Abstract

The Conover Solution is a nonparametric method used to analyze relative growth in students' achievement on state tests administered on two or more occasions. However, there has been very little research assessing the robustness of this method in the presence of missing data. Using vertically scaled and non-vertically scaled data from the math portion of a statewide assessment for grades 4-7, I compare results from listwise deletion and multiple imputation across the residual gain score model, the simple gain score model, and the HLM-NPAR model. In these approaches, I study differences by gender and race in two-level models and then extend the modeling to a three-level model that incorporates school-level random effects. The results are similar across missing data and the modeling approaches for both gender and race. These results hold across multiple cohorts. In addition, there are school-level effects. The results do not vary across missing data or modeling approaches. I discuss implications for these findings and guidelines for practitioners.

Degree

PhD

College and Department

David O. McKay School of Education; Educational Inquiry, Measurement, and Evaluation

Rights

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