Classroom Assessment and Instructional Modes: An Exploration of School-Level Contextualized Psychometric Challenges

References

• American Educational Research Association (AERA), American Psychological Association (APA), and National Council on Measurement in Education (NCME). (2014). Standards for educational and psychological testing.
   Google Scholar
• Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F. M. Lord and M. R. Novick’s (Eds.), Statistical theories of mental test scores (pp. 397–479). Addison-Wesley.
   Google Scholar
• Brennan, R. L. (Ed.). (2006). Educational measurement. (4th ed.) American Council on Education/Praeger.
   Google Scholar
• Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29. https://doi.org/10.18637/jss.v048.i06
   Web of Science ®Google Scholar
• Chen, M. Y., & Zumbo, B. D. (2017). Ecological framework of item responding as validity evidence: An application of multilevel DIF modeling using PISA data. In B. D. Zumbo & A. M. Hubley’s (Eds.), Understanding and investigating response processes in validation research. (53–68) Springer.
   Google Scholar
• Clauser, B., & Mazor, K. M. (2005). Using statistical procedures to identify differential item functioning test items. Educational Measurement: Issues and Practice, 17(1), 31–44. https://doi.org/10.1111/j.1745-3992.1998.tb00619.x
   Google Scholar
• Cope, B., & Kalantzis, M. (2016). Big Data comes to school: Implications for learning, assessment, and research. AERA Open, 2(2), 233285841664190. https://doi.org/10.1177/2332858416641907
   Web of Science ®Google Scholar
• DiCerbo, K. E., & Behrens, J. T. (2014). Impacts of the digital ocean on education. Pearson.
   Google Scholar
• Dixon-Román, E. (2020). A haunting logic psychometrics: Toward the speculative and indeterminacy of blackness in measurement. Educational Measurement: Issues and Practice, 39(3), 94–96. https://doi.org/10.1111/emip.12375
   Web of Science ®Google Scholar
• Drasgow, F. (1987). Study of the measurement bias of two standardized psychological tests. Journal of Applied Psychology, 72(1), 19–29. https://doi.org/10.1037/0021-9010.72.1.19
   Web of Science ®Google Scholar
• Drasgow, F., Levine, M. V., & Williams, E. A. (1985). Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38(1), 67–86. https://doi.org/10.1111/j.2044-8317.1985.tb00817.x
   Web of Science ®Google Scholar
• Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates.
   Google Scholar
• García, E., Weiss, E. (2020). COVID-19 and student performance, equity, and U.S. education policy: Lessons from pre-pandemic research to inform relief, recovery, and rebuilding (Report No. 205622). Economic Policy Institute. https://www.epi.org/publication/the-consequences-of-the-covid-19-pandemic-for-education-performance-and-equity-in-the-united-states-what-can-we-learn-from-pre-pandemic-research-to-inform-relief-recovery-and-rebuilding/
   Google Scholar
• Goldstein, H. (1983). Measuring changes in educational attainment over time: Problems and possibilities. Journal of Educational Measurement, 20(4), 369–377. https://doi.org/10.1111/j.1745-3984.1983.tb00214.x
   Web of Science ®Google Scholar
• Harris, D. (2020). Special section: Impact of COVID19 on educational measurement [Special issue]. Educational Measurement: Issues and Practice, 39(3), 7–7. (Ed.). https://doi.org/10.1111/emip.12392
   Web of Science ®Google Scholar
• Huggins-Manley, A. C., Beal, C. R., D’Mello, S. K., Leite, W. L., Cetin-Berber, D. D., Kim, D., & McNamara, D. S. (2019). A commentary on construct validity when using operational virtual learning environment data in effectiveness studies. Journal of Research on Educational Effectiveness, 12(4), 750–759. https://doi.org/10.1080/19345747.2019.1639869
   Web of Science ®Google Scholar
• Karabatsos, G. (2003). Comparing the aberrant response detection performance of thirty-six person-fit statistics. Applied Measurement in Education, 16(4), 277–298. https://doi.org/10.1207/S15324818AME1604_2
   Web of Science ®Google Scholar
• Lastinger Center for Learning, & University of Florida (2019). Algebra Nation. Retrieved 9/20/2019 from http://lastingercenter.com/portfolio/algebra-nation-2/
   Google Scholar
• Magis, D., Beland, S., Raiche, G. (2020). difR (R package). https://cran.r-project.org/web/packages/difR/difR.pdf.
