Evaluating Value-Added Models for Teacher Accountability by Daniel McCaffrey

By Daniel McCaffrey

Clarifies the first questions raised by means of value-added versions for measuring instructor results, experiences crucial fresh functions of VAM, and discusses statistical and dimension matters assoicated with VAM.

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2) 26 Evaluating Value-Added Models for Teacher Accountability where αi and βi are the random intercept and slope for the school; αij and β ij are the random intercept and slope for the student; xijt denotes a vector of student characteristics, some of which might vary over time; θ1(ij) to θ t(ij) are the effects for the student’s teachers at testing times 1 to t; and eijt is a residual error term. Thus, scores are models by a common quadratic function of time (α + β t + δt 2 ) plus school-specific and student-specific random linear time trends.

Most schools, however, have only a handful of teachers in any grade. With small samples, D will vary with teacher effects as well as with turnover rates. In particular, if average teacher effectiveness is related to turnover rates at schools, the estimate will be biased upward. Such a relationThe authors argue that the covariance between (T i+ 1g +1 − T i +1 g ) and (T ig+1 − T ig ) is a function of the proportion, p, of teachers who teach both cohorts and the teacher effect variance component, τ 2 .

Along the same line of reasoning, the causal effect of a teacher alone does not give any indication of what makes a teacher effective. It provides no description of the practices, traits, or characteristics of teachers with large effects. Policies for improving teacher effectiveness require knowledge of the attributes that distinguish effective teachers from others. Knowledge that they can produce larger gains in student achievement than other teachers is not sufficient. Also, if empirical estimates of teacher effects do not correlate with other generally ac- What Are We Trying to Measure with VAM?

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