Researchers and policymakers often assume that teacher turnover harms student achievement, though recent studies suggest this may not be the case Using a unique identification strategy that employs school-by-grade level turnover and two classes of fixed-effects models, this study estimates the effects of teacher turnover on over 850,000 New York City fourth- and fifth-grade student observations over 8 years The results indicate that students in grade levels with higher turnover score lower in both English language arts (ELA) and math and that these effects are particularly strong in schools with more low-performing and Black students Moreover, the results suggest that there is a disruptive effect of turnover beyond changing the distribution in teacher quality

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How Teacher Turnover Harms Student Achievement

For teacher education, this is perhaps the best of times and the worst of times. It may be the best of times because so much hard work has been done by many teacher educators over the past two decades to develop more successful program models and because voters have just elected a president of the United States who has a strong commitment to the improvement of teaching. It may be the worst of times because there are so many forces in the environment that conspire to undermine these efforts. In this article, the author discusses the U.S. context for teacher education, the power of teacher preparation for transforming teaching and learning, and the current challenges for this enterprise in the United States.

Teacher Education and the American Future.

There are fierce debates over the best way to prepare teachers. Some argue that easing entry into teaching is necessary to attract strong candidates, while others argue that investing in high quality teacher preparation is the most promising approach. Most agree, however, that we lack a strong research basis for understanding how to prepare teachers. This paper is one of the first to estimate the effects of features of teachers' preparation on teachers' value-added to student test score performance in math and English Language Arts. Our results indicate variation across preparation programs in the average effectiveness of the teachers they are supplying to New York City schools. In particular, preparation directly linked to practice appears to benefit teachers in their first year.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w14314.pdf?abstractid=1264576&mirid=1&type=2

Teacher Preparation and Student Achievement

There are fierce debates over the best way to prepare teachers. Some argue that easing entry into teaching is necessary to attract strong candidates, whereas others argue that investing in high quality teacher preparation is the most promising approach. Most agree, however, that we lack a strong research basis for understanding how to prepare teachers. This article is one of the first to estimate the effects of features of teachers’ preparation on teachers’ value added to student test score performance. Our results indicate variation across preparation programs in the average effectiveness of the teachers they are supplying to New York City schools. In particular, preparation directly linked to practice appears to benefit teachers in their 1st year.

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We use data on statewide end-of-course tests in North Carolina to examine the relationship between teacher credentials and student achievement at the high school level The availability of test scores in multiple subjects for each student permits us to estimate a model with student fixed effects, which helps minimize any bias associated with the non-random distribution of teachers and students among classrooms within schools We find compelling evidence that teacher credentials affect student achievement in systematic ways and that the magnitudes are large enough to be policy relevant As a result, the uneven distribution of teacher credentials by race and socio-economic status of high school students -- a pattern we also document -- contributes to achievement gaps in high school

Teacher Credentials and Student Achievement in High School: A Cross-Subject Analysis with Student Fixed Effects

The role of teacher quality in retention and hiring: Using applications to transfer to uncover preferences of teachers and schools: The Role of Teacher Quality in Retention and Hiring

