EMP 879 C. Laurenson 3 credits
Mentorship in Statistical Analysis
Description
This Mentorship in Statistical Analysis course is designed to be taken concurrently with a doctoral level course in order to complete a research project that requires the design, collection, analysis and presentation of original field data. Students who are working on a research project requiring statistical analysis may also enroll in this course. Participants are mentored through all aspects of proper statistical analysis (including design of the project and data collection, data collection, data analysis, and presentation of the results) using their research project as the guiding vehicle and teaching example. All of the statistical principles and techniques taught will be immediately applied to their research project. University guidelines, the research parameters of the Energy Medicine field, and the guidelines for acceptable statistical practice govern all aspects of the data analysis completed in the research project.
Prerequisite
Doctorate students must be concurrently working on a research project that requires the collection and analysis of data.
Objectives
Course objective is to support the student in the statistical analysis portion of the requirements of a research project representing original work by the student; and in the design, collection, and analysis of data collected from research subjects (field data). The purpose of this course is to mentor the participant through the process of properly designing the study, collecting and analyzing the data, and presenting the findings. That is, the purpose of this course is to guide the participant through all statistical aspects of their research project to ensure that their quantitative analyses are sound and conform to the standards set forth by the Holistic Health field, and the statistics profession.
Using their specific research project as the teaching example, the participants will learn:
- The role statistics plays in quantitative research
- The difference between analytical and enumerative research
- The relationship between the field of statistics and intuitive insight
- Some key principles of variation and their impact in inference
- The difference and relationship between data and information
- How to clearly define the objective of their project, study or experiment
- How to clearly formulate a research hypothesis
- How to statistically design how the project, study or experiment is conducted
- How to determine what data to collect and how to effectively collect it
- How to effectively analyze the collected data and transform it into information
- How to effectively present the findings
- To identify both the extent and the limitations of their research
Topics
- Understanding research as a system, and the role of statistics in that system
- The difference between analytical and enumerative research
- Statistical integrity. The relationship between intuition and statistics
- Elementary principles of variation — a key to effective research and analysis
- The difference and relationship between data and information
- The relationship between sample and population
- Research objectives and hypotheses
- Statistical design of experiments, studies or projects
- Data collection methodologies and elementary metrology
- Elementary survey (questionnaire) design — if appropriate to the research project
- Handling errors and omissions in data
- Graphical analyses of data
- Numerical analyses of data
- Graphical, numerical and other presentations of the findings
- The limitations of research or a study. What the findings say and don’t say.
Texts
- D. Huff, How to Lie with Statistics
- G. Bhattacharyya & R. Johnson, Statistical Concepts and Methods
- G. Box, W. Hunter, J. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building
- William Cleveland, The Elements of Graphing Data
- Edward Tufte, The Visual Display of Quantitative Information
- Edward Tufte, Envisioning Information
- Edward Tufte, Visual Explanations
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