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Course Objectives:

The purpose of this course is to teach students the principles of research design, data collection, data analysis, and data presentation employed in empirical social science research. By the end of this course you will become skilled in the collection, presentation, analysis and interpretation of numerical data relevant to a great many social issues. In general, the course stresses research design and research skills more than statistical calculation. Although much of the course is devoted to quantitative statistical methods used by political scientists in their research, these are research skills which you should find valuable in a variety of academic and non-academic settings. Among the things student will learn to do in this course are:

collect and analyze social and political statistical data available from reference and internet sources.
assess the reliability and validity of social indicator data and quantitative social science research.
understand descriptive statistics: mean, mode, median, standard deviation, percentages, and sampling error, levels of measurement and units of analysis.
how to present numerical data effectively using charts, tables and graphs.
interpret, use, and evaluate social science surveys.
use spreadsheet software to analyze data.
prepare a literature review on a social science research question.
write a research report based on statistical data.
understand the general principles of research design: assessing causation, spurious relationships, bivariate analysis.
interpret multivariate regression analysis.

Approach to this course:

More so than other social science statistics or methodology courses, this course will stress a variety of practical statistical and computer skills that you will find of use both in your other coursework and future career. Most of the laboratory assignments and the initial two-thirds of the course will teach you how to find, analyze and present numerical information in an efficient and informative manner.

A major component of the course will be the Data Profile Assignment, comprising 50% of the final grade. In addition, 10% of the assignment grade will be based on a grade given to the draft of the assignment at the end of the fifth and tenth week of the semester. You must maintain a link to the draft of the assignment on your course web page (as will be done with the homework and laboratory assignments.)

The remaining 50% percent of your grade will be based on the completion of a series in-class, homework and laboratory assignments, class attendance and class participation. The participation will include some formal in-class presentations. Most of these assignments will be stored as Word or Excel spreadsheet files on your ISU website drive and your home page will include links to each of the files. They will be graded at least three times (probably more, and not at any announced time) during the semester, the last time at the beginning of exam week.

Each student will be responsible for maintaining a home page with active links to each of the assignments at all times.

There will be no examinations.

Your grades are posted on the web. Login with your university ULID and the password.

Course Links:

Plagiarism, Attendance and Expectations.

You are expected to show up for all classes and laboratory sessions. You are responsible for any material and assignments for classes that you miss. Each absence after the first week of class reduces your overall grade by 1 point, but these can be made up by doing an extra assignment within one week after returning to class. If you miss five classes before the week eight withdrawal date, you will be expected to withdraw from the course.

All students in the course are expected to know what plagiarism is, why it is wrong and how to avoid doing it. All the written work submitted for the course must reflect each student’s own original efforts, any portion of the written assignments, including portions that may have been prepared by other members of the class, that is not a student’s own original effort must clearly acknowledge the source. All instances of plagiarism will be severely penalized and reported to the Student Judicial Office.