By Susan Archer (Mitchell College)
Information
Students often take an introductory statistics class to fulfill their college math requirement and do what they need to do to find reasonable success without having to engage deeply with course content--especially if they are not STEM majors. These are the students who need to understand how and why type questions. Introducing them to more advanced methods than traditional hypothesis testing of means and proportions can bring statistics to life in a relatable way. We discuss factor analysis, structural equation modeling for interrelationships between factors, MANOVA to see if differences in factor importance exist for population subgroups, and triangulation with emerging trend phenomenological analysis of qualitative data to help all students (but especially the non-STEM students) see how statistics fits with what they want to know. Examples in real research include evaluation of organizational design using survey data, checking for differences in levels of success for Fortune 500 companies based on CEO demographic characteristics, and understanding why college students choose a particular major.