References
- Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public high schools. Journal of Labor Economics, 25, 95–135. doi:10.1086/508733
- American Educational Research Association (AERA). (2015). AERA statement on use of value-added models (VAM) for the evaluation of educators and educator preparation programs. Washington, DC. Retrieved from http://edr.sagepub.com/content/early/2015/11/10/0013189X15618385.full.pdf+html
- American Statistical Association (ASA). (2014). ASA statement on using value-added models for educational assessment. Alexandria, VA. Retrieved from https://www.amstat.org/policy/pdfs/ASA_VAM_Statement.pdf
- Amrein-Beardsley, A. (2014). Rethinking value-added models in education: Critical perspectives on tests and assessment-based accountability. New York, NY: Routledge.
- Au, W. (2011, Winter). Neither fair nor accurate: Research-based reasons why high-stakes tests should not be used to evaluate teachers. Rethinking Schools. Retrieved from http://www.rethinkingschools.org/archive/25_02/25_02_au.shtml
- Baker, B. D. (2012, March 31). Firing teachers based on bad (VAM) versus wrong (SGP) measures of effectiveness: Legal note. School Finance 101. Retrieved from http://schoolfinance101.wordpress.com/2012/03/31/firing-teachers-based-on-bad-vam-versus-wrong-sgp-measures-of-effectiveness-legal-note
- Baker, B. D., Oluwole, J. O., & Green, P. C. (2013). The legal consequences of mandating high stakes decisions based on low quality information: Teacher evaluation in the Race-to-the-Top era. Education Policy Analysis Archives, 21(5), 1–71. Retrieved from http://epaa.asu.edu/ojs/article/view/1298
- Ballou, D., & Springer, M. G. (2015). Using student test scores to measure teacher performance: Some problems in the design and implementation of evaluation systems. Educational Researcher, 44(2), 77–86. doi:10.3102/0013189X15574904
- Barnett, J., & Wills, K. (2015). Measuring and assessing classroom instruction: Properties of TAP observational rubric. Santa Monica, CA: National Institute for Excellence in Teaching. Retrieved from http://tapinminnesota.org/wp-content/uploads/2015/08/060415-SKR-Stability_Final.pdf
- Betebenner, D. W. (2009). Norm- and criterion-referenced student growth. Education Measurement: Issues and Practice, 28(4), 42–51. doi:10.1111/j.1745-3992.2009.00161.x
- Betebenner, D. W. (2016, January). Package ‘SGP’. Retrieved from https://cran.r-project.org/web/packages/SGP/SGP.pdf
- Bill & Melinda Gates Foundation. (2013, January 8). Ensuring fair and reliable measures of effective teaching: Culminating findings from the MET project’s three-year study. Seattle, WA. Retrieved from http://www.gatesfoundation.org/press-releases/Pages/MET-Announcment.aspx
- Bloom, H. S., Hill, C. J., Black, A. R., & Lipsey, M. W. (2008). Performance trajectories and performance gaps achievement effect-size benchmarks for educational interventions. Journal of Research on Educational Effectiveness, 1, 289–328. doi:10.1080/19345740802400072
- Braun, H., Goldschmidt, P., McCaffrey, D., & Lissitz, R. (2012). Graduate student council division D fireside chat: VA modeling in educational research and evaluation. Paper Presented at Annual Conference of the American Educational Research Association (AERA), Vancouver, Canada.
- Brennan, R. L. (2006). Perspectives on the evolution and future of educational measurement. In R. L. Brennan (Ed.), 2006. Educational measurement (4th ed., pp. 1–16). Westport, CT: American Council on Education/Praeger.
- Briggs, D. C., & Betebenner, D. (2009). Is growth in student achievement scale dependent? Paper presented at the annual meeting of the National Council for Measurement in Education (NCME), San Diego, CA.
- Broatch, J., & Lohr, S. (2012). Multidimensional assessment of value added by teachers to real-world outcomes. Journal of Educational and Behavioral Statistics, 37(2), 256–277. doi:10.3102/1076998610396900
- Castellano, K. E., & Ho, A. D. (2013a). Contrasting OLS and quantile regression approaches to student “growth” percentiles. Journal of Educational and Behavioral Statistics, 38(2), 190–215. doi:10.3102/1076998611435413
- Castellano, K. E., & Ho, A. D. (2013b). A practitioner’s guide to growth models. Washington, DC: Council of Chief State School Officers.