Value-added models in education research allow researchers to explore how a wide variety of policies and measured school inputs affect the academic performance of students. Researchers typically quantify the impacts of such interventions in terms of effect sizes, i.e., the estimated effect of a one standard deviation change in the variable divided by the standard deviation of test scores in the relevant population of students. Effect size estimates based on administrative databases typically are quite small. Research has shown that high quality teachers have large effects on student learning but that measures of teacher qualifications seem to matter little, leading some observers to conclude that, even though effectively choosing teachers can make an important difference in student outcomes, attempting to differentiate teacher candidates based on pre-employment credentials is of little value. This illustrates how the perception that many educational interventions have small effect sizes, as traditionally measured, are having important consequences for policy. In this paper we focus on two issues pertaining to how effect sizes are measured. First, we argue that model coefficients should be compared to the standard deviation of gain scores, not the standard deviation of scores, in calculating most effect sizes. The second issue concerns the need to account for test measurement error. The standard deviation of observed scores in the denominator of the effect-size measure reflects such measurement error as well as the dispersion in the true academic achievement of students, thus overstating variability in achievement. It is the size of an estimated effect relative to the dispersion in the true achievement or the gain in true achievement that is of interest. Adjusting effect-size estimates to account for these considerations is straightforward if one knows the extent of test measurement error. Technical reports provided by test vendors typically only provide information regarding the measurement error associated with the test instrument. However, there are a number of other factors, including variation in scores associated with students having particularly good or bad days, which can result in test scores not accurately reflecting true academic achievement. Using the covariance structure of student test scores across grades in New York City from 1999 to 2007, we estimate the overall extent of test measurement error and how measurement error varies across students. Our estimation strategy follows from two key assumptions: (1) there is no persistence (correlation) in each student’s test measurement error across grades; (2) there is at least some persistence in learning across grades with the degree of persistence constant across grades. Employing the covariance structure of test scores for NYC students and alternative models characterizing the growth in academic achievement, we find estimates of the overall extent of test measurement error to be quite robust. Returning to the analysis of effect sizes, our effect-size estimates based on the dispersion in gain scores net of test measurement error are four times larger than effect sizes typically measured. To illustrate the importance of this difference, we consider results from a recent paper analyzing how various attributes of teachers affect the test-score gains of their students (Boyd et al., in press). Many of the estimated effects appear small when compared to the standard deviation of student achievement – that is effect sizes of less than 0.05. However, when measurement error is taken into account, the associated effect sizes often are about 0.16. Furthermore, when teacher attributes are considered jointly, based on the teacher attribute combinations commonly observed, the overall effect of teacher attributes is roughly half a standard deviation of universe score gains – even larger when teaching experience is also allowed to vary. The bottom line is that there are important differences in teacher effectiveness that are systematically related to observed teacher attributes. Such effects are important from a policy perspective, and should be taken into account in the formulation and implementation of personnel policies.

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Measuring Effect Sizes: the Effect of Measurement Error

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The Effect of School Neighborhoods on Teacher Career Decisions

The use of value-added models in education research has expanded rapidly. These models allow researchers to explore how a wide variety of policies and measured school inputs affect the academic performance of students. An important question is whether such effects are sufficiently large to achieve various policy goals. For example, would hiring teachers having stronger academic backgrounds sufficiently increase test scores for traditionally low-performing students to warrant the increased cost of doing so? Judging whether a change in student achievement is important requires some meaningful point of reference. In certain cases a grade-equivalence scale or some other intuitive and policy relevant metric of educational achievement can be used. However, this is not the case with item response theory (IRT) scale-score measures common to the tests usually employed in value-added analyses. In such cases, researchers typically describe the impacts of various interventions in terms of effect sizes, although conveying the intuition of such a measure to policymakers often is a challenge. The effect size of an independent variable is measured as the estimated effect of a one standard deviation change in the variable divided by the standard deviation of test scores in the relevant population of students. Intuitively, an effect size represents the magnitude of change in a variable of interest, e.g., student achievement, resulting from a one standard deviation, or rather large, change in another variable, e.g., class-size. Effect size estimates derived from value-added models employing administrative databases typically are quite small. For example, in several recent papers the average effect size of being in the second year of teaching relative to the first year, other things equal, is about 0.04 standard deviations for math achievement and 0.025 standard deviations for reading achievement, with variation no more than 0.02. Additional research examines the effect sizes of a variety of other teacher attributes: alternative certification compared to traditional certification (Boyd et al. 2006; Kane et al. in press); passing state certification exams As one example, consider results from a recent paper analyzing how various attributes of teachers affect the test-score gains of their students (Boyd et al. 2008). Parameter estimates reflecting the effects of a subset of the teacher attributes included in the analysis are shown in the first column of table 1. These estimated effects, measured relative to the standard deviation of observed student achievement scores, indicate that none of the estimated effect sizes are large by standards often employed …

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Overview of Measuring Effect Sizes: The Effect of Measurement Error. Brief 2.

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Hamp Lankford

Papers

The role of teacher quality in retention and hiring: Using applications to transfer to uncover preferences of teachers and schools: The Role of Teacher Quality in Retention and Hiring

Measuring Effect Sizes: the Effect of Measurement Error

The Effect of School Neighborhoods on Teacher Career Decisions

Overview of Measuring Effect Sizes: The Effect of Measurement Error. Brief 2.

The Narrowing Gap in New York City Teacher Qualifications and Implications for Student Achievement in High-Poverty Schools. Brief 6.