Data Analysis: Comm 516 p. 1 Data Analysis: Comm 516 Spring 2009 Dr. Mary Beth Oliver Office Hours: 210 Carnegie T: 1-4 863-5552 (0) 235-0646 (H) (sign up) [email protected] And by Appointment Purpose of Course: Many people consider data analysis the heart and soul of quantitative research. It’s a stage of research that can be thrilling and heartbreaking at the same time. For me, data analysis is like opening presents – finally, after a long wait, data analysis lets you look inside the box! But looking inside the box doesn’t mean tearing into the wrapping paper without any delicacy. Data analysis involves strategy, reflection, and interpretation. There are many steps involved in the analysis of data – everything from setting up the data file, deciding the appropriate analysis strategy, interpreting results, and presenting them in the text of a paper. This course is designed to provide you with a broad examination of all of these steps within a social science framework, with an eye toward introducing you to the many types of decisions that data analysis involves and familiarizing you with the tools that are appropriate for addressing a variety of research questions. Because this is an introductory, “survey” course in data analysis, the emphasis in the class will be on decisions and interpretations of data analysis. Consequently, it is important for students in this course to recognize that this class represents an introductory, first look at data analysis. Students who intend to focus on quantitative methods for their dissertations or academic careers would be well advised to understand this course as the start of many, many more statistics classes in their futures that provide more in depth analyses of each of the procedures covered here. Required Texts: Green, S. B., & Salkind, N. J (2007). Using SPSS for Windows and Macintosh: Analyzing and understanding data (5th ed.). Upper Saddle River, NJ: Prentice Hall. Journal Articles to Be Announced Evaluation: Exam 1: In Class Exam 2: In Class Final Exam/Paper: Take Home *Exams are each worth 1/3rd of the grade in the course. Please note that all three exams must be completed to receive a passing grade in this course.Data Analysis: Comm 516 p. 2 Components of Evaluation Exams: Two in-class exams will be administered during the course of the semester. These exams will be open-book, open-note, but they will completed individually. These exams will ask you to apply the concepts that have learned thus far in the course, to justify your decisions in analysis, to present your data, and to interpret others’ research. There are several aspects of the exams that are worth noting: • Although the exams are open-book and open-note, it is imperative that you prepare for the exams by reviewing course exercises, homework assignments, and your texts. If you do not prepare, you run the very real risk of not having enough time during class to complete the exam. • These exams (including the final project) are cumulative. That is, the 2nd exam and the final paper/project will build on information covered in earlier sections of the course. Final Exam/Paper: The final exam/paper is the culmination of all materials covered in the class. For this final exam you will be given the raw data for a study, a copy of the questionnaire/instrument employed, a description of the basic design of the study, and a series of hypotheses or research questions. Your final exam will involve setting up the data file, manipulating the data in a way dictated by the hypotheses/research questions of interest, analyzing the hypotheses/research questions, and writing up the results in a journal format that would be acceptable to most major journals in the discipline. When you turn in your final exam/paper, the following materials must be included: 1) your results section; 2) the final data file that you employed; 3) your syntax file, presented in order of the analyses that you conducted. This exam will be distributed on the last day of class (April 30th) and will be due Monday, May 4th at noon (or any time prior to that). “Homework”: Because comfort and skill in data analysis increase dramatically with practice, every class session will include “homework” exercises. This homework will require you to apply the skills that you learned in class and from your texts, to interpret results reported by other authors, and to illustrate your decision-making in the data analysis process. In short, the homework will require that you become a data analyst and explain your reasoning. These homework exercises will not be graded. However, my recommendation is that you complete the homework as if you were receiving a grade on it, and subsequently check your answers – first with classmates and then with the answers that I provide. Of course, whether or not you choose to do the “homework” is up to you, but working through the problems provided will not only help you prepare for the exams, it will also increase your ability and confidence.Data Analysis: Comm 516 p. 3 Course Policies • It is very important that everyone in the class feel completely free and comfortable to ask questions and contribute to class discussions. I never consider your questions misplaced or misinformed, and you should understand that if you have a question or need clarification, you are most certainly not alone. • Attendance in every class is expected. Because this course will proceed at a fairly rapid pace, if you miss a class, you miss a lot of information. Even if you are only late for class, you are likely to miss relevant information. Consequently, it is in your interest to be in attendance and to be on time. If you miss a class or are late, please do not ask the instructor to repeat lectures, to go over missed information or procedures, or to accept late work. • It is expected that you will have read all of the assigned readings prior to coming to class. Furthermore, your understanding will be greatly enhanced if you work through the sample problems that are included with the readings. • Although I encourage students in the course to study and practice data analysis together, all materials turned in for evaluation must reflect only your own work. Collaboration on evaluated materials will result in a failing grade in the class. • Students are