- Castellano, K. E., & Ho, A. D. (2015). Practical differences among aggregate-level conditional status metrics: From median student growth percentiles to value-added models. Journal of Educational and Behavioral Statistics, 40(1), 35–68. doi:10.3102/1076998614548485
- Castellano, K. E., & McCaffrey, D. F. (2017). The accuracy of aggregate student growth percentiles as indicators of educator performance. Educational Measurement: Issues and Practice, 36(1), 14–27. doi:10.1111/emip.12144
- Cody, C. A., McFarland, J., Moore, J. E., & Preston, J. (2010, August). The evolution of growth models. Public Schools of North Carolina. Raleigh, NC. Retrieved from http://www.dpi.state.nc.us/docs/intern-research/reports/growth.pdf
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
- Croft, M., Glazerman, S., Goldhaber, D., Loeb, S., Raudenbush, S., Staiger, D., & Whitehurst, G. J. (2011). Passing muster: Evaluating teacher evaluation system. Washington, D.C.: Brookings Institution. Retrieved from http://www.brookings.edu/research/reports/2011/04/26-evaluating-teachers
- Curtis, R. (2011). District of Columbia public schools: Defining instructional expectations and aligning accountability and support. Washington, D.C.: The Aspen Institute. Retrieved from www.nctq.org/docs/Impact_1_15579.pdf
- Daley, G., & Kim, L. (2010). A teacher evaluation system that works. Santa Monica, CA: Institute for Excellence in Teaching. Retrieved from http://files.eric.ed.gov/fulltext/ED533380.pdf
- Di Carlo, M. (2013, January 17). A few points about the instability of value-added estimates. The Shanker Blog. Retrieved from http://shankerblog.org/?p=7446
- Ehlert, M., Koedel, C., Parsons, E., & Podgursky, M. (2012, August). Selecting growth measures for school and teacher evaluations. Washington, D.C: National Center for Analysis of Longitudinal Data in Education Research (CALDER). Retrieved from www.caldercenter.org/publications/upload/WP-80.pdf
- Every Student Succeeds Act of 2015, P.L. 114–95, 20 U.S.C. § 6301 (2015).
- Glazerman, S. M., & Potamites, L. (2011, December). False performance gains: A critique of successive cohort indicators. Mathematica Policy Research. Retrieved from www.mathematica-mpr.com/publications/pdfs/…/False_Perf.pdf
- Goldhaber, D., & Hansen, M. (2010). Is it just a bad class? Assessing the stability of measured teacher performance. Seattle, WA: Center for Education & Data Research (CEDR) Working Paper 2010-3. Retrieved from http://www.cedr.us/publications.html
- Goldhaber, D., & Theobald, R. (2012). Do different value-added models tell us the same things? Stanford, CA: Carnegie Knowledge Network. Retrieved from http://www.carnegieknowledgenetwork.org/briefs/value-added/different-growth-models/
- Goldhaber, D., Walch, J., & Gabele, B. (2014). Does the model matter? Exploring the relationship between different student achievement-based teacher assessments. Statistics and Public Policy, 1(1), 28–39. doi:10.1080/2330443X.2013.856169
- Goldring, E., Grissom, J., Rubin, M., Neumerski, C., Cannata, M., Drake, T., & Schuermann, P. (2015). Make room value-added: Principal’s human capital decisions and the emergence of teacher observational data. Educational Researcher, 44(2), 96–104. doi:10.3102/0013189x15575031
- Goldschmidt, P., Choi, K., & Beaudoin, J. B. (2012, February). Growth model comparison study: Practical implications of alternative models for evaluating school performance. Technical Issues in Large-Scale Assessment State Collaborative on Assessment and Student Standards. Council of Chief State School Officers.
- Good, T. L. (2014). What do we know about how teachers influence student performance on standardized tests: And why do we know so little about other student outcomes? Teachers College Record. Retrieved from http://www.tcrecord.org/Content.asp?ContentId=17289
- Grossman, P., Cohen, J., Ronfeldt, M., & Brown, L. (2014). The test matters: The relationship between classroom observation scores and teacher value added on multiple types of assessment. Educational Researcher, 43(6), 293–303. doi:10.3102/0013189X14544542
- Guarino, C., Reckase, M., Stacy, B., & Wooldridge, J. (2015). A comparison of student growth percentile and value-added models of teacher performance. Statistics and Public Policy, 2(1), e1034820–1. doi:10.1080/2330443X.2015.1034820
- Hanges, P., Schneider, B., & Niles, K. (1990). Stability of performance: An interactionist perspective. Journal of Applied Psychology, 75, 658–667. doi:10.1037/0021-9010.75.6.658
- Harris, D. N. (2011). Value-added measures in education: What every educator needs to know. Cambridge, MA: Harvard Education Press.
