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The equivalent of 16-18 weeks of study in college, and graduate level statistics.
The principal aim of this course is to prepare graduate and doctoral students to use the concepts and methods associated with quantitative social science research. In specific, the examples used in this course aim to prepare students as they may seek to conduct original research on education issues. Examples draw from primary, secondary, and post-secondary education contexts.
A related aim is to help students grow as savvy discussants and consumers of quantitative research. In this course students will have opportunities to build thier critical reasoning and analytical skills. Though this course does not provide an exhaustive introduction to the entire field of statistics, it provides a thorough overview of topics and techniques often taught in first semester graduate statistics.
In this course we will view statistics as a set of tools that helps researchers examine the world. We will look closely at how research and statistics helps us produce and disseminate new knowledge about how the world works. "How the world works" is a broad phrase meant to include sub-topics such as "how people behave," "how organizations react to policy," "how we can make data informed decisions about organizational management," "how we can evaluate program performance."
This course also provides examples in multiple formats. There is an emphasis on the following tool sets:
1) Pen or pencil and paper - It is important to have an ability to execute rudimentary statistical analyses using simple tools such as a pen, pencil, paper, graph paper, and a calculator.
2) Stata - This software is a widely used statistical computing package in education and social science research. Besides presenting examples in multiple platforms side-by-side, this course presents most examples in Stata. Through this course, students will also learn to use this popular statistical computing package.
3) Python - The Python programming language is a free platform that provides an opportunity to show how we can execute many of the statistical techniques taught through this course. This course presents many examples using the Python programming language. Thus, through this course, students will also learn the rudiments of Python as a programming language.
4) Spreadsheets - Spreadsheets (such as, Microsoft Excel, Google Sheets, Apple's Numbers, and others) are a popular, widely available tool, that provide convenient platforms in which we can easily show many of the statistical techniques taught through this course. This course uses a spreadsheet platform to show many of the statistical techniques taught through this course.
In most cases, this course will show each statistical technique in many of the above tool sets. By presenting and (re)presenting - multiple times - each statistical technique again-and-again in multiple platforms, this course provides multiple and thorough opportunities to see how to execute the techniques. This repetitive approach serves to ensure that students gain exposure to the core concepts in multiple and related ways. This repetitive approach is intentional and it aims to promote learning retention.