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sociological research methods to collect data. Students will be able to analyze data with appropriate sociological data analysis methods. Printable pdfCourse-level Learning OutcomesCourse-level outcomes reflect what students will learn by the end of the course. Certainly, each course will reflect either an introduction to a concept, practice at gaining competence in this outcome, or demonstration that a student knows and/or owns the knowledge/concept/skill/ability. Instructors should include a range of

problem statement, hypothesis or project goal. Logical methodology. Easy to understand. Easy to understand display of raw data and data analysis. Statistical analysis must be valid. For engineering projects, often a second improved prototype but not absolutely required. Very clear discussion of data analysis/results related to the real world problem or issue. Conclusion tied directly to the problem statement or hypothesis. Developed something new or discovered a new relationship in science. Other

Student Retention through Multiple Logistic Regression Analysis Meghan Bonafilia In this presentation, we will explore a data set with over 4000 observations concerning first year students at PLU and their likelihood of staying at PLU for the next year. Using logistic regression for analysis, we will review different model selection processes and explore several factors like: GPA, race, whether or not a student is first generation, whether or not a student lives on campus, and more. 2:00pm

Student Retention through Multiple Logistic Regression Analysis Meghan Bonafilia In this presentation, we will explore a data set with over 4000 observations concerning first year students at PLU and their likelihood of staying at PLU for the next year. Using logistic regression for analysis, we will review different model selection processes and explore several factors like: GPA, race, whether or not a student is first generation, whether or not a student lives on campus, and more. 2:00pm

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and Student Retention at Pacific Lutheran University 3:30pm Logan Margo Data Analysis of Minority Suspension Disparities in School Districts 4:00 – 4:30pm Break 4:30pm Jackie Lindstrom Principal Components Analysis: Using Math to Extract Underlying Structures in the Iraqi Migration Survey Data 5:00pm Zhanna Kukosh Preparing Students to Succeed in Geometry 5:30pm Kioni Kamau Exploration of Causal Effects and Modeling

. The outcome is to recommend SoN improvement initiatives based on the analysis of assessment/evaluation data and is documented in the comprehensive Program Improvement Plan (PIP). The PIP process consists of the Plan – Do – Study – Act method.  This process occurs throughout the academic year, and discussed in program level faculty meetings as well as the School of Nursing Organizational meetings (SNO meetings).  Program level leads will present a comprehensive report updating the faculty during