Assessing Aircraft Noise Conditions Affecting Student Learning, Volume 2: Appendices (2014) / Chapter Skim
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APPENDIX D Development of Alternative Research Designs D.1.
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2. School Selection School information will be obtained from the CCD.
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Pros Cons within the budget. • Provides quantitative answers for the first five research questions.
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2. School Selection School information will be obtained from the CCD as noted in the Overview.
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school conducts most of the classes in temporary buildings with little sound insulation. We will also examine the role of any limitation in the aircraft noise modeling, such as, gross over- or under-prediction of aircraft noise levels due to incorrect model input assumptions.
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The analysis to derive the answer is the same as the Datum and Alternative 1.
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4. Noise Measure We will have same INM input files and ETMS operational data as the Datum and Alternative 1 and can calculate the same noise metrics as we proposed in those other candidates.
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D.4. Alternative 3 -- Macro-Analysis (Top 30 Airports)
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For the case study, we will choose a single airport; one with a high frequency and mix of aircraft operations with several schools nearby. Los Angeles (LAX)
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Case Study The case study is the same as Alternative 2. Plan Assessment Pros Cons • Data gathering and macro-analysis workloads fit within the budget.
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disrupts the classroom environment; looking for confirmation of the correlation finding like Alternatives 2 and 3.
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vicinity of the airport selected. As with Alternatives 2 and 3, this part of the plan requires IRB, school district, school and parent approval processes.
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• How do teachers who are regularly exposed to aircraft noise at school differ from teachers who are not exposed to aircraft noise at school with respect to inhibitory factors including stress, health difficulties, noise annoyance, absenteeism, and vocal strain? • According to students and teachers, how, if at all, does aircraft noise influence teaching and learning?
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• There is no "state" effect. Test scores are standardized to the same mean and standard deviation in each state in order to enable aggregation of results across states.
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