Understanding the challenges associated with conducting secondary analysis of large-scale assessment data is important for identifying the strengths and weaknesses of various statistical models, and it can lead to the improvement of this type of research. The challenges encountered in the analysis of assessment data from subpopulations may be of particular value for this purpose. To date, few studies have discussed the problems associated with the secondary analysis of large-scale assessment data.
This article examines the impact of the School Achievement Indicators Program (SAIP) on the educational system. SAIP is a Canada-wide program involving the large-scale assessment of student achievement in mathematics, science, reading, and writing. Data were collected using semi-structured interviews conducted with the SAIP's jurisdictional coordinators (n = 20) and from surveys sent to participating school boards across Canada (n = 147). SAIP's impact is described using two dimensions: intrinsic versus extrinsic and positive versus negative.