Political Analysis in an Experiential/Collaborative Setting

University Of Southern Mississippi, Hattiesburg MS

Investigators

Abstract

Political Science (85) Test data compiled by the University of Southern Mississippi indicate that many political science students frequently need direct assistance in developing their statistical and critical reasoning skills. Political science at USM attracts a large number of female students and students who are from under-represented populations. A survey of USM political science students in the Fall of 1998 indicated that they wanted opportunities to develop greater expertise in using computers to engage in data analysis. The department of political science has responded by offering two new courses in introductory statistics and research methods. These courses are designed to address their needs and provide them with a greater understanding of the practice of science. This project is an adaptation of classroom methods developed elsewhere. The Principal Investigator developed and used a similar approach at Grambling State University. (See "Creating a Critical Thinking Learning Environment: Teaching Statistics to Social Science Undergraduates," PS: Political Science and Politics, 1996, pp. 517-521.) This project is also adapting materials from (1) Beth Chance, "Experience with Authentic Assessment Techniques in an Introductory Statistics course," Journal of Statistics Education, Vol. 5, No. 3, 1997; (2) Sandra Fillebrown, "Using projects in an Elementary Statistics Course for Non-Majors," Journal of Statistics Education, Vol. 2, No. 2, 1994; (3) Gerald Giraud, "Cooperative Learning and Statistics Learning," Journal of Statistics Education," Vol. 5, No. 3 1997; and (4) Gary Smith, "Learning Statistics by Doing Statistics," Journal of Statistics Education, Vol. 6, No. 3, 1998. The project makes extensive use of peer interaction, following the suggestions made by Simon Hooper, "Effects of Peer Interaction during computer based Mathematics Instruction," Journal of Educational Research, Vol. 41, No. 2, 1990, pp. 180-189. We are refining a process for teaching statistical analysis, data analysis skills, and critical thinking to undergraduate students in political science in a way that will have a lasting impact. We are using computer-based technology in the classroom and teaching our students exploratory data analysis techniques. (See for example, John Tukey, Exploratory Data Analysis, Addison-Wesley, 1977; Lawrence Hamilton, Modern Data analysis: A first Course in Applied Statistics, Brooks/Cole Publishers, 1990; James Mullenex, "Box Plots: Basic and Advanced," Mathematics Teacher, 1990, pp.108-112; Frederick Hartwig and Brian Dearning, "Exploratory Data Analysis," Sage Publications, 1979; Peter Barbella, Lorraine Denby, and James Landwehr, "Beyond Exploratory Data Analysis: The Randomization Test," Mathematics Teachers, 1990, pp. 144-149; and Gretchen Davis, "Using Data Analysis to Explore Class Enrollment," Mathematics Teacher, 1990, pp. 104-106.) Our classroom environment is one of both collaborative learning (See, for example, Lois Rubin and Catherine Hebert, "Model for Active Learning: Collaborative Peer Teaching," College Teaching, 1998, pp. 26-30) and peer teaching (See, for example, Brian Keller, Chris Russell, and Heather Thompson, "effects of Student-Centered Teaching on Student Evaluations in Calculus," Educational Research Quarterly, 1999, pp. 79-93) where students work with a partner who is assigned based upon scores on a course pretest. The focus of this course is an experiential learning model. We are employing data sets drawn from two national social and political surveys used by researchers in political science - the General Social Survey (1972 to present) and the National Elections Studies Data sets (1948 to present), preparing samples of each year for use by students both as cross-sectional and longitudinal analysis (the use of "real-life" data sets has been adopted by a number of university faculty including faculty at Virginia Technical University, West Virginia State College, and Hunter College - CUNY). By using the same data that researchers use in their work, we are able to link research to teaching, and prepare our students to eventually design and carry out their own study using these data sources.