Overview
Description
Statistical methods related to single factor, factorial, repeated measures and split-plot designs in social, life, and environmental sciences with emphasis on statistical programming.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- identify key elements in a prose research question or data description (cases/ experimental units, variables, types of variables/ levels of measurement) and make inferences about implicit causal assumptions and parameter of interest.
- formulate hypotheses symbolically and in writing.
- make analysis plans and combine statistical power analysis and real-world constraints to develop sample size targets.
- construct detailed research action places for choosing statistics, implementing the proposed statistics, and testing the proposed hypotheses.
- devise and defend a plan for managing missing data.
- create outlines for reporting and interpreting results, as well as skeleton tables and graphs.
- identify key elements in output, report accurately and succinctly upon them, and evaluate their implications for hypotheses.
- demonstrate ability to use a reference manager program.
- demonstrate ability to organize a multi-investigator project through file organization and management.
- conduct an independent research project using methods learnt in the course.
- present results from the independent research project to the class.