quantitative (surveys, student achievement measures) data at the district and/or school levels.
Sub-study one focused on the types and nature of data use by principals in their decision making; district influences on data-informed decision making by principals; and the relationship between school data use and variability in student achievement.
Sub-study two focused on data use and support for data use in schools and at the district level, along with case studies of six site-visit schools identified from our surveys as high data-use schools.
While our research questions varied for each analysis, they all employed the Ikemoto and Marsh framework as a common organizer for analysis and discussion. The discussion that follows integrates findings from each sub-study where appropriate. Method Sub-study one. Interview data collected from 27 principals during the second round of site visits provided the qualitative evidence for this sub-study. While these interviews were relatively open-ended, our analysis of them was explicitly guided by the framework described above. Our quantitative evidence consisted of responses collected from 3,969 teachers and 107 principals during the first round of surveys (for a response rate of approximately 70%). The school was the unit of analysis. Data from each of the 107 schools included responses from the principal and seven or more teachers. Five questions on the principal survey asked about the extent of their districts‘ approach to data use; four questions inquired about principals‘ own approach to data use; and two questions on the teacher survey asked teachers about their principals‘ approach to data use. Data about annual levels of achievement in literacy and mathematics provided the final source of evidence for this analysis. These data, obtained from each school's website, derived from state testing programs. We explored the relationship between variations in data use and student achievement using average annual achievement measures. Following Linn‘s (2003) advice for generating stable achievement measures, we represented each school‘s performance by the combined mathematics and language scores for all grades tested, averaged over three years. We also examined mathematics and language scores separately. We did not select schools for sub-study one on the basis of their data-use practices. Rather, we selected them to represent the normal distribution of schools on such variables as size, student SES, and school level, but weighted more heavily in favor of schools serving high-needs students. We assume that the data-use practices portrayed by our data are typical of many schools across the country. Sub-study two. Here we examined what district administrators (e.g., superintendents, assistant superintendents, curriculum and assessment directors) from the 18 site-visit districts had to say about data use for decision making at the district and
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