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Use of quantile regression, analysis at different levels of aggregation, and inclusion of covariates were used to assess how different methodologies produced varying results with contradictory implications for policy interventions regarding the relation between school funding and achievement. Results indicated that significantly different relations existed at various quantiles of the distribution of achievement, both for student and school-level outcomes. Further, significant differences were found when outcomes at the school-level were compared to outcomes for individual students. Finally, the inclusion of SES drastically altered the results of every analysis in this study, indicating the importance of controlling for confounds. Taking all of these findings into account, we find that higher levels of funding at the student-level were associated with smaller gaps in growth in achievement by SES for lower achieving students. The opposite finding was found at the school level, with higher levels of funding at the school level being associated with larger gaps in growth in achievement based on SES. We compare our findings to results of existing studies, framing our results and interpretations thereof in weighted-student funding literature. Discussion on how these findings can be translated into quasi-experimental research designs are also included.

We will examine the developmental dynamics between reading fluency and reading comprehension. Specifically, we will explore the directionality of influences between these constructs, differences in relations dependent on the point in development, and the etiological influences related to the development of the constructs. To preserve the data integrity of the preregistration, only approximate data is available regarding the sample. Longitudinal DIBELS Oral Reading Fluency and MAZE data from grades 2-6 (ages 7-11) will be analyzed for an estimated sample of 1,800 twin pairs from the NATPAT project. Sample demographics are expected to reflect the demographics of the larger NATPAT dataset, which comprises twin pairs that are approximately 82.4% White, 8.2% Black, 6.5% multiple races, and 2.8% other races; 89.3% non-Hispanic, 9.1% Hispanic, and 1.3% of unknown ethnicity; and 48.9% male, 50.5% female, and 0.6% without identifying information about sex.

Do you want to know more about IRB considerations for data sharing? See this white paper on thinking about informed consent language and data use agreements.

Steps to take to check your data for identifiable cases.

An IRB protocol template planning to include data sharing.

A template of an informed consent form that anticipates data sharing.