   Google Scholar
• National Forum on Education Statistics (2015). Forum Guide to Elementary/Secondary Virtual Education Data. (NFES 2016-095). U.S. Department of EducationNational Center for Education Statistics.
   Google Scholar
• Paek, I., & Guo, H. (2011). Accuracy of DIF estimates and power in unbalanced designs using the mantel-haenszel DIF detection procedure. Applied Psychological Measurement, 35(7), 518–535. https://doi.org/10.1177/0146621611420559
   Web of Science ®Google Scholar
• Penfield, R. D., & Camilli, G. (2006). Differential item functioning and item bias. In C. R. Rao & S. Sinharay’s (Eds.) Handbook of Statistics., Vol. 6 (125–167) Elsevier.
   Google Scholar
• R Development Core Team (2020). R: A language and environment for statistical computing, reference index version 4.1.0. R Foundation for Statistical Computing. URL http://www.R-project.org.
   Google Scholar
• Randall, J. (2021). “Color-Neutral” is not a thing: Redefining construct definition and representation through a justice-oriented critical anti-racist lens. Educational Measurement: Issues and Practice, 40(online first). (4), 82–90. https://doi.org/10.1111/emip.12429
   Web of Science ®Google Scholar
• Rosnowski, M. (1987). Use of tests manifesting sex differences as measures of intelligence: Implications for measurement bias. Journal of Applied Psychology, 72, 480–483.
   Web of Science ®Google Scholar
• Sireci, S. (2021). NCME Presidential Address 2020: Valuing educational measurement. Educational Measurement: Issues and Practice, 40(1), 7–16. https://doi.org/10.1111/emip.12415
   Web of Science ®Google Scholar
• StataCorp (2021). Stata Statistical Software: Release 17. StataCorp LLC.
   Google Scholar
• U. S. Department of Education (2020a). (Notice of waiver approval for Florida Department of Education). Author. Retrieved May 26, 2021 from http://www.fldoe.org/core/fileparse.php/19861/urlt/FLFiscalWaiverResponse.pdf.
   Google Scholar
• U. S. Department of Education (2020b). Notice of waivers granted under Section 8401 of the Elementary and Secondary Education Act of 1965 (ESEA) (Notice 85 FR 29935; Document No. 2020-10740). Author. Retrieved May 26, 2021 from https://www.federalregister.gov/documents/2020/05/19/2020-10740/notice-of-waivers-granted-under-section-8401-of-the-elementary-and-secondary-education-act-of-1965.
   Google Scholar
• U. S. Department of Education, Office for Civil Rights, Education in a Pandemic: The Disparate Impacts of COVID-19 on America’s Students, 86 Fed.Reg. 7009, 7009 (June 9, 2021), https://www2.ed.gov/about/offices/list/ocr/docs/20210608-impacts-of-covid19.pdf.
   Google Scholar
• Vo, T. T., & French, B. F. (2021). An ecological framework for item responding within the context of a youth risk and needs assessment. Educational Measurement: Issues and Practice, 40(online first). (3), 64–72. https://doi.org/10.1111/emip.12426
   Web of Science ®Google Scholar
• von Davier, A. A., Deonovic, B., Yudelson, M., Polyak, S. T., & Woo, A. (2019). Computational psychometric approach to holistic learning and assessment systems. Frontiers in Education, 4, 1–12. https://doi.org/10.3389/feduc.2019.00069
   Google Scholar
• Xue, K., Huggins-Manley, A. C., & Leite, W. L. (2022). Semi-supervised learning method to adjust biased item difficulty estimates caused by nonignorable missingness in a virtual learning environment. Educational and Psychological Measurement, 82(3), 539–567. https://doi.org/10.1177/00131644211020494
   PubMed Web of Science ®Google Scholar
• Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4(2), 223–233. https://doi.org/10.1080/15434300701375832
   Google Scholar