To determine the efficacy of the use of data from student test scores, particularly in the form of Value-Added Measures (VAMs), to evaluate and to make key personnel decisions about classroom teachers.
Currently, a number of states either are adopting or have adopted new or revamped teacher evaluation systems, which are based in part on data from student test scores in the form of value-added measures (VAM). Some states mandate that up to 50% of the teacher evaluation must be based on data from student test scores. States and school districts are using the evaluation systems to make key personnel decisions about retention, dismissal, and compensation of teachers and principals.
At the same time, states have also adopted and are implementing new, more rigorous college and career readiness standards. These new standards are intended to raise the bar from having every student earn a high school diploma to the much more ambitious goal of having every student be on-target for success in postsecondary education and training.
The assessments accompanying these new standards depart from the old, much less expensive, multiple-choice style tests to assessments, which include constructed responses. These new assessments demand higher-order thinking and up to a two-year increase in expected reading and writing skills. Not surprisingly, the newness of the assessments combined with increased rigor has resulted in significant drops in the number of students reaching “proficient” levels on assessments aligned to the new standards.
Herein lies the challenge for principals and school leaders. New teacher evaluation systems demand the inclusion of student data at a time when scores on new assessments are dropping. The fears accompanying any new evaluation system have been magnified by the inclusion of data that will get worse before it gets better. Principals are concerned that the new evaluation systems are eroding trust and are detrimental to building a culture of collaboration and continuous improvement necessary to successfully raise student performance to college and career-ready levels.
Specific questions have arisen about using value-added measurement (VAM) to retain, dismiss, and compensate teachers. VAMs are statistical measures of student growth. They employ complex algorithms to figure out how much teachers contribute to their students’ learning, holding constant factors such as demographics. And so, at first glance, it would appear reasonable to use VAMs to gauge teacher effectiveness. Unfortunately, policymakers have acted on that impression over the consistent objections of researchers who have cautioned against this inappropriate use of VAM.
In a 2014 report, the American Statistical Association urged states and school districts against using VAM systems to make personnel decisions. A statement accompanying the report pointed out the following:
- “VAMs are generally based on standardized test scores, and do not directly measure potential teacher contributions toward other student outcomes.
- VAMs typically measure correlation, not causation: Effects – positive or negative – attributed to a teacher may actually be caused by other factors that are not captured in the model.
- Under some conditions, VAM scores and rankings can change substantially when a different model or test is used, and a thorough analysis should be undertaken to evaluate the sensitivity of estimates to different models.
- VAMs should be viewed within the context of quality improvement, which distinguishes aspects of quality that can be attributed to the system from those that can be attributed to individual teachers, teacher preparation programs, or schools.
- Most VAM studies find that teachers account for about 1% to 14% of the variability in test scores, and that the majority of opportunities for quality improvement are found in the system-level conditions. Ranking teachers by their VAM scores can have unintended consequences that reduce quality.”
Another peer-reviewed study funded by the Gates Foundation and published by the American Educational Research Association (AERA) stated emphatically, “Value-Added Performance Measures Do Not Reflect the Content or Quality of Teachers’ Instruction.” The study found that “state tests and these measures of evaluating teachers don’t really seem to be associated with the things we think of as defining good teaching.” It further found that some teachers who were highly rated on student surveys, in classroom observations by principals, and through other indicators of quality had students who scored poorly on tests. The opposite also was true. “We need to slow down or ease off completely for the stakes for teachers, at least in the first few years, so we can get a sense of what do these things measure, what does it mean,” the researchers admonished. “We’re moving these systems forward way ahead of the science in terms of the quality of the measures.”
Researcher Bruce Baker cautions against using VAMs even when test scores count less than 50% of a teacher’s final evaluation. Using VAM estimates in a parallel weighting system with other measures like student surveys and principal observations “requires that VAM be considered even in the presence of a likely false positive. NY legislation prohibits a teacher from being rated highly if their test-based effectiveness estimate is low. Further, where VAM estimates vary more than other components, they will quite often be the tipping point—nearly 100% of the decision even if only 20% of the weight.”
Stanford’s Edward Haertel takes the objection for using VAMs for personnel decisions one step further: “Teacher VAM scores should emphatically not be included as a substantial factor with a fixed weight in consequential teacher personnel decisions. The information they provide is simply not good enough to use in that way. It is not just that the information is noisy. Much more serious is the fact that the scores may be systematically biased for some teachers and against others, and major potential sources of bias stem from the way our school system is organized. No statistical manipulation can assure fair comparisons of teachers working in very different schools, with very different students, under very different conditions.”
