title: Strategies for Improving Power in School-Randomized Studies of Professional Development.
abstract: OBJECTIVE
  Group-randomized designs are well suited for studies of professional development because they can accommodate programs that are delivered to intact groups (e.g., schools), the collaborative nature of professional development, and extant teacher/school assignments. Though group designs may be theoretically favorable, prior evidence has suggested that they may be challenging to conduct in professional development studies because well-powered designs will typically require large sample sizes or expect large effect sizes. Using teacher knowledge outcomes in mathematics, we investigated when and the extent to which there is evidence that covariance adjustment on a pretest, teacher certification, or demographic covariates can reduce the sample size necessary to achieve reasonable power.
  
  
  METHODS
  Our analyses drew on multilevel models and outcomes in five different content areas for over 4,000 teachers and 2,000 schools. Using these estimates, we assessed the minimum detectable effect sizes for several school-randomized designs with and without covariance adjustment.
  
  
  RESULTS
  The analyses suggested that teachers' knowledge is substantially clustered within schools in each of the five content areas and that covariance adjustment for a pretest or, to a lesser extent, teacher certification, has the potential to transform designs that are unreasonably large for professional development studies into viable studies.
authors: College of Education, Criminal Justice, and Human Services, University of Cincinnati, Cincinnati, OH, USA ben.kelcey@gmail.com.
authors: Ben
authors: B
authors: Kelcey
authors: Educational Testing Service, Princeton, NJ, USA.
authors: Geoffrey
authors: G
authors: Phelps
keywords: cluster-randomized trials; covariance adjustment; design of experiments; group-randomized designs; power; precision; pretest–posttest; professional development; teacher development; teacher knowledge