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We examine access to effective teachers for low-income students in 26 geographically dispersed school districts over a 5-year period. We measure teacher effectiveness using a value-added model that accounts for measurement error in prior test scores and peer effects. Differences between the average value added of teachers of high- and low-income students are 0.005 standard deviations in English/language arts and 0.004 standard deviations in math. Differences between teachers of Black, Hispanic, and White students are also small. Rearranging teachers to obtain perfect equity would do little to narrow the sizable student achievement gap between low- and high-income students. We also show that a higher proportion of novice teachers in high-poverty schools contributes negligibly to differences in access to effective teachers.

In 2010, Teach For America (TFA) launched a major expansion effort, funded in part by a five-year Investing in Innovation scale-up grant from the U.S. Department of Education. To examine the effectiveness of TFA elementary school teachers in the second year of the scale-up, we recruited thirty-six schools from ten states and randomly assigned students in participating schools to a class taught by a TFA teacher or a class taught by a comparison teacher. We then gathered data on student achievement and surveyed teachers on their educational background, preparation for teaching, and teaching experience. The TFA teachers in the study schools had substantially less teaching experience than comparison teachers but were more likely to have graduated from a selective college. Overall, TFA and comparison teachers in the study were similarly effective in teaching both reading and math. TFA teachers in early elementary classrooms (grades 2 and below), however, were more effective than comparison teachers: TFA teachers in prekindergarten through grade 2 had a positive, statistically significant effect of 0.12 standard deviations on students’ reading achievement, and TFA teachers in grades 1 and 2 had a positive, marginally significant effect of 0.16 standard deviations on student math achievement.

It is common in the implementation of teacher accountability systems to use empirical Bayes shrinkage to adjust teacher value-added estimates by their level of precision. Because value-added estimates based on fewer students and students with “hard-to-predict” achievement will be less precise, the procedure could have differential impacts on the probability that the teachers of fewer students or students with hard-to-predict achievement will be assigned consequences. This article investigates how shrinkage affects the value-added estimates of teachers of hard-to-predict students. We found that teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates. However, in our sample, shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences.

Researchers often presume that it is better to use administrative data from grades 4 and 5 than data from grades 6 through 8 for conducting research on teacher effectiveness that uses value-added models because (1) elementary school teachers teach all subjects to their students in self-contained classrooms and (2) classrooms are more homogenous at the elementary school level. We examined the first issue by using data on teacher–student links in which teachers of mathematics and/or English/language arts had verified the subjects and students they taught. We compared these data to teacher–student links from the original administrative data. Results show that instruction is often departmentalized in these grades. About one in six elementary school teachers in the original data was linked to a subject that he or she did not teach. To examine the second issue, we computed the variation in baseline student achievement within classes, between classes at the same school, and between schools. We found mor...

We compare teacher evaluation scores from a typical value-added model to results from the Colorado Growth Model (CGM), which 16 states currently use or plan to use as a component of their teacher performance evaluations. The CGM assigns a growth percentile to each student by comparing each student's achievement to that of other students with similar past test scores. The median (or average) growth percentile of a teacher's students provides the measure of teacher effectiveness. The CGM does not account for other student background characteristics and excludes other features included in many value-added models used by states and school districts. Using data from the District of Columbia Public Schools (DCPS), we examine changes in evaluation scores across the two methods for all teachers and for teacher subgroups. We find that use of growth percentiles in place of value added would have altered evaluation consequences for 14% of DCPS teachers. Most differences in evaluation scores based on the two methods ...

As states and districts incorporate value-added estimates into multiple-measures systems of teacher evaluation, it has become increasingly important to understand how to model value added when the same student is taught the same subject by multiple teachers. Roster data on teacher–student links that have been checked and confirmed by the teachers themselves show levels of co-teaching far beyond what appear in administrative data. Therefore, to help states and districts overcome a potential limitation in the use of value added, we propose and examine three methods estimating teacher value added when students are co-taught: the Partial Credit Method, Teacher Team Method, and Full Roster Method. The Partial Credit Method apportions responsibility between teachers according to the fraction of the year a student spent with each. This method, however, has practical problems limiting its usefulness. The Teacher Team Method and Full Roster Method presume that co-teachers share joint responsibility for the achievement of their shared students. We explore the properties of these methods and compare empirical estimates. Both methods produce similar estimates of teacher value added, but the Full Roster Method can be more easily implemented in practice.