- Harris, D. N. (2012). How do teacher value-added indicators compare to other measures of teacher effectiveness? Stanford, CA: Carnegie Knowledge Network. Retrieved from htpp://www.carnegieknowledgenetwork.org/briefs/value-added/value-added-other-measures/
- Harris, D. N., & Herrington, C. D. (2015). Editors’ introduction: The use of teacher value-added measures in schools: New evidence, unanswered questions, and future prospects.
- Harris, D. N., Ingle, W. K., & Rutledge, S. A. (2014). How teacher evaluation methods matter for accountability: A comparative analysis of teacher effectiveness ratings by principals and teacher value-added measures. American Educational Research Journal, 51, 73–112. doi:10.3102/0002831213517130
- Hill, H. C. (2009). Evaluating value-added models: A validity argument approach. Journal of Policy Analysis and Management, 28(4), 700–709. doi:10.1002/pam.20463
- Hill, H. C., Kapitula, L., & Umlan, K. (2011, June). A validity argument approach to evaluating teacher value-added scores. American Educational Research Journal, 48(3), 794–831. doi:10.3102/0002831210387916
- Ho, A. D., Lewis, D. M., & Farris, J. L. (2009). The dependence of growth-model results on proficiency cut scores. Educational Measurement: Issues and Practice, 28(4), 15–26. doi:10.1111/j.1745-3992.2009.00159.x
- Hofmann, D., Jacobs, R., & Gerras, S. (1992). Mapping individual performance over time. Journal of Applied Psychology, 77, 185–195. doi:10.1037/0021-9010.77.2.185
- Ishii, J., & Rivkin, S. G. (2009). Impediments to the estimation of teacher value added. Education Finance and Policy, 4, 520–536. doi:10.1162/edfp.2009.4.4.520
- Jerald, C. D., & Van Hook, K. (2011). More than measurement: The TAP System’s lessons learned for designing better teacher evaluation systems. Santa Monica, CA: National Institute for Excellence in Teaching. Retrieved from http://www.tapsystem.org/publications/eval_lessons.pdf
- Joint American Educational Research Association. (2000). The standards for educational and psychological testing. American Educational Research Association, Washington, DC.
- Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. doi:10.1111/jedm.12000
- Kane, T., & Staiger, D. (2012). Gathering feedback for teaching: Combining high-quality observations with student surveys and achievement gains. Seattle, WA: Bill & Melinda Gates Foundation. Retrieved from http://www.metproject.org/downloads/MET_Gathering_Feedback_Practioner_Brief.pdf
- Kennedy, M. M. (2010, November). Attribution error and the quest for teacher quality. Educational Researcher, 39(8), 591–598. doi:10.3102/0013189X10390804
- Kersting, N. B., Chen, M., & Stigler, J. W. (2013). Value-added added teacher estimates as part of teacher evaluations: Exploring the effects of data and model specifications on the stability of teacher value-added scores. Education Policy Analysis Archives, 21(7), 1–39. Retrieved from http://epaa.asu.edu/ojs/article/view/1167
- Kimball, S. M., White, B., Milanowski, A. T., & Borman, G. (2004). Examining the relationship between teacher evaluation and student assessment results in Washoe County. Peabody Journal of Education, 79(4), 54–78. doi:10.1207/s15327930pje7904_4
- Koedel, C., & Betts, J. R. (2007, April). Re-examining the role of teacher quality in the educational production function. Working Paper No. 2007-03. Nashville, TN: National Center on Performance Initiatives. Retrieved from https://economics.missouri.edu/working-papers/2007/wp0708_koedel.pdf
- Koedel, C., Mihaly, K., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180–195. doi:10.1016/j.econedurev.2015.01.006
