Quantitative Analysis - Applied Inferential Statistics
Updates in Progress
Minor revisions to the curriculum are in progress for the next edition of the course. General topics will remain the same, but repos will be re-organized and additional resources will be provided. Existing resources should not be considered current if their date is for 2017.
This site is for sections of SOC 4015 and SOC 5050 taught by Assistant Professor Christopher Prener .
This course provides an introduction to applied statistical analysis for both undergraduate and graduate students with an emphasis placed on statistical techniques that are most common in the sociological literature. The statistical techniques introduced include measures of central tendency and dispersion as well as measures of bi-variate association. Multivariate statistical analyses are also introduced along with essential skills for cleaning data, creating plots, and reporting results. While the examples may be specific to the social sciences, the theories and skills that are covered are broadly applicable across academic disciplines.
This course has four intertwined objectives. After completing the course, students will be able to:
Fundamentals of inferential statistics: Describe the use of various statistical tests, their requirements and assumptions, and their interpretation; execute these tests both by hand and programmatically using
Fundamentals of data analysis: Perform basic data cleaning and analysis tasks programmatically using
Rin ways that support high quality documentation and replication.
Fundamentals of data visualization: Create and present publication quality plots programmatically using
Quantitative research synthesis: Plan, implement (using
R), and present (using
knitras well as the word pressing and presentation applications of your choice) a research project that uses linear regression to answer a research question.
Like many things in academia, the “final” product here is the result of others’ hard work as well as my own. I owe gratitude and thanks to Hadley Wickham , whose data science tools are heavily featured as part of this course. I also draw significant inspiration from Jenny Bryan , whose Stat 545 course has served as a significant resource and a model for my own teaching.
Finally, I want to acknowledge my now nine semesters’ worth of research methods students, whose collective feedback and experiences have helped dramatically reshape the course content and how I teach. I am immensely grateful for each of your contributions to my methods courses.