Motivation and Synopsis
course provides an introduction to modern statistical methods for comparing
distributions. Social science
research relies on these methods any time comparisons are made between
groups. When the attribute of interest is continuous, for example racial
differences in life expectancy, or earnings differences between men and
women, the traditional methods make comparisons in terms of means, medians
and standard deviations. Traditional methods, however, provide a weak and
unnecessarily restrictive framework for comparison. Consider the earnings
distribution in the
With the emergence of Exploratory Data Analysis (EDA, Chambers, et al 1983; Tukey 1977) and the development of high speed computing and graphical user interfaces, there has been a movement towards more nonparametric and distribution-oriented analytic methods. A prominent feature of these methods is the use of graphical displays. Graphics exploit the power of our visual senses to convey information in a direct way.
Objectives of the Course
In this course we will start from scratch and introduce practical nonparametric, distribution-oriented and graphical analytic methodological tools to aid social science research.
We will follow the topics of traditional methods courses: univariate and multivariate summaries; simple and multivariate regression. These will be supplemented by quantile regression, methods for categorical data and an overall emphasis on distributional comparisons.
These methods aim to bridge the gap between exploratory tools and parametric restrictions. The goal is to present the concepts, theory and practical aspects of the methods in a coherent fashion, with a minimum of statistical prerequisites.
The course will have an applied focus on the development of tools for research in the social sciences. The course will involve the practical application of the ideas and their implementation through statistical software to make them accessible to social scientists.
This course is part of the curriculum of the new Center for Statistics and the Social Sciences (CSSS), with funding from the University Initiatives Fund. The CSSS is includes faculty members from the Department of Statistics and a broad-range of social science disciplines including Anthropology, Economics, Geography, Political Science, and Sociology. This curriculum is been developed to complement and strengthen the quantitative methods course offerings for social science students at both the undergraduate and graduate levels.
Structure of the Course
There will be a two lectures per week. The lecture on Thursday will sometimes be a laboratory session.
Course Requirements and Grades
There will be weekly homeworks and exercises relating to computing and programming. Students will be graded on a scale of 1 to 10 for each homework.
Discussion of homework problems is encouraged. However, each student is required to prepare and submit solutions (including computer work) to the assignments and project on their own; solutions prepared “in committee” are not acceptable. Duplication of homework solutions and computer output prepared in whole or in part by someone else is not acceptable and is considered plagiarism. If you receive assistance from anyone, you must give due credit in your report. (Example: “Since the data are all positive, and skewed to the right, a logarithmic transformation is clearly appropriate as a next step. I thank David Cox for pointing this out to me.”)
I welcome comments or suggestions about the course at any time, either in person, by letter, or by anonymous email. Please feel free to use these ways make comments to me about any aspect of the course.
Use the menu on the top-left of this page to find out more about the course.
STUDENTS WITH DISABILITIES
If you have a disability that requires special testing accommodations or other classroom modifications you need to notify the instructor and the Office of Disabled Student Services as soon as possible. You may contact the DSS office at 543-8925.