http://ies.ed.gov/ncee/whatsnew/For the latest in events, developments, and updates to the NCEE website, check back here often.en-useducationstatisticsdata access toolslibrariesschoolscolleges11/16/2009http://ies.ed.gov/ncee/pubs/2009013.aspReports in this series are designed for use by researchers, methodologists, and evaluation specialists to provide guidance in resolving or advancing challenges to evaluation methods. For RCTs of education interventions, it is often of interest to estimate associations between student and mediating teacher practice outcomes, to examine the extent to which the study’s conceptual model is supported by the data, and to identify specific mediators that are most associated with student learning. ]]>10/13/2009http://ies.ed.gov/ncee/pubs/20094065.asp10/13/2009http://ies.ed.gov/ncee/pubs/20090049.aspThe Evaluation of Enhanced Academic Instruction in After-School Programs: Final Report, includes two parallel impact studies, a math program study ("Mathletics" developed by Harcourt School Publishers) and a reading program study ("Adventure Island" developed by the Success for All Foundation) in which students attending an afterschool program are assigned by lottery to either receive the structured academic programming or the after-school programming regularly offered. For each academic program, the evaluation design allows for information about the one-year impact in the first and second years of operation as well as the two-year impact in which the program was offered to students for two consecutive years. Data on after-school staff characteristics, program implementation, and student outcomes were collected in the first and second years in 27 centers (12 providing the reading program and 15 providing the math program).]]>9/29/2009http://ies.ed.gov/ncee/pubs/20094077/index.aspReports in this series are designed for use by researchers, methodologists, and evaluation specialists to provide guidance in resolving or advancing challenges to evaluation methods. This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the finite-population model) or randomly selected from a vaguely-defined universe (the super-population model). Appropriate estimators are derived and discussed for each model. Using data from five large-scale clustered RCTs in the education area, the empirical analysis estimates impacts and their standard errors using the considered estimators. For all studies, the estimators yield identical findings concerning statistical significance. However, standard errors sometimes differ, suggesting that policy conclusions from RCTs could be sensitive to the choice of estimator. Thus, a key recommendation is that analysts test the sensitivity of their impact findings using different estimation methods and cluster-level weighting schemes.]]>8/31/2009http://ies.ed.gov/ncee/tech_methods/index.aspImpacts of Comprehensive Teacher Induction: Results from the Second Year of a Randomized Controlled Study, compares outcomes of teachers offered intensive induction activities with full-time mentors to teachers with less intensive, less structured induction activities using an experimental study design. This second report includes information from 10 districts in which beginning teachers were offered one year of comprehensive induction services and 7 in which beginning teachers were offered two years of comprehensive induction services. For both sets of districts, findings are reported on support services received by teachers and student test scores during the teachers' second year in the classroom and teacher retention data collected at the beginning of teachers' third year. ]]>8/31/2009http://ies.ed.gov/ncee/pubs/20094072/index.asp5/4/2009http://ies.ed.gov/ncee/pubs/20094032/4/6/2009http://ies.ed.gov/ncee/pubs/20094040.aspEvaluation of the DC Opportunity Scholarship Program: Impacts After Three Years, measures the effects of the Program on student achievement in reading and math, and on student and parent perceptions of school satisfaction and safety.]]>4/3/2009http://ies.ed.gov/ncee/pubs/20094050/Impact Evaluation of the U. S. Department of Education's Student Mentoring Program, compares school engagement, academic achievement, delinquent behavior, and prosocial behavior of students in the fourth through eighth grades who were randomly assigned to either receive or not receive school-based mentoring from one of the U. S. Department of Education's Student Mentoring Program grantees. ]]>2/25/2009http://ies.ed.gov/ncee/pubs/20094047/Achievement Effects of Four Early Elementary School Math Curricula: Findings from First Graders in 39 Schools, reports on the impacts on first-grade student achievement of four math curricula which were selected to represent diverse approaches to teaching elementary school math in the United States. ]]>2/24/2009http://ies.ed.gov/ncee/pubs/20094052/index.aspEffectiveness of Reading and Mathematics Software Products: Findings from Two Student Cohorts, reports on the impacts on student achievement of a second year of use of selected software programs in 1st grade reading, 4th grade reading, 6th grade math, and algebra I -- as a group and for ten individual products.]]>2/17/2009http://ies.ed.gov/ncee/pubs/20094041/index.aspAn Evaluation of Teachers Trained Through Different Routes to Certification, compares the achievement of elementary school students in the same grade, at the same school who were randomly assigned to teachers who chose to be trained through different routes to certification -- traditional education school routes that complete coursework prior to becoming the teacher of record and alternative routes. ]]>2/9/2009http://ies.ed.gov/ncee/pubs/20094043/index.aspThe Late Pretest Problem in Randomized Control Trials of Education Interventions, by Peter Schochet, addresses pretest-posttest experimental designs that are often used in randomized control trials (RCTs) in the education field to improve the precision of the estimated treatment effects. For logistic reasons, pretest data are often collected after random assignment, so that including them in the analysis could bias the posttest impact estimates. Thus, the issue of whether to collect and use late pretest data in RCTs involves a variance-bias tradeoff.. This paper addresses this issue both theoretically and empirically for several commonly-used impact estimators using a loss function approach that is grounded in the causal inference literature. The key finding is that for RCTs of interventions that aim to improve student test scores, estimators that include late pretests will typically be preferred to estimators that exclude them or that instead include uncontaminated baseline test score data from other sources. This result holds as long as the growth in test score impacts do not grow very quickly early in the school year. ]]>12/1/2008http://ies.ed.gov/ncee/pubs/20094033.asp11/20/2008http://ies.ed.gov/ncee/pubs/20094036.asp