- Lash, A., Makkonen, R., Tran, L., & Huang, M. (2016). Analysis of the stability of teacher-level growth scores from the student growth percentile model (REL 2016–104). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory West. Retrieved from http://ies.ed.gov/ncee/edlabs
- Lockwood, J. R., & Castellano, K. E. (2015). Alternative statistical frameworks for student growth percentile estimation. Statistics and Public Policy, 2(1), 1–9. doi:10.1080/2330443X.2014.962718
- Lockwood, J. R., & McCaffrey, D. F. (2009). Exploring student-teacher interactions in longitudinal achievement data. Education Finance and Policy, 4(4), 439–467. doi:10.1162/edfp.2009.4.4.439
- Lozier, C. (2012, July 18). What the PGA can teach us about value-added modeling. Getting Smart. Retrieved from http://gettingsmart.com/blog/2012/07/what-pga-can-teach-us-about-value-added-modeling/
- Martinez, J. F., Schweig, J., & Goldschmidt, P. (2016). Approaches for combining multiple measures of teacher performance: Reliability, validity, and implications for evaluation policy. Educational Evaluation and Policy Analysis, 38(4), 738–756. doi:10.3102/0162373716666166
- McCaffrey, D. F., Castellano, K. E., & Lockwood, J. R. (2015). The impact of measurement error on the accuracy of individual and aggregate SGP. Educational Measurement: Issues and Practice, 34(1), 15–21. doi:10.1111/emip.12062
- McCaffrey, D. F., Sass, T., Lockwood, J., & Mihaly, K. (2009). The intertemporal variability of teacher effect estimates. Education Finance and Policy, 4(4), 572–606. doi:10.1162/edfp.2009.4.4.572
- Measures of Effective Teaching (MET) Project. (2013). Ensuring fair and reliable measures of effective teaching. Non-technical research brief. Seattle, WA: Bill & Melinda Gates Foundation. Retrieved from http://metproject.org/downloads/MET_Ensuring_Fair_and_Reliable_Measures_Practitioner_Brief.pdf
- Merrigan, G., & Huston, C. L. (2004). Communication research methods. Wadsworth Publishing Company.
- Messick, S. (1975). The standard problem: Meaning and values in measurement and evaluation. American Psychologist, 30, 955–966. doi:10.1037//0003-066x.30.10.955
- Monroe, S., & Cai, L. (2015). Examining the reliability of student growth percentiles using multidimensional IRT. Educational Measurement: Issues and Practice, 34, 21–30. doi:10.1111/emip.12092
- National Governors Association Center for Best Practices, & Council of Chief State School Officers. (2010). Common Core State Standards for mathematics: Kindergarten introduction. Retrieved from http://www.corestandards.org/Math/Content/K/introduction
- National Institute for Excellence in Teaching (NIET). (2010). TAP System CORE Training. Washington, D.C.: National Institute for Excellence in Teaching. Retrieved from http://www.tapsystemtraining.org/
- National Institute for Excellence in Teaching (NIET). (n.d.a). Elements of success. Washington, D.C. Retrieved from http://www.niet.org/tap-system/elements-of-success/
- National Institute for Excellence in Teaching. (2011). TAP: The system for teacher and student advancement. Santa Monica, CA: National Institute for Excellence in Teaching. Retrieved from http://tapsystem.org/
- National Institute for Excellence in Teaching (NIET). (n.d.b). FAQs. Washington, D.C. Retrieved from http://www.niet.org/tap-system/faq/
- National Institute for Excellence in Teaching (NIET). (n.d.c). NIET Impact Overview. Washington, D.C. Retrieved from http://www.niet.org/our-impact/niet-impact-overview/
- Newton, X., Darling-Hammond, L., Haertel, E., & Thomas, E. (2010). Value-added modeling of teacher effectiveness: An exploration of stability across models and contexts. Educational Policy Analysis Archives, 18(23), 1–27. Retrieved from http://epaa.asu.edu/ojs/article/view/810
- No Child Left Behind Act of 2001, P.L. 107-110, 20 U.S.C. § 6319 (2002).