Still other researchers believe that VAM is flawed at its very foundation. Linda Darling-Hammond et al. point out that the use of test scores via VAMs assumes “that student learning is measured by a given test, is influenced by the teacher alone, and is independent from the growth of classmates and other aspects of the classroom context. None of these assumptions is well supported by current evidence.” Other factors including class size, instructional time, home support, peer culture, and summer learning loss impact student achievement. Darling-Hammond points out that VAMs are inconsistent from class to class and year to year. VAMs are based on the false assumption that students are randomly assigned to teachers. VAMs cannot account for the fact that “some teachers may be more effective at some forms of instruction…and less effective in others.”
NASSP Guiding Principles
- As instructional leader, “the principal’s role is to lead the school’s teachers in a process of learning to improve teaching, while learning alongside them about what works and what doesn’t.”
- The teacher evaluation system should aid the principal in creating a collaborative culture of continuous learning and incremental improvement in teaching and learning.
- Assessment for learning is critical to continuous improvement of teachers.
- Data from student test scores should be used by schools to move students to mastery and a deep conceptual understanding of key concepts as well as to inform instruction, target remediation, and to focus review efforts.
- NASSP supports recommendations for the use of “multiple measures” to evaluate teachers as indicated in the 2014 “Standards for Educational and Psychological Testing” measurement standards released by leading professional organizations in the area of educational measurement, including the American Educational Research Association (AERA), American Psychological Association (APA), and National Council on Measurement in Education (NCME).
- Successful teacher evaluation systems should employ “multiple classroom observations across the year by expert evaluators looking to multiple sources of data, and provide meaningful feedback to teachers.”
- Districts and states should encourage the use of Peer Assistance and Review (PAR) programs, which use expert mentor teachers supporting novice teachers and struggling veteran teachers, and which have been proven to be an effective system for improving instruction.
- States and districts should allow the use of teacher-constructed portfolios of student learning, which are being successfully used as a part of teacher evaluation systems in a number of jurisdictions.
- VAMs should be used by principals to measure school improvement and to determine the effectiveness of programs and instructional methods.
- VAMs should be used by principals to target professional development initiatives.
- VAMs should not be used to make key personnel decisions about individual teachers.
- States and districts should provide ongoing training for principals in the appropriate use of student data and VAMs.
- States and districts should make student data and VAMs available to principals at a time when decisions about school programs are being made.
- States and districts should provide the resources and time principals need in order to determine the best use of data.
- American Statistical Association (2014). ASA statement on using value-added models for educational assessment. Alexandria, VA. Retrieved from: http://vamboozled.com/wp-content/uploads/2014/03/ASA_VAM_Statement.pdf
- Amrein-Beardsley, A. (2008). Methodological concerns about the Education Value-Added Assessment System (EVAAS). Educational Researcher, 37(2), 65-75. doi: 10.3102/0013189X08316420.
- Amrein-Beardsley, A. (2009). Buyers be-aware: What you don’t know can hurt you. Educational Leadership, 67(3), 38-42.
- Amrein-Beardsley, A. (2012). Value-added measures in education: The best of the alternatives is simply not good enough \[Commentary\]. Teachers College Record. Retrieved from http://www.tcrecord.org/content.asp?contentid=16648
- Amrein-Beardsley, A. (2014). Rethinking value-added models in education: Critical perspectives on tests and assessment-based accountability. New York, NY: Routledge. *To order please visit Routledge here and use the following 20% off discount code at checkout: IRK69.
- Amrein-Beardsley, A., & Barnett, J. H. (2012). Working with error and uncertainty to increase measurement validity. Educational Assessment, Evaluation and Accountability, 1-11. doi: 10.1007/s11092-012-9146-6
- Amrein-Beardsley, A., & Collins, C. (2012). The SAS Education Value-Added Assessment System (SAS® EVAAS®) in the Houston Independent School District (HISD): Intended and unintended consequences. Education Policy Analysis Archives, 20(12), 1-36. Retrieved from http://epaa.asu.edu/ojs/article/view/1096
- Au, W. (2011). 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, E. L., Barton, P. E., Darling-Hammond, L., Haertel, E., Ladd, H. F., Linn, R. L., Ravitch, D., Rothstein, R., Shavelson, R. J., & Shepard, L. A. (2010). Problems with the use of student test scores to evaluate teachers. Washington, D.C.: Economic Policy Institute. Retrieved from http://www.epi.org/publications/entry/bp278
- 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. (2012). Review of “The long-term impacts of teachers: Teacher value-added and student outcomes in adulthood.” \[Review of the report The long-term impacts of teachers: Teacher value-added and student outcomes in adulthood, by R. Chetty, J Friedman, & J. Rockoff\]. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/thinktank/review-long-term-impacts.
