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State Data Systems: Michigan

Identifying Effective Teachers Policy

Look at National Findings
Goal

The state should have a data system that contributes some of the evidence needed to assess teacher effectiveness.

Meets goal in part
Suggested Citation:
National Council on Teacher Quality. (2011). State Data Systems: Michigan results. State Teacher Policy Database. [Data set].
Retrieved from: https://www.nctq.org/yearbook/state/MI-State-Data-Systems-8

Analysis of Michigan's policies

Michigan does not have a data system that can be used to provide evidence of teacher effectiveness.

However, Michigan does have two of three necessary elements that would allow for the development of a student- and teacher-level longitudinal data system. The state has assigned unique student identifiers that connect student data across key databases across years. It also has the capacity to match student test records from year to year in order to measure student academic growth.

Although Michigan assigns teacher identification numbers, it cannot match individual teacher records with individual student records.

Citation

Recommendations for Michigan

Develop capacity of state data system.
Michigan should ensure that its state data system is able to match individual teacher records with individual student records. 

Develop a clear definition of "teacher of record."
A definition of teacher of record is necessary in order to use the student-teacher data link for teacher evaluation and related purposes. Michigan defines the teacher of record as the certificated teacher who provides instruction, gives tests and quizzes, and evaluates student performance. However, to ensure that data provided through the state data system are actionable and reliable, Michigan should articulate a more distinct definition of teacher of record and require its consistent use throughout the state.

State response to our analysis

Michigan asserted that its Teacher Student Data Link Collection (TSDL) in the state's student data system will report links between students and the teacher(s) who provide instruction to them. These data are necessary to meet the requirements of the American Recovery and Reinvestment Act (ARRA) and the America Competes Act as part of the State Fiscal Stabilization Fund.

Michigan noted that this is a full-year collection, and reported data reflect the student's performance in classes taken throughout the current academic year and the status of his or her academic report at the end of the school year. Collection will be open mid-May through August 31.

The state also pointed out that in the April 2011 State School Aid Update, funds for the teacher student data link were included in the April 2011 payment. The amount of the reimbursement is $5.38 per pupil. The pupil count used is the current year blend of both general education and special education pupils. Additional State Aid Status Reports are posted online.

    • Best practice
    • Meets goal
    • Nearly Meets goal
    • Meets goal in part
    • Meets a small part of goal
    • Does not meet goal

  • Michigan

    Michigan map
Evaluation of Effectiveness
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Delivering Well Prepared Teachers
Expanding the Pool of Teachers
Identifying Effective Teachers
Retaining Effective Teachers
Exiting Ineffective Teachers
Research rationale

The Data Quality Campaign tracks the development of states' longitudinal data systems by reporting annually on states' inclusion of 10 elements in their data systems. Among these 10 elements are the three key elements (Elements 1, 3 and 5) that NCTQ has identified as being fundamental to the development of value-added assessment. For more information, see http://www.dataqualitycampaign.org.

For information about the use of student-growth models to report on student-achievement gains at the school level, see P. Schochet and H. Chiang, "Error Rates in Measuring Teacher and School Performance Based on Student Test Score Gains." Mathematica Policy Research. Department of Education (2010); as well as The Commission on No Child Left Behind, "Commission Staff Research Report: Growth Models, An Examination Within the Context of NCLB," Beyond NCLB, 2007.

For information about the differences between accountability models, including the differences between growth models and value-added growth models, see Pete Goldschmidt, et al., "Policymakers' Guide to Growth Models for School Accountability: How Do Accountability Models Differ?" Council of Chief State School Officers' Report, 2005 at: http://www.ccsso.org/publications/details.cfm?PublicationID=287

For information regarding the methodologies and utility of value-added analysis see, C. Koedel and J. Betts, "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique." Education Finance and Policy Vol. 6 No. 1 (2011), D. Goldhaber and M. Hansen, "Assessing the Potential of Using Value-Added Estimates of Teacher Job Performance for Making Tenure Decisions." Urban Institute (2010), and S. Glazerman et al, "Evaluating Teachers; The Important Role of Value-Added." Brookings Brown Center Task Group on Teacher Quality (2011); Glazerman, Steven et. al., Passing Muster: Evaluating Teacher Evaluation Systems, The Brookings Brown Center Task Group on Teacher Quality (2011); Harris, D.N.  (2009). "Teacher value-added: Don't end the search before it starts," Journal of Policy Analysis and Management, 28(4), pp. 693-699. Hill, H.C. (2009). "Evaluating value-added models: A validity argument approach," Journal of Policy Analysis and Management, 28(4), pp. 700-709; Kane, T.J. & Staiger, D.O. (2008). Estimating teacher impacts on student achievement: An experimental evaluation. NBER Working Paper W14607. Cambridge, MA: National Bureau of Economic Research.

There is no shortage of studies using value-added methodologies by researchers including Thomas J. Kane, Eric Hanushek, Steven Rivkin, Jonah E. Rockoff and Jessie Rothstein. See also Kane, T.J. 2008. Estimating teacher impacts on student achievement: An experimental evaluation. Working Paper 14607. Cambridge, MA: National Bureau of Economic Research; Hanushek, Erik A. and Steven G. Rivkin. "Generalizations about using value-added measures of teacher quality." American Economic Review (May 2010); Rothstein, Jesse. 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement." Quarterly Journal of Economics, 25(1); Kane, Thomas J. and Douglas O. Staiger. 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation." National Bureau of Economic Research W14607, December. Rivkin, Steven G.; Eric A. Hanushek and John F. Kain. 2005. "Teachers, Schools, and  Academic Achievement." Econometrica, 73(2), pp. 417-58; Hanushek, Eric A. 2010. "The Difference is Teacher Quality." In Waiting for "Superman": How We Can Save America's Failing Public Schools, Karl Weber, ed. New York: Public Affairs.

See also NCTQ's "If Wishes Were Horses" by Kate Walsh at: http://www.nctq.org/p/publications/docs/wishes_horses_20080316034426.pdf and the National Center on Performance Incentives at: www.performanceincentives.org.

For information about the limitations of value-added analysis, see Jesse Rothstein, "Do Value-Added Models Add Value? Tracking, Fixed Effects, and Casual Inference." Princeton University and NBER. (2007) as well as Dale Ballou, "Value-added Assessment: Lessons from Tennessee," Value Added Models in Education: Theory and Applications, ed. Robert W. Lissitz (Maple Grove, MN: JAM Press, 2005).See also Dale Ballou, "Sizing Up Test Scores," Education Next, Summer 2002; 2(2).

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