Search

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. Sample Results for Math with Pathway/Institution Effects and Description of Variables are appended. (Contains 6 footnotes, 9 tables and 4 figures.) [Funding for this report was provided by the City University of New York.]

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. An appendix is included. (Contains 34 footnotes, 4 figures, and 9 tables.) ["Overview of Measuring Effect Sizes: The Effect of Measurement Error. Brief 2" (ED508284) was based on this report.]

This paper explores a little-understood aspect of labor markets, their spatial geography. Using data from New York State, it finds teacher labor markets to be geographically very small. Teachers express preferences to teach close to where they grew up, and, controlling for proximity, they prefer areas with characteristics similar to their hometown. The paper discusses implications of these preferences for the successful recruitment of teachers, including the potential benefits of local recruiting and training. It also discusses implications for the modeling of teacher labor markets, including the possible biases that arise in estimates of compensating differentials when distance is omitted from the analyses. This study contributes to the literature on the geography of labor markets more generally by employing data on residential location during childhood instead of current residence, which may be endogenous to job choice. The final section of the paper summarizes findings on the geographic scope of teacher labor markets and examines their application to policy issues in the recruitment of teachers.(Contains 37 references and 10 tables/figures.) (SM)

Understanding what makes an effective teacher, as well as how teachers sort by their effectiveness across schools, is central to understanding and addressing student achievement gaps. Prior studies have found substantial sorting of teachers across schools, with the schools with the highest proportions of poor, non-white, and low-scoring students having the least qualified teachers as measured by certification, exam performance, and inexperience. Yet there have been substantial changes in the educational policy landscape over the past five years. In this paper, the authors examine these changes, asking how the distribution of teachers has changed in recent years and what the implications of these changes are for students. The authors address these questions using data on New York City teachers, students, and schools. While the findings may be specific to New York City, they may mirror changes in other large urban districts, many of which have seen similar policy changes over the past decade. The authors find that measurable characteristics of teachers are more equal across schools in 2005 than they were in 2000. Schools with large proportions of poor students and students of color, on average, have teachers whose observable qualifications are much stronger than they were five years ago. Nonetheless, a meaningful number of schools with large proportions of poor students did not demonstrate such improvement. The authors find that changes in these observed qualifications of teachers account for a modest improvement in the average achievement of students in the poorest schools. More important, the results suggest that recruiting teachers with stronger observed qualifications--for example, math SAT scores or certification status--could substantially improve student achievement. (Contains 6 tables, 9 figures, and 9 footnotes.)

We are in the midst of what amounts to a national experiment in how best to attract, prepare, and retain teachers, particularly for high poverty urban schools. Using data on students and teachers in grades three through eight, this study assesses the effects of pathways into teaching in New York City on the teacher workforce and on student achievement. We ask whether teachers who enter through new routes, with reduced coursework prior to teaching, are more or less effective at improving student achievement than other teachers and whether the presence of these alternative pathways affects the composition of the teaching workforce. Results indicate that in some instances the new routes provide teachers with higher student achievement gains than temporary license teachers, though more typically there is no difference. When compared to teachers who completed a university-based teacher education program, teachers with reduced course work prior to entry often provide smaller initial gains in both mathematics and English language arts. Most differences disappear as the cohort matures and many of the differences are not large in magnitude, typically 2 to 5 percent of a standard deviation. The variation in effectiveness within pathways is far greater than the average differences between pathways.

When states prepare their budgets, they usually base revenue and expenditure projections upon forecasts of national and state economic and demographic trends. This brief presents findings of a Center for the Study of the States survey that asked state budget offices what they were assuming for many key variables. The survey obtained 41 state forecasts, most of which were used to prepare official executive budget projections for 1996-97. The most commonly forecasted state-level variables included employment (39 states), nominal personal income (41), Medicaid caseloads (25), Aid to Families with Dependant Children (AFDC) caseloads (29), and prison populations (27). State budget-office forecasts of the national economy were slightly more conservative than those of private forecasters. The median budget forecast of 1996 real GDP growth was 2.4 percent, slightly below the comparable Blue Chip consensus of 2.6 percent. The states' median forecast of 1996 consumer price inflation was 2.8 percent, compared to the median Blue Chip forecast of 2.9 percent. States expect a significant slowdown in corporate profits over the next 2 years, with eight states expecting declines in 1996. States do not appear to suffer from a "provincial illusion": fast-growing states were not more optimistic about the national economy than slow-growing states. Finally, many states are expecting declines in AFDC caseloads in the next 2 years. More than 60 percent of reporting states expect a decline in 1997, with Delaware's anticipated decline of 22 percent by far the largest. Two tables and three figures are included. (LMI)

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.

Almost a quarter of entering public-school teachers leave teaching within their first three years. High attrition would be particularly problematic if those leaving were the more able teachers. The goal of this paper is estimate the extent to which there is differential attrition based on teachers' value-added to student achievement. Using data for New York City schools from 2000-2005, we find that first-year teachers whom we identify as less effective at improving student test scores have higher attrition rates than do more effective teachers in both low-achieving and high-achieving schools. The first-year differences are meaningful in size; however, the pattern is not consistent for teachers in their second and third years. For teachers leaving low-performing schools, the more effective transfers tend to move to higher achieving schools, while less effective transfers stay in lower-performing schools, likely exacerbating the differences across students in the opportunities they have to learn