- Berliner, D. C. (2014). Exogenous variables and value-added assessments: A fatal flaw. Teachers College Record, 116(1). Retrieved from http://www.tcrecord.org/content.asp?contentid=17293 See also the full article here.
- Bracey, G. W. (2004a). Serious questions about the Tennessee Value-Added Assessment System. Phi Delta Kappan, 85(9), 716-717.
- Bracey, G. W. (2004b). Value-added assessment findings: Poor kids get poor teachers. Phi Delta Kappan, 86(4), 331-333.
- Briggs, D. & Domingue, B. (2011). Due diligence and the evaluation of teachers: A review of the value-added analysis underlying the effectiveness rankings of Los Angeles Unified School District Teachers by the Los Angeles Times. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/publication/due-diligence
- Burris, C. C. & Welner, K. G. (2011). Letter to Secretary of Education Arne Duncan concerning evaluation of teachers and principals. Boulder, CO: National Education Policy Center. Retrieved from: http://nepc.colorado.edu/publication/letter-to-Arne-Duncan
- Chester, M. D. (2003). Multiple measures and high-stakes decisions: A framework for combining measures. Educational Measurement: Issues and Practice, 22(2), 32–41. doi:10.1111/j.1745-3992.2003.tb00126.x
- Cole, R., Haimson, J., Perez-Johnson, I., & May, H. (2011). Variability in pretest-posttest correlation coefficients by student achievement level. Washington, D.C.: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from ies.ed.gov/ncee/pubs/20114033/pdf/20114033.pdf
- Collins, C., & Amrein-Beardsley, A. (2014). Putting growth and value-added models on the map: A national overview. Teachers College Record, 16(1). Retrieved from: http://www.tcrecord.org/Content.asp?ContentId=17291
- Constantine, J., Player, D., Silva, T., Hallgren, K., Grider, M., Deke, J., & & Warner, E. (2009). An evaluation of teachers trained through different routes to certification: Final report. Princeton, NJ: Mathematica Policy Research. Retrieved from http://ies.ed.gov/ncee/pubs/20094043/pdf/20094043.pdf
- Corcoran, S. P. (2010). Can teachers be evaluated by their students’ test scores? Should they be? The use of value-added measures of teacher effectiveness in policy and practice. Providence, RI: Annenberg Institute for School Reform. Retrieved from http://www.annenberginstitute.org/publications/can-teachers-be-evaluated-their-students%E2%80%99-test-scores-should-they-be-use-value-added-me
- Darling-Hammond, L. (2010). Too unreliable. The New York Times. Retrieved from http://www.nytimes.com/roomfordebate/2010/09/06/assessing-a-teachers-value/value-added-assessment-is-too-unreliable-to-be-useful
- Darling-Hammond, L., Amrein-Beardsley, A., Haertel, E., & Rothstein, J. (2012). Evaluating teacher evaluation. Phi Delta Kappan, 93(6), 8-15. Retrieved from http://www.kappanmagazine.org/content/93/6/8.full.pdf+html
- Darling-Hammond, L. & Haertel, E. (2012). A better way to grade teachers. Los Angeles Times \[op-ed\]. Retrieved from http://www.latimes.com/news/opinion/commentary/la-oe-darling-teacher-evaluations-20121105,0,650639.story
- Di Carlo, M. (2013). A few points about the instability of value-added estimates. The Shanker Blog. Retrieved from http://shankerblog.org/?p=7446
- Eckert, J. M., & Dabrowski, J. (2010). Should value-added measures be used for performance pay? Phi Delta Kappan, 91(8), 88-92.
