There are two serious methodological problems in the research literature on school effectiveness, the ecological problem in the analysis of aggregate data and the problem of not controlling for important confounding variables. This dissertation corrects these errors by using multilevel modeling procedures, specifically Hierarchical Linear Modeling (HLM), and the Canadian Trends in International Mathematics and Science Study (TIMSS) 2007 data, to evaluate the effect of school variables on the students’ academic achievement when a number of theoretically-relevant student variables have been controlled. In this study, I demonstrate that an aggregate analysis gives the most biased results of the schools’ impact on the students’ academic achievement. I also show that a disaggretate analysis gives better results, but HLM gives the most accurate estimates using this nested data set. Using HLM, I show that the physical resources of schools, which have been evaluated by school principals and classroom teachers, actually have no positive impact on the students’ academic achievement. The results imply that the physical resources are important, but an excessive improvement in the physical conditions of schools is unlikely to improve the students’ achievement. Most of the findings in this study are consistent with the best research literature. I conclude the dissertation by suggesting that aggregate analysis should not be used to infer relationships for individual students. Rather, multilevel analysis should be used whenever possible.

There are two serious methodological problems in the research literature on school effectiveness, the ecological problem in the analysis of aggregate data and the problem of not controlling for important confounding variables. This dissertation corrects these errors by using multilevel modeling procedures, specifically Hierarchical Linear Modeling (HLM), and the Canadian Trends in International Mathematics and Science Study (TIMSS) 2007 data, to evaluate the effect of school variables on the students’ academic achievement when a number of theoretically-relevant student variables have been controlled. In this study, I demonstrate that an aggregate analysis gives the most biased results of the schools’ impact on the students’ academic achievement. I also show that a disaggretate analysis gives better results, but HLM gives the most accurate estimates using this nested data set. Using HLM, I show that the physical resources of schools, which have been evaluated by school principals and classroom teachers, actually have no positive impact on the students’ academic achievement. The results imply that the physical resources are important, but an excessive improvement in the physical conditions of schools is unlikely to improve the students’ achievement. Most of the findings in this study are consistent with the best research literature. I conclude the dissertation by suggesting that aggregate analysis should not be used to infer relationships for individual students. Rather, multilevel analysis should be used whenever possible.

Health studies often rely on aggregated instead of individual-level data to protect patient privacy. However, aggregated data are subject to the modifiable areal unit problem (MAUP), meaning results of statistical analyses may differ depending on the data's scale and areal unit. Past studies have suggested MAUP is context-specific and analyzing multiple spatial scales may provide richer understandings of examined phenomena. More research is needed to understand the role of scale and areal unit in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at more spatially refined exposure estimates. We identified surface mines using Landsat imagery and geocoded birth records. Airsheds, used to quantify the influence area of potential airborne pollutants from surface mining activity, were built using HYSPLIT4. The frequency values of each airshed that intersected each geocoded birth record were summed. These cumulative frequency airshed values were then aggregated. Finally, we implemented multiple regression models, each at a different scale, to examine associations between airsheds and birth outcomes. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and reduced birthweight at each scale. / Master of Science / Health studies often rely on data that has been grouped together within political boundaries (e.g. counties) instead of individual-level data to protect patient privacy. However, results from analyses using grouped data may differ depending on the data's scale and areal unit, which describes the modifiable areal unit problem (MAUP). Past studies have suggested MAUP is specific to the situation being analyzed and examining multiple scales may provide richer understandings of the situation. More research is needed to understand the role of scale and areal unit choice in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at finer scales. Surface mines were identified using satellite images, and we identified the locations of birth records using the mother's home address. Airsheds, used to determine the influence area of airborne pollutants from surface mining activity, were created. We then used statistical models, to examine associations between airsheds and birth outcomes at four spatial scales. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and decreased birthweight in grams at each scale.