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1 Sep 2007
Report

Partitioning NAEP Trend Data

Fundamental to statistical analyses is the comparison of means of one variable from two or more populations. Population samples may be constructed (i.e., experimental and control groups), or they may be natural groupings (i.e., students at a particular grade in different years). If the populations are similar, the mean comparisons are straightforward; if not, the question arises as to whether the mean differences are due to differences in the variable or differences in the populations. Partitioning analysis is a way of distinguishing between these differences.

This paper is a demonstration of how partitioning analysis can be used to help separate changes in reading and mathematical proficiency from changes in school populations over assessment years. NAEP reading and mathematics trend data were readily available from published NAEP reports. Subgroup means were published separately for White, Black, Hispanic, and “Other” students. We selected 13-year-old students from four assessment years as sufficient for this demonstration.

Partitioning analysis separates the difference between two means into three parts: proficiency effect, population effect, and joint effect. The proficiency effect is the change in means attributable to changes in student ability, the population effect is the part attributable to population changes, and the joint effect is the part attributable to the way that the population and proficiency work together. Partitioning analysis makes it simple to compute a well-known statistic, the standardized mean, which estimates what the mean would have been if the percentages of the various subgroups had remained the same.

PDF icon Partitioning NAEP Trend Data

Related Projects

Project

NAEP Validity Studies (NVS) Panel

AIR formed the NAEP Validity Studies Panel in 1995 under contract with the National Center for Education Statistics. The panel provides a technical review of NAEP plans and products and identifies technical concerns and promising techniques worthy of further study and research.

Further Reading

  • NCES Data R Project- EdSurvey
  • Pilot Instructor Rater Training: The Utility of the Multifacet Item Response Theory Model
  • NAEP Trends: Main NAEP vs. Long-Term Trend
  • NAEP Validity Studies Panel Responses to the Re-analysis of TUDA Mathematics Scores
  • Reading Motivation, Reading Achievement, and Reading Achievement Gaps: Evidence from the NAEP 2015 Reading Assessment
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