- Papay, J. P. (2010). Different tests, different answers: The stability of teacher value-added estimates across outcome measures. American Educational Research Journal. doi:10.3102/0002831210362589
- Polikoff, M. S., & Porter, A. C. (2014). Instructional alignment as a measure of teaching quality. Educational Evaluation and Policy Analysis. doi:10.3102/0162373714531851
- Race to the Top (RttT) Act of 2011, S. 844–112th Congress. (2011). Retrieved from http://www.govtrack.us/congress/bills/112/s844
- Rothstein, J. (2009). Student sorting and bias in value-added estimation: Selection on observables and unobservables. Education Finance and Policy, 4(4), 537–571. doi:10.1162/edfp.2009.4.4.537
- Rothstein, J., & Mathis, W. J. (2013, January). Review of two culminating reports from the MET Project. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/thinktank/review-MET-final-2013
- Sass, T. R., Hannaway, J., Xu, Z., Figlio, D. N., & Feng, L. (2012). Value added of teachers in high-poverty schools and lower poverty schools. Journal of Urban Economics, 72(2–3), 104–122.
- Sass, T., & Harris, D. (2012). Skills, productivity and the evaluation of teacher performance. W. Andrew Young School of Policy Studies Research Paper Series No. 12-11. Atlanta, GA: Georgia State University. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2020717
- Sass, T., Semykina, A., & Harris, D. (2014). Value-added models and the measurement of teacher productivity. Economics of Education Review, 38, 9–23. doi:10.1016/j.econedurev.2013.10.003
- Sass, T. R. (2008). The stability of value-added measures of teacher quality and implications for teacher compensation policy. Washington, D.C.: National Center for Analysis of Longitudinal Data in Education Research (CALDER). Retrieved from www.urban.org/UploadedPDF/1001266_stabilityofvalue.pdf
- Schochet, P. Z., & Chiang, H. S. (2010). Error rates in measuring teacher and school performance based on student test score gains. Washington, DC: Institute of Education Sciences. Retrieved from http://ies.ed.gov/ncee/pubs/20104004/pdf/20104004.pdf
- Schochet, P. Z., & Chiang, H. S. (2013). What are error rates for classifying teacher and school performance using value-added models? Journal of Educational and Behavioral Statistics, 38, 142–171. doi:10.3102/1076998611432174
- Strauss, V. (2015). Gates Foundation puts millions of dollars into new education focus: Teacher preparation. The Washington Post. Retrieved from https://www.washingtonpost.com/news/answer-sheet/wp/2015/11/23/gates-foundation-put-millions-of-dollars-into-new-education-focus-teacher-preparation/
- Strunk, K. O., Weinstein, T. L., & Makkonen, R. (2014). Sorting out the signal: Do multiple measures of teachers’ effectiveness provide consistent information to teachers and principals? Education Policy Analysis Archives, 22, 100. Retrieved from http://epaa.asu.edu/ojs/article/view/1590
- Toth, H. (2015). College of education wins $7 million grant for teacher prep reform. Lubbock, TX: Texas Tech University. Retrieved from http://today.ttu.edu/posts/2015/11/college-of-education-wins–gates-foundation-grant-for-teacher-prep-reform
- Walsh, E., & Isenberg, E. (2015). How does value-added compare to student growth percentiles? Statistics and Public Policy, 2(1), e1034390. doi:10.1080/2330443X.2015.1034390
- Walsh, K., Joseph, N., Lakis, K., & Lubell, S. (2017). Running in place: How new teacher evaluations fail to live up to promises. Washington, DC: National Council on Teacher Quality (NCTQ). Retrieved from http://www.nctq.org/dmsView/Final_Evaluation_Paper
- Will, M. (2017). States are all over the map when it comes to how they’re looking to approach teacher-evaluation systems under ESSA. Education Week. Retrieved from http://www.edweek.org/ew/articles/2017/01/04/assessing-quality-of-teaching-staff-still-complex.html?intc=EW-QC17-TOC&_ga=1.138540723.1051944855.1481128421
- Wright, S. P. (2010). An investigation of two nonparametric regression models for value-added assessment in education. Cary, NC: SAS Institute.
- Wright, S. P. (2015). Educational value-added analysis of covariance models with error in the covariates. In R. Lissitz & H. Jiao (Eds.), Value added modeling and growth modeling with particular application to teacher and school effectiveness. Charlotte, NC: Information Age Publishing.
- Wright, S. P., White, J. T., Sanders, W. L., & Rivers, J. C. (2010). SAS® EVAAS® statistical models. SAS white paper. Cary, NC: SAS Institute. Retrieved from http://www.sas.com/resources/asset/SAS-EVAAS-Statistical-Models.pdf