, N., Rasmussen, C., & Katz, B. (2020). Critical stance within a community of inquiry in an advanced mathematics course for pre-service teachers. Journal of Mathematics Teacher Education, 24(3), 231–252. https://doi.org/https://doi.org/10.1007/s10857-020-09456-2
- Harper, F. K. (2019). A qualitative metasynthesis of teaching mathematics for social justice in action: Pitfalls and promises of practice. Journal for Research in Mathematics Education, 50(3), 268–310. https://doi.org/https://doi.org/10.5951/jresematheduc.50.3.0268
- Hattie, J. A.C. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York, NY: Routledge.
- Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/https://doi.org/10.1007/BF02291575
- Kogan, M., & Laursen, S. L. (2014). Assessing long-term effects of inquiry-based learning: A case study from college mathematics. Innovative Higher Education, 39(3), 183–199. https://doi.org/https://doi.org/10.1007/s10755-013-9269-9
- Krupa, E. E., Carney, M., & Bostic, J. (2019). Argument-based validation in practice: Examples from mathematics education. Applied Measurement in Education, 32(1), 1–9. https://doi.org/https://doi.org/10.1080/08957347.2018.1544139
- Laurson, S. L., & Rasmussen, C. (2019). I on the prize: Inquiry approaches in undergraduate mathematics. International Journal of Research in Undergraduate Mathematics Education, 5(1), 129–146. https://doi.org/https://doi.org/10.1007/s40753-019-00085-6
- Leyva, L. A., Quea, R., Weber, K., Battey, D., & López, D. (2020). Detailing racialized and gendered mechanisms of undergraduate precalculus and calculus classroom instruction. Cognition and Instruction, 39(1), 1–34. https://doi.org/https://doi.org/10.1080/07370008.2020.1849218
- Lotan, R. (2003). Group-worthy tasks. Educational Leadership, 60(6), 72–75.
- Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for Research in Mathematics Education, 28(1), 26–47. https://doi.org/https://doi.org/10.2307/749662
- Ma, X., & Xu, J. (2004). Determining the causal ordering between attitude toward mathematics and achievement in mathematics. American Journal of Education, 110(3), 256–280. https://doi.org/https://doi.org/10.1086/383074
- Maciejewski, W. (2016). Instructors’ perceptions of their students’ conceptions: The case in undergraduate mathematics. International Journal of Teaching and Learning in Higher Education, 28(1), 1–8.
- MacNell, L., Driscoll, A., & Hunt, A. N. (2015). What’s in a name: Exposing gender bias in student ratings of teaching. Innovative Higher Education, 40(4), 291–303. https://doi.org/https://doi.org/10.1007/s10755-014-9313-4
- Malhotra, N. K., & Dash, S. (2011). Marketing Research an Applied Orientation. London: Pearson Publishing.
- Martin, D. B., Anderson, C. R., & Shah, N. (2017). Race and mathematics education. In J. Cai (Ed.), Compendium for Research in Mathematics Education (pp. 607–636). National Council of Teachers of Mathematics.
- Merritt, D. (2008). Bias, the brain, and student evaluations of teaching. St. John’s Law Review, 82(1), 235–287.
- Miller, E. R. (2020). Analyzing the cognitive demand of enacted examples in precalculus: A comparative case study of graduate student instructors. Journal of Mathematics and Science: Collaborative Explorations, 16(1), 18. https://doi.org/https://doi.org/10.25891/dy91-tx93
- Patrick, C. L. (2011). Student evaluations of teaching: Effects of the big five personality traits, grades, and the validity hypothesis. Assessment & Evaluation in Higher Education, 36(2), 239–249. https://doi.org/https://doi.org/10.1080/02602930903308258
- Rasmussen, C., & Kwon, O.N. (2007). An inquiry-oriented approach to undergraduate mathematics. The Journal of Mathematical Behavior, 26, 189–194. https://doi.org/https://doi.org/10.1016/j.jmathb.2007.10.001
- Reinholz, D. L., Rasmussen, C., & Nardi, E. (2020). Time for (research on) change in mathematics departments. International Journal of Research in Undergraduate Mathematics Education, 6(2), 147–158. https://doi.org/https://doi.org/10.1007/s40753-020-00116-7
- Revelle, W. (2020). Procedures for Psychological, Psychometric, and Personality Research. CRAN. https://cran.r-project.org/web//packages/psych/psych.pdf
- Rönkkö, M., & Cho, E. (2020). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1), 6–47. https://doi.org/https://doi.org/10.1177/1094428120968614
- Rosseel, Y. (2012). lavaan: An r package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/https://doi.org/10.18637/jss.v048.i02
- Schoenfeld, A. H. (1995). A brief biography of calculus reform. UME Trends: News and Reports on Undergraduate Mathematics Education, 6(6), 3–5.
- Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling: Fourth Edition (4th ed.). Routledge.
- Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Westview Press.
- Stains, M., Harshman, J., Barker, M., Chasteen, S., Cole, R. S., DeChenne-Peters,S., Eagan, M. K., Jr., Esson, J. M., Knight, J. K., Laski, F. A., Levis-Fitzgerald, M., Lee, C. J., Lo, S. M., McDonnell, L. M., McKay, T. A., Michelotti, N., Musgrove, A., Palmer, M. S., Plank, K. M., and Young, A. M. (2018). Anatomy of STEM teaching in North American universities. Science, 359(6383), 1468–1470. https://doi.org/https://doi.org/10.1126/science.aap8892
- Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/https://doi.org/10.5116/ijme.4dfb.8dfd
- Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., 2nd, Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., and Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proc Nat Acad Sci U S A, 117, 6476–6483. https://doi.org/https://doi.org/10.1073/pnas.1916903117
- Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.
- Uhing, K., Hass, M., Voigt, M., Ström, A., & Calleros, E. (2021). Students’ experiences with active learning mathematics. In W. M. Smith, M. Voigt, A. Ström, D. C. Webb, & W. G. Martin (Eds.), Transformational change efforts: student engagement in mathematics through an institutional network for active learning (pp. 221–241). MAA/AMS Press.
- Walter, E. M., Henderson, C. R., Beach, A. L., & Williams, C. T. (2016). Introducing the postsecondary instructional practices survey (PIPS): A concise, interdisciplinary, and easy-to-score survey of postsecondary instructional practices. CBE Life Sciences Education, 15(ar53), 1–11. https://doi.org/https://doi.org/10.1187/cbe.15-09-0193
- Watkins, M. (2018). Exploratory factor analysis: a guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/https://doi.org/10.1177/0095798418771807
- Young, S., Rush, L., & Shaw, D. (2009). Evaluating gender bias in ratings of university instructors’ teaching effectiveness. International Journal of Scholarship of Teaching and Learning, 3(2), 1–14. https://doi.org/https://doi.org/10.20429/ijsotl.2009.030219