By Daniel McCaffrey
Clarifies the first questions raised by means of value-added versions for measuring instructor results, stories crucial fresh functions of VAM, and discusses statistical and size matters assoicated with VAM.
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Implicitly, any variation in δ is captured in the residual error term. A teacher effect, θ t(ij) ,,is added for each year and these effects remain in the model undiminished at the future test administrations, which is why the model for the score at time t includes terms for all previous teachers. Although for convenience we refer to the term θ t(ij) as teacher effects, RCM acknowledge that they are residual classroom effects. As shown in McCaffrey et al. 3) so the only teacher effect on gain scores is from the current teacher; other teachers do not contribute.
Also, if empirical estimates of teacher effects do not correlate with other generally ac- What Are We Trying to Measure with VAM? 15 cepted traits of effective teachers, we might be concerned that our statistical estimation of teacher effects is too error-prone (due to sampling or systematic errors) to be useful. Identifying Assumptions Required for Appropriate Application of VAM Regardless of the definition of the causal teacher effect, most statistical models will produce unbiased or consistent estimates of a particular effect only when certain untestable assumptions hold.
6 The factor will depend on the standard error of measurement in tests, which must be obtained from external sources such as the test publisher. Literature Review 29 bound on the ideal) and to present both. Without more information, we cannot determine a lower bound for the RCM estimates. The Prospects data include many students with incomplete records of test scores (for some cohorts as many as 73 percent of records are incomplete—that is, 73 percent of students are missing a score for one or more tests) and RCM provide no details on how these records were used in the various analyses.