- Ehlert, M., Koedel, C., Parsons, E., & Podgursky, M. (2012). Selecting growth measures for school and teacher evaluations. Washington, D.C: National Center for Analysis of Longitudinal Data in Education Research (CALDER). Retrieved from http://economics.missouri.edu/working-papers/2012/WP1210_koedel.pdf
- Ehlert, M., Koedel, C., Parsons, E., & Podgursky, M. (2013). The sensitivity of value-added estimates to specification adjustments: Evidence from school- and teacher-level models in Missouri. Statistics and Public Policy, 1(1), 19-27. doi: 10.1080/2330443X.2013.856152
- Ewing, D. (2011). Leading mathematician debunks ‘value-added.’ The Washington Post \[blog post\]. Retrieved from http://www.washingtonpost.com/blogs/answer-sheet/post/leading-mathematician-debunks-value-added/2011/05/08/AFb999UG_blog.html
- Gabriel, R. & Lester, J. N. (2013). Sentinels guarding the grail: Value-added measurement and the quest for education reform. Education Policy Analysis Archives, 21(9), 1-30. Retrieved from http://epaa.asu.edu/ojs/article/view/1165
- Glazerman, S. M., & Potamites, L. (2011). False performance gains: A critique of successive cohort indicators. Princeton, NJ: Mathematica Policy Research. Retrieved from https://www.mathematica-mpr.com/our-publications-and-findings/publications/false-performance-gains-a-critique-of-successive-cohort-indicators
- Graue, M. E., Delaney, K. K., & Karch, A. S. (2013). Ecologies of education quality. Education Policy Analysis Archives, 21(8), 1-36. Retrieved from http://epaa.asu.edu/ojs/article/view/1163
- Guarino, C. M., Maxfield, M., Reckase, M. D., Thompson, P., & Wooldridge, J.M. (2012). An evaluation of Empirical Bayes’ estimation of value-added teacher performance measures. East Lansing, MI: Education Policy Center at Michigan State University. Retrieved from http://www.aefpweb.org/sites/default/files/webform/empirical_bayes_20120301_AEFP.pdf
- Guarino, C. M., Reckase, M. D., & Wooldridge, J. M. (2012). Can value-added measures of teacher education performance be trusted? East Lansing, MI: The Education Policy Center at Michigan State University. Retrieved from http://education.msu.edu/epc/library/documents/WP18Guarino-Reckase-Wooldridge-2012-Can-Value-Added-Measures-of-Teacher-Performance-Be-T_000.pdf
- Haertel, E. H. (2013). Reliability and validity of inferences about teachers based on student test scores. Princeton, NJ: Education Testing Service. Retrieved from http://www.ets.org/Media/Research/pdf/PICANG14.pdf
- Haut, M. & Elliott, S. W (Eds.). (2011). Incentives and test-based accountability in education. Committee on Incentives and Test-based Accountability in Public Education, National Research Council. Washington, DC: National Academies Press. Retrieved from http://nfpcar.org/Archive/Education_Evaluation_12521.pdf
- Hermann, M., Walsh, E., Isenberg, E., & Resch, A. (2013). Shrinkage of value-added estimates and characteristics of students with hard-to-predict achievement levels. Princeton, NJ: Mathematica Policy Research. Retrieved from https://www.mathematica-mpr.com/our-publications-and-findings/publications/ja-shrinkage-of-valueadded-estimates-and-characteristics-of-students-with-hardtopredict
- Hill, H. C., Kapitula, L, & Umlan, K. (2011). A validity argument approach to evaluating teacher value-added scores. American Educational Research Journal, 48(3), 794-831. doi:10.3102/0002831210387916
- 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
- Jackson, C. K. (2012). Teacher quality at the high-school level: The importance of accounting for tracks. Cambridge, MA: The National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w17722
- Jennings, J. L., & Corcoran, S. P. (2009). “Beware of geeks bearing formulas:” Reflections on growth models for school accountability. Phi Delta Kappan. 90(9), 635-639.
- Johnson, W. (2012). Confessions of a ‘bad’ teacher. The New York Times. Retrieved from http://www.nytimes.com/2012/03/04/opinion/sunday/confessions-of-a-bad-teacher.html?_r=2&pagewanted=all
- Kennedy, M. M. (2010). 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
- Koedel, C., & Betts, J. (2010). Value-added to what? How a ceiling in the testing instrument influences value-added estimation. Education Finance and Policy, 5(1), 54-81.