This article discusses quantitative research on homeschooling, including the available data, pitfalls of using the data, estimates of the number of homeschooled children, part-time homeschooling, and why families homeschool. I compare research on homeschooling to research on charter schools, voucher programs, and private schools.

In 2010, Teach For America (TFA) launched a major expansion effort, funded in part by a five-year Investing in Innovation (i3) scale-up grant of $50 million from the U.S. Department of Education. This study examines the effectiveness of TFA elementary school teachers in the second year of the scale-up, relative to other teachers in the same grades and schools.

In this report, we document our approach to estimating a value-added model of teacher effectiveness in the District of Columbia Public Schools and eligible DC charter schools participating in Race to the Top during the 2013–2014 school year.

Some educators are concerned that students with test scores at the bottom or top of the test score distribution will negatively affect the value-added estimates of teachers of those students.

Recent federal initiatives in education, such as Race to the Top, the Teacher Incentive Fund, and the flexibility policy for the Elementary and Secondary Education Act are designed in part to ensure that disadvantaged students have equal access to effective teaching. The initiatives respond to the concern that disadvantaged students may be taught by less effective teachers and that this could contribute to the achievement gap between disadvantaged students and other students. To address the need for evidence on this issue, the Institute of Education Sciences at the U.S. Department of Education initiated a study to examine access to effective teaching for disadvantaged students in 29 diverse school districts. Mathematica Policy Research and its partner, the American Institutes for Research, conducted the study, which focused on English/language arts (ELA) and math teachers in grades 4 through 8 from the 2008–2009 to the 2010–2011 school year.

This working paper compares teacher/student links that have undergone a roster confirmation process—whereby teachers verify the subjects and students they taught—to teacher/student links from unconfirmed administrative data. Due to the departmentalization of instruction in math and reading/English language arts in grades 4 and 5, about one in six teachers in these grades and subjects is linked in the unconfirmed data to a subject that he or she does not teach. The authors discuss the circumstances in which using unconfirmed teacher/student links in value-added models most affect research.

This working paper compares teacher evaluation scores from a typical value-added model with results from the Colorado Growth Model (CGM) and finds that use of the CGM in place of a value-added model depresses the evaluation scores for teachers with more English language learner students and increases the evaluation scores for teachers of low-achieving students.

We outline the options available to policymakers for addressing co-teaching in a value-added model. Building on earlier work, we propose an improvement to a method of accounting for co-teaching that treats co-teachers as teams, with each teacher receiving equal credit for co-taught students. Hock and Isenberg (2012) described a method known as the Full Roster Method (FRM) that is feasible and practical, but it effectively counts co-taught students more than once—these students receive a full weight with each of their teachers, so such students receive extra weight when calculating the relationship between student characteristics and achievement. The improvement, known as the Full Roster-Plus Method, allows co-taught students to receive full weight with their teachers, but all students contribute equally to the calculation of the relationship between student characteristics and achievement. To investigate how the application of this method empirically changes value-added estimates, we use data from...

This working paper investigates how empirical Bayes shrinkage, an approach commonly used in implementing teacher accountability systems, affects the value-added estimates of teachers of students with hard-to-predict achievement levels, such as students who have low prior achievement and receive free lunch. Teachers of these students tend to have less precise value-added estimates than teachers of other types of students. Shrinkage increases their estimates’ precision and reduces the absolute value of their value-added estimates. However, this paper found shrinkage has no statistically significant effect on the relative probability that teachers of hard-to-predict students receive value-added estimates that fall in the extremes of the value-added distribution and, as a result, receive consequences in the accountability system.

This working paper uses urban school district data to investigate whether including track indicators or accounting for classroom characteristics in the value-added model is sufficient to eliminate potential bias resulting from the sorting of students into academic tracks. Accounting for two classroom characteristics—mean classroom achievement and the standard deviation of classroom achievement—may reduce bias for middle school math teachers, whereas track indicators help for high school reading teachers. However, including both of these measures simultaneously reduces the precision of the value-added estimates in this context. While these different specifications produce substantially different value-added estimates, they produce small changes in the tails of value-added distribution.

