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Using State Longitudinal Data Systems for Education Policy Research : The NC Experience

Using State Longitudinal Data Systems for Education Policy Research : The NC Experience. Helen F. Ladd CALDER and Duke University Caldercenter.org ladd@pps.duke.edu. Examples of research in NC by CALDER researchers. Charter schools (Bifulco and Ladd)

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Using State Longitudinal Data Systems for Education Policy Research : The NC Experience

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  1. Using State Longitudinal Data Systems for Education Policy Research : The NC Experience Helen F. Ladd CALDER and Duke University Caldercenter.org ladd@pps.duke.edu

  2. Examples of research in NC by CALDER researchers • Charter schools (Bifulco and Ladd) • How charter schools affect student achievement, achievement gaps and racial sorting. • Achievement gaps (Clotfelter, Ladd and Vigdor) • By student cohort grades 3-8; by race. • Distribution and movement of teachers (Clotfelter, Ladd Vigdor and Wheeler) • Descriptive with focus on high poverty schools • Public policies and the distribution of teachers (Clotfelter, Ladd, Vigdor and Glennie) • Salaries, alternative salaries and working conditions • $1800 bonus program program for teachers of math , and special education in low performing middle and high schools • The state’s accountability system

  3. NC research (cont) • Teacher credentials and student achievement (Clotfelter, Ladd, and Vigdor) • Cross sectional analysis – 5th graders • (JHR, 2006) • Longitudinal analysis – grades 3-5. • (CALDER web page and shorter version forthcoming in EER) • Cross subject analysis – high school. • Work in progress

  4. Strengths of NC Data • Student test scores • Based on tests that are linked to the state’s standard course of study • Grades 3-8 End-of-Grade (EOG) tests in math and reading • High School End-of-Course (EOC) tests in multiple subjects – e.g. algebra I, English I, biology, • Available since the mid 1990s. • Test scores can be linked by student over time

  5. Strengths (cont.) • Other student data • Standard demographic data Race, gender, free lunch status, LEP • Education level of the parents (Some concerns) • Survey responses (connected to the test) • E.g. homework, use of computer, TV watching • Student addresses in some districts and in some years

  6. Strengths (cont.) • Teachers • Wide array of credentials • Teacher licensure test scores • Licensure – regular, lateral entry, other • National board certification (NC has high percentage) • Graduate degrees and when they got them • Undergraduate college • Certification by subject • Teacher salary data • Can follow teachers over time

  7. Limitations of NC data • Linking of student to their specific teacher is possible but imperfect. • Grades 3-5. About 85 percent match • Grades 6-8. Poor match • High school- courses with EOC tests Good match – 75 percent • Incomplete home address data – relevant for choice and charter school studies • No link yet with higher education data

  8. Basic “value-added” model of student achievement (grades 3-5) A student’s achievement in year t is a function of: • her achievement in the previous year (accounts for the cumulative nature of achievement) • teacher characteristics and credentials (e.g. gender, years of experience, teacher test score) • classroom characteristics (e.g. class size, profile of students in the class) • student characteristics (e.g. race, gender, poverty status)

  9. Achievement models 1-3 Achievement levels (Ait) or achievement gains (Ait- Ai,t-1 ) No fixed effects • Levels. Upward biased coefficients because of teacher- student sorting; potential bias from lagged achievement on RHS. With school fixed effects 2. Levels. Better, but problem of sorting within schools remains and potential bias from lagged achievement; direction of bias unclear (see our JHR paper) 3. Gains. Downward bias from misspecified persistence effect.

  10. Models 4 and 5 (preferred) Full use of the longitudinal aspect of the data With student fixed effects 4.Levels (but no lagged achievement). Lower bound estimates of effects of teacher credentials 5. Gains. Upward bound estimates of effects of teacher credentials

  11. Interpretation of results All test scores are normalized to have a mean of 0 and a standard deviation of 1. => coefficients of interest will be small. E.g. 0.05 Point of reference . Compare test score of a typical student whose parent has a high school degree but no college degree to the test score of a student whose parents are college educated. Estimated effect size = -0.11 Question : are the effects of teacher credentials on student achievement large enough to counter this negative effect of relatively low parental education?

  12. Effects of credentials:Teacher experienceCoefficients from preferred model (model 5)All are statistically significant

  13. Other teacher characteristics and credentials (math only, preferred model) • Teacher test score – linear form 0.015 • Quality of undergraduate institution Competitive 0.010 (Base = noncompetitive) • License Other license -0.059 (Base = regular license) (Note: all coefficients are statistically significant)

  14. Teacher credentials Master’s degree (math: preferred model) • Master’s degree -0.007 * unexpected Disaggregated specification MA before teaching -0.009 MA 1-5 years into teaching -0.005 MA 5+ years into teaching -0.010* * indicates that the coefficient is statistically significant at the 0.05 level. .

  15. Teachercredentials National Board Certification(math: preferred model NBCT-2 0.055 NBCT-1 0.061 NBCTcurr 0.046 NBCTpost 0.041

  16. Are these effects large or small? • Teachers have bundles of characteristics Comparison of teacher with average/strong credentials compared to one with weak credentials math + 0.15 to 0.20 reading +0.08 to 0.12 • Magnitudes are large relative to the estimated effects of reducing class size by 5 students. • Sufficient to offset much of the effect of weak parental education – particularly for math.

  17. Teacher credentials at the high school level – work in progress • Use NC EOC test scores – with students linked to their teachers by subject • Same problem of teacher and student sorting as at the elementary level • Additional challenge – students self select into courses

  18. Our approach – cross-subject model with student fixed effects • Start with all students in grade 10 in four different cohorts (2000-2003) • Focus on the subjects typically taken in 9th or 10th grade (algebra 1, English 1, biology, geometry, and ELP)– but include all test scores in whatever grade the course (and test) was taken • Define unit of observation as the student by subject • Include student fixed effects – to control for unmeasurable characteristics of students such as their ability

  19. Preliminary illustrative results: Teacher credentials matter • Teacher experience -- mainly first 2 years • Teacher licensure -- negative effect for lateral entry teachers • Certification by subject -- Teachers certified in subject > those in related subject> than other certified teachers • NBCT – more effective pre-NBCT; even more effective after certification. • Teacher test scores. Similar to elementary school. And more – to come. Stay tuned and check the CALDER web page later this summer.

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