Course Description: Religious Holidays:PSYCHOLOGY 341: ADVANCED STATISTICAL METHODS II Department of Psychology University of Vermont Spring 2011 3 Credit Hours Instructor: Timothy Stickle, Ph.D. Office: 232 John Dewey Hall Phone: 656-3842 Email: [email protected] Office Hours: by appointment T.A.: Julia McQuade Office: 329 John Dewey Hall E-mail: [email protected] Office Hrs: Monday 11-12, Friday 12:50-3:50 (during lab), available by email and additional meetings are available by appointment. Lecture: M 12:50-3:50 (100 John Dewey Hall) Lab: F 12:50-3:50 (128 John Dewey Hall) Course Description: This is the second part of a two-series course required of all graduate students in the psychology program. The class will provide a more detailed approach to advanced statistics. We will cover data analysis with an emphasis on understanding data and quantitative thinking, graphical data displays, building and testing theoretically-driven models, how distributions affect hypothesis testing, Multiple Regression, the General Linear Model, and some extensions of multiple regression are the primary emphasis. There is also coverage of a select number of additional topics. These statistical techniques are central to evaluating a variety of hypotheses in psychology and related fields. Course Objectives: After completing this course, you will be familiar with the basic theory and analyses underlying each of the topics noted in the course schedule. More specifically, you will be proficient in: (1) Organizing and describing data, including useful plotting and graphical data display. (2) Selecting data analytic approaches that best test for specific research questions. (3) Understanding the theoretical assumptions underlying specific analyses. (4) Organizing data and importing it into statistical programs (5) Applying SPSS or SAS to perform the analyses covered in the course. Meeting these objectives, however, does NOT mean that you will be an expert in all, or even any, of these statistical techniques. Expertise in statistics generally develops only after multiple, repeated applications to real-world problems (i.e., repeated analyses with data that you care 12about such as the problems and data you will encounter in your theses and dissertations). The intent of this course is to provide you with a basic overview and introduction to some statistical techniques. You will most likely develop your own area of statistical expertise and training through subsequent coursework and especially through research projects. Course Materials (all books are available at the UVM bookstore): Required: Keith, T.Z. (2006). Multiple Regression and Beyond. New York: Pearson A number of helpful SPSS/PASW guides, including the Base and Advanced Statistics User Manuals are available for free in PDF format here: http://support.spss.com/ProductsExt/Statistics/Documentation/18/clientindex.html Additional handouts and readings will be passed out in class. A writable CD or flash drive is needed to save data sets and work throughout the semester. You will have access to lectures, assignments, labs, and review materials. Optional: Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd Ed. Mahwah, NJ: Erlbaum. Field, A. (2009). Discovering Statistics Using SPSS, 3 Ed. Thousand Oaks, CA: Sage. rd Field, A. & Miles, J. (2010). Discovering Statistics Using SAS. Thousand Oaks, CA: Sage. Howell, D. C. (2010, 2007). Statistical methods for psychology (7 edition). Belmont, CA: Cengage Wadsworth. th Course Components: General: Attend• ance is required at all class meetings. The information presented and discussed is integrative and cumulative. Missing any aspect, therefore, leads to increased difficulty in understanding later material. Students are expected to have read assigned material before it is discussed in class; class lecture and class discussion presume that students have read all assigned material. Work must be comp• lete and handed in on time. 1) Lab Assignments (Applied) • There will be weekly lab assignments for most topics covered. They are designed to help you understand and integrate materials by working to solve problems using real data sets. These assignments will be available prior to the day when we cover them in lab, so that you may work on them beforehand. The intent of the labs is not for you to only “get the right answer,” but to develop statistical skills using SPSS or SAS in order to apply the ideas discussed in lecture. • All lab assignments are due on the next lecture day (Mondays) following that lab. The assignments will be graded using a “pass/fail” system. A pass is equivalent to 1 point, a fail is no points. If you don’t turn in a homework assignment, or if you turn it in late and unexcused, you will receive no credit. You must make a genuine effort to complete the work. NO credit will be awarded for poor and sloppy work. The only exceptions to this policy are if you are ill or have an emergency and are unable to get the assignment in on time. You must notify the instructor of illness or in advance if you must be away. For example, if you will be out of town at a conference or other professional activity, discuss it ahead of time with me and make arrangements to complete missed assignments.3) Exams (Theoretical and applied) terial will be covered on the exams, with an emphasis on s will involve analyzing data using SPSS (or SAS if you prefer), ms are in take home format. ) Pd to participate in group discussions, ask questions to clarify sk ss ou2 • Both theoretical and applied mathe former. • In general, the examinterpreting the output, and applying the results to theoretical principles covered in the course. • The exa• Late exams will not be accepted. 3 articipation (Essential) • All students are expectematerial, and contribute to helping others in the class learn the material. If you do not aquestions, I will assume that you understand the material. It is your contributions to the clathat will best help everyone meet their learning goals, so please adopt a helpful and team-oriented approach to the class as much as possible. rse Evaluation: CLab Assignments 20% eighted exams) rading Scale: Exams 60% (2 equally wParticipation 20% G 77-79% C+ cademic Integrity: ent and on time for all