Suzanne D Withers
Introduces elementary spatial statistics and advanced statistical techniques in quantitative human geography. Methods reviewed include geographic applications of multiple regression analysis, spatial statistics and spatial autocorrelation, geographically weighted regression, factor analysis, discriminant analysis, and logistic regression. Prioritizes the interpretation and application of methods. Prerequisite: either GEOG 317 or GEOG 326.
This course is an introduction to quantitative methods in geographic research. The goal of this course is to provide a practical understanding of the application of quantitative analysis to geographic problem solving. Emphasis is placed on the application of appropriate methods to analyze geographic data, the appropriate procedures for research design, and the interpretation of research results. The student will gain practical experience via weekly assignments that require the use of statistical methods for specific geographic research questions using SPSS (Statistical Package for the Social Sciences) software. The course is divided into three sections: (1) Univariate descriptive statistics; (2) Univariate inferential statistics (parametric and nonparametric); and (3) Multivariate inferential statistics.
Student learning goals
General method of instruction
The general method of instruction follows a lecture format (4 days a week). Geographic research examples are used to introduce the various statistical procedures. Once a week (Wednesdays) the class meets in a computer laboratory for practical sessions that are designed to give the student an opportunity to apply the methods learned in class.
Geography 326 or equivalent is recommended, but not required.
Class assignments and grading
Due to the emphasis on the practical application of quantitative methods, there are 8 assignments during this course. Although each assignment addresses a distinct research question, all involve the following procedure: (1) selecting the appropriate quantitative method for the research question; (2) analyzing data using SPSS; (3) interpreting the results of the empirical analysis; and (4) writing a short report to summarize the findings within the context of the substantive issue.
Student evaluation is based on the following: 70%: 8 practical assignments / reports; 10%: 1 midterm examination; 20%: 1 final examination.