Provides a comprehensive portrait of typical induction support provided to elementary school teachers in urban districts around the country in their first year.

This report presents the value-added models used to measure school and teacher effectiveness in the District of Columbia Public Schools in the 2010–2011 school year.

This report describes the design of a value-added model to measure school and teacher performance in the District of Columbia Public Schools. It presents the main features of the method in nontechnical terms, data used, and technical details of the statistical methods.

Teacher induction programs are designed to support new teachers and reduce teacher turnover. Comprehensive teacher induction, however, goes beyond traditional induction by relying on carefully selected and trained full-time mentors and also includes an intensive curriculum involving instructional support and professional development, opportunities to observe experienced teachers, and assessment tools for teachers that permit ongoing evaluation of practice and constructive feedback. Final results from Mathematica’s national evaluation show that compared to business-as-usual induction programs, two years of comprehensive induction can boost student achievement. For teachers who received one year of comprehensive induction, however, there was no impact. The study found no impacts on other outcomes.

This report presents the methods used to identify effective schools in the Memphis City Schools during the second year of the project.

This report presents the methods used to identify effective schools and teachers for a consortium of over 140 charter schools in 17 states and the District of Columbia during the second year of the project.

This report presents the methods used to identify effective schools in the Memphis City Schools during the second year of the project.

Teacher induction programs are designed to support new teachers and reduce teacher turnover. Comprehensive teacher induction, however, goes beyond traditional induction by relying on carefully selected and trained full-time mentors and also includes an intensive curriculum involving instructional support and professional development, opportunities to observe experienced teachers, and assessment tools for teachers that permit ongoing evaluation of practice and constructive feedback. Findings from the second year of Mathematica’s national evaluation show that compared to business-as-usual induction programs, comprehensive induction did not lead to measurable improvements in student test scores, percentage of teachers remaining in their district or in the profession, or in qualifications of the teachers who were retained. This was true whether induction programs were delivered as one-year or two-year programs.

Comprehensive teacher induction provides novice teachers with carefully selected and trained full-time mentors, as well as a curriculum of intensive and structured support. This report presents the first round of results from a large-scale randomized controlled study of the effects of two comprehensive teacher induction programs. Intensive mentoring and professional development for teachers just entering the profession did not increase teacher retention or generate any significant improvements in classroom practices or student achievement during the first year of teaching.

Comprehensive teacher induction provides novice teachers with carefully selected and trained full-time mentors, as well as a curriculum of intensive and structured support. This report presents the first round of results from a large-scale randomized controlled study of the effects of two comprehensive teacher induction programs. Intensive mentoring and professional development for teachers just entering the profession did not increase teacher retention or generate any significant improvements in classroom practices or student achievement during the first year of teaching.

Comprehensive teacher induction provides novice teachers with carefully selected and trained full-time mentors, as well as a curriculum of intensive and structured support. This report presents the first round of results from a large-scale randomized controlled study of the effects of two comprehensive teacher induction programs. Intensive mentoring and professional development for teachers just entering the profession did not increase teacher retention or generate any significant improvements in classroom practices or student achievement during the first year of teaching.

New Leaders for New Schools, a nonprofit organization committed to training school principals, is heading an initiative that offers financial awards to effective educators. This report details the value-added model Mathematica developed to identify effective schools in the Memphis City school district during the first year of the project. The model estimates each school’s effect on student performance across all tested grades and subjects, accounting for student mobility, observable differences in student demographics, and measurement error in test scores.

In 2010, Teach For America (TFA) launched a major expansion effort, funded in part by a five-year Investing in Innovation (i3) scale-up grant of $50 million from the U.S. Department of Education. This study examines the effectiveness of TFA elementary school teachers in the second year of the scale-up, relative to other teachers in the same grades and schools.

This study explores the disparity in access to effective teachers in 29 school districts across the country, revealing that disadvantaged students receive poorer-quality instruction, on average, compared with other students. Mathematica conducted the studies for the Institute of Education Sciences.

Mathematica Policy Research has adapted its school-level value-added model to teacher-level data to help New Leaders for New Schools identify high-performing teachers in award-winning schools within Memphis City Schools.

New Leaders contracted with Mathematica Policy Research to help design the methods for identifying effective schools and teachers.