- Kupermintz, H. (2003). Teacher effects and teacher effectiveness: A validity investigation of the Tennessee Value-Added Assessment System. Educational Evaluation and Policy Analysis, 25, 287-298. doi:10.3102/01623737025003287
- Linn, R. L. (2008). Methodological issues in achieving school accountability. Journal of Curriculum Studies, 40, 699-711. doi:10.1080/00220270802105729
- Linn, R L., & Haug, C. (2002). Stability of school-building accountability scores and gains. Educational Evaluation and Policy Analysis, 24, 29-36. doi:10.3102/01623737024001029
- McCaffrey, D. F., Lockwood, J. R., Koretz, D. M., & Hamilton, L. S. (2003). Evaluating value-added models for teacher accountability. Santa Monica, CA: RAND Corporation. Retrieved from http://www.rand.org/content/dam/rand/pubs/monographs/2004/RAND_MG158.pdf
- McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A. & Hamilton, L. (2004a). Let’s see more empirical studies on value-added modeling of teacher effects: A reply to Raudenbush, Rubin, Stuart and Zanutto, and Reckase. Journal of Educational and Behavioral Statistics, 29(1), 139-143. doi:10.3102/10769986029001139
- McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004b). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67-101. Retrieved from http:// www.rand.org/pubs/reprints/2005/RAND_RP1165.pdf.
- McCaffrey, D. F., Sass, T. R., Lockwood, J. R., & 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
- Martineau, J. A. (2010). The validity of value-added models: An allegory. Phi Delta Kappan, 91(7), 64-67.
- Mathis, W. (2016). Research-based options for education policy making: Teacher evaluation. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/publication/options-teacher-evaluations
- 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
- Nye, B., Konstantopoulos, S., & Hedges, L. V. (2004). How large are teacher effects? Educational Evaluation and Policy Analysis, 26(3), 237-257. doi:10.3102/01623737026003237
- Papay, J. P. (2011). Different tests, different answers: The stability of teacher value-added estimates across outcome measures. American Educational Research Journal, 48(1), 163-193. doi: 10.3102/0002831210362589
- Paufler, N. A., & Amrein-Beardsley, A. (2014). The random assignment of students Into elementary classrooms: Implications for value-added analyses and interpretations. American Educational Research Journal, 51(2), 328-362. doi: 10.3102/0002831213508299
- Polikoff, M. S., & Porter, A. C. (2014). Instructional alignment as a measure of teaching quality. Education Evaluation and Policy Analysis. doi:10.3102/0162373714531851
- Pullin, D. (2013). Legal issues in the use of student test scores and value-added models (VAM) to determine educational quality. Education Policy Analysis Archives, 21(6), 1-27. Retrieved from http://epaa.asu.edu/ojs/article/view/1160
- Raudenbush, S. W. (2004). What are value-added models estimating and what does this imply for statistical practice? Journal of Educational and Behavioral Statistics, 29(1), 121-129. doi:10.3102/10769986029001121
- Ravitch, D. (2013). Reign of error: The hoax of the privatization movement and the danger to America’s public schools. New York, NY: Knopf, Random House.
- Reardon, S. F., & Raudenbush, S. W. (2009). Assumptions of value-added models for estimating school effects. Education Finance and Policy, 4(4), 492-519. doi:10.1162/edfp.2009.4.4.492
- 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: http://dx.doi.org/10.1162/edfp.2009.4.4.537
- Rothstein, J. (2010). Teacher quality in educational production: Tracking, decay, and student achievement. Quarterly Journal of Economics, 175-214. doi:10.1162/qjec.2010.125.1.175
- Rubin, D. B., Stuart, E. A., & Zanutto, E. L. (2004). A potential outcomes view of value-added assessment in education. Journal of Educational and Behavioral Statistics, 29(1), 103-116. doi:10.3102/10769986029001103
- Scherrer, J. (2011). Measuring teaching using value-added modeling: The imperfect panacea. NASSP Bulletin, 95(2), 122-140. doi:10.1177/0192636511410052
- Schochet, P. Z. & Chiang, H. S. (2010). Error rates in measuring teacher and school performance based on student test score gains. Washington DC: U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/pubs/20104004/
- Stacy, B., Guarino, C., Recklase, M., & Wooldridge, J. (2012). Does the precision and stability of value-added estimates of teacher performance depend on the types of students they serve? East Lansing, MI: Education Policy Center at Michigan State University. Retrieved from https://appam.confex.com/appam/2012/webprogram/Paper3327.html
- Tekwe, C. D., Carter, R. L., Ma, C., Algina, J., Lucas, M. E., Roth, J., …Resnick, M. B. (2004). An empirical comparison of statistical models for value-added assessment of school performance. Journal of Educational and Behavioral Statistics, 29(1), 11-36. doi:10.3102/10769986029001011
- Zeis, C., Waronska, A. K., & Fuller, R. (2009). Value-added program assessment using nationally standardized tests: Insights into internal validity issues. Journal of Business and Economics, 9(1), 114-127.