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Dec 04, 2024
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BUS 6302 - Statistical Thinking and Data Analytics-Education Leadership3 lecture hours 0 lab hours 3 credits
Course Description
BUS 6302 is an introductory level statistics course intended to strengthen skills in applied statistics in education. This course provides an overview of common statistical techniques used in educational research and clinical practice. Students will learn the importance of using statistics and data analysis to support decision-making and reporting. They will understand the importance of using both quantitative data and qualitative data in an educational setting. They will learn to formulate questions, identify legitimate sources of data, and assess data quality. This course will work on collecting and analyzing data related to MBA/EL culminating project using descriptive statistics, inferential statistics, data analysis, review of research, and survey instruments. Students will also spend time understanding the statistical formulas more deeply around concepts commonly used in education: such as standard deviation, risk ratios, normal distribution, cohort growth, and frequency distributions. Embedded support will be given around WISEdash, WI State Report Cards, National Center for Educational Statistics, and other locally used data monitoring tools. (prereq: none)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Create a plan that ensures a high degree of transparency in the reporting of local measures against standards and benchmarks
- Compare current performance to benchmarks and identify gaps
- Utilize data from organization performance monitoring systems to improve student achievement
- Demonstrate how statistics and analytics can be used to identify patterns and analyze problems
- Determine the root cause of a problem and identify the data needed to help understand and address it
- Apply methods of descriptive inferential, predictive, and evaluative statistics to support decision-making and problem resolution
- Use existing data sources, extract data, and convert it into a useful format to support decision-making and goal achievement
- Identify tools and techniques that support the aggregation and disaggregation of organization performance data to support decision-making on multiple levels
- Identify data sources and evaluate data quality
- Apply multiple techniques to analyze and define the root cause of a problem
- Assist others in developing a “statistical thinking” mindset and incorporating data analysis in appropriate tasks
Prerequisites by Topic
Course Topics
- Qualitative vs. quantitative, descriptive vs. inferential
- Central tendency, normal distributions, Bell curve, standard deviation
- Calculating risk ratios, survey development (qualitative) - interviews/focus groups/observations
- Survey development and sampling, survey data analysis, qualitative coding, frequency distributions
- Reporting results, data ethics, formal standardized testing, and norms
- Strategic Use of Data and Multiple Measures of Data (Bernhardt)
Coordinator
Dan Pavletich
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