Advanced Statistics and Research Methods for Psychology I

Psychology 611

Fall 2007

Meeting Time: Fridays, 9:30am-12:10pm, Robinson B201

Website: http://archlab.gmu.edu/people/ckello/Psyc611Fall07.htm

Instructor: Christopher Kello

ckello@gmu.edu

2057 David King Hall

Thursdays 12-2p and by appointment

Laboratory Instructors, Office Hours, and Sections

David Kidd dkidd3@gmu.edu David King 2063 Tues 9am-10am	Richard Hermida rhermida@gmu.edu Robinson B 213A Weds 9am-10am	Joseph Luchman jluchman@gmu.edu Robinson B 213B Mon 4pm-5pm
Psyc 611-203 Innovation 318 Mon 6pm-7:50pm	Psyc 611-201 Innovation 318 Mon 8:30am-10:20am	Psyc 611-205 Innovation 319 Mon 6pm-7:50pm
Psych 611-204 Innovation 319 Mon 8pm-9:50pm	Psyc 611-202 Innovation 319 Mon 10:30am-12:20pm	Psyc 611-206 Innovation 319 Mon 8pm-9:50pm

Textbook

Myers, J. L. & Well, A. D. (2003). Research Design and Statistical Analysis (2nd Ed.). Mahwah, NJ: Lawrence Erlbaum.

Course Description

This is the first part of a two‑course sequence on statistical and research methods in psychology. It is designed to help you develop skills that will enable you to effectively evaluate the research of others, and to design, conduct, and report on research of your own. The course will emphasize conceptual understanding, as well as practical how‑to skills.

The course will closely follow the Myers and Well textbook, which combines lessons on research methods with lessons on the statistical tools that one would use to analyze results from studies using the research methods. The lab sections will follow along with the textbook and lectures, but labs will enhance the textbook and lectures in two important respects: Prior to each lab, students will collect their own data or use data from a study conducted outside of class (e.g., an advisor’s study, with permission from the advisor). In lab, students will learn how to conduct a given statistical analysis using SPSS (a statistical software package), and will conduct that analysis on their own data. Students will write up their results each week in very brief (500-700 words) research reports, which will include graphs and tables as appropriate.

Course Requirements

Requirements are mostly the same for Masters and Doctoral students. Everyone attends the same lectures and labs, is responsible for the same material, does the same homework assignments, and takes the same exams. The only difference is that Doctoral students are additionally required to work under the supervision of their primary research advisor to identify a substantive area of interest, conduct a review of the relevant theoretical and empirical literature, and formulate a specific research question to address (this is separate from the lab brief reports). In the Fall (Psyc 611), this will culminate with a written literature review. Then in the Spring (Psyc 612), these students will work with their advisors to develop a detailed research plan that culminates in a formal research proposal. Any Masters student who is interested can participate in the literature review and research proposal activity, provided that the student has identified a faculty member willing to serve as advisor.

All students participating in the research project must identify the faculty advisor with whom they will work. The advisor should understand that they are responsible for working with the student on an ongoing basis to identify relevant literature and discuss it with the student. The advisor will also grade the literature review in the Fall and the research proposal in the Spring. Each student must ensure that his or her faculty advisor send an email to Dr. Kello BY SEPTEMBER 14th stating that the faculty member is willing to advise the student on his or her 611 literature review.

Assessment of Performance

Grades will be based on four components of the course as follows:

• Literature review, worth 10% of final grade (only for students doing a literature review). Criterion and grade to be determined by advisor (see above).
  
  
• Overall lab grade, worth 30% of final grade (40% for students not doing a literature review). The lab grade will be based on brief reports due each week (see above) and lab participation, which includes attendence. Labs will be taught using SPSS, and students will use SPSS to conduct analyses for the brief reports.
  
  
• Four exams, each worth 10% of final grade. Each exam will administered through the web immediately or soon after each of the four presentation lectures (see next). Exams will be open-book, but students are expected to complete them individually. Exams will be non-cumulative, in that each one will NOT explicitly cover material from previous exams. However, statistics is cumulative in nature, so knowledge learned earlier in the course will be foundational to topics covered later in the course. Because students will be given ample time and flexibility to take each exam, there will absolutely NO make-up exams. If one exam is missed, the student may write a 2500-word research proposal, topic and format to be negotiated with Dr. Kello. The research proposal will incorporate material covered on the missed exam, and its grade will replace the missed exam grade. Only ONE exam can be missed, no matter what the reason. If two or more exams are missed, the student will receive a zero grade for each exam missed after the first.
  
  
• Brief report presentations, worth 20% of final grade. Four lectures (including the final exam period) will be devoted to student presentations (5 minutes each plus 3 minutes for questions/comments/transition). Each presentation will be on a brief report written by the student since the last exam, and each student will present one report in the semester (order to be chosen at random). Presentations will be graded on the basis of clarity, form, correctness of method and analysis, and informativeness. This last criterion is based on the fact that presentations will cover material on the upcoming exam (handed out at the end of lecture), and students will be required to attend all presentations and give presenters the courtesy of their undivided attention. In return, presentations should be designed in part to help students study for the upcoming exam. Presentation grades will also be based attending the four presentation lectures (attendence will not be taken in other lectures): For each missed presentation lecture, one eighth of the presentation grade (2.5% of total grade) will receive a zero grade. So, if a student misses all four presentation lectures, he or she will receive a zero for half the presentation grade.

The GMU Honor Code will be followed. Studying in groups is encouraged, but all exams and lab assignments must represent your own work. It is perfectly acceptable to use outside sources (e.g., journals, books) to complete assignments, but any such use must be cited explicitly. If you are a student with disability and you need academic accommodations, please see Dr. Kello and contact the Disability Resource Center (DRC) at 709-993-2474.  All academic accommodations must be arranged through that office. The last day to add this class is September 11th, and the last day to drop is September 28th.

Screening Exam

To enroll in this course, all students must either pass the 611 screening exam, or failing that, successfully complete a preparatory statistics course. See Mike Hurley for details and to check on the status of your enrollment.

Course Schedule

Lecture notes will be made available on the course website as links to PowerPoint files. The schedule is likely to be adjusted throughout the semester.

Date Aug 31 Sept 7 Sept 14 Sept 21 Sept 28 Oct 5 Oct 12 Oct 19 Oct 26 Nov 2 Nov 9 Nov 16 Nov 23 Nov 30 Dec 7 Dec 14

Topic Intro, review of basic concepts and stats Distributions, hypothesis testing Chi-square, one-way ANOVA *** Brief Report Presentations, Exam 1 *** Contrasts, trends, two-way ANOVAS More factorial designs *** Columbus Day: Oct 8 labs meet on Oct 9 *** Repeated measures, mixed designs *** Brief Report Presentations, Exam 2 *** ANCOVA Hierarchical, latin-square designs Correlation and bivariate regression *** Brief Report Presentations, Exam 3 *** *** Thanskgiving, no lecture, Nov 26 labs meet *** Multiple regression General linear model *** Brief Report Presentations, Exam 4 ***

Reading Chap 1,2,3 Chap 4,5,6 Chap 7,8 Chap 9,10,11 Chap 11,12 Chap 13,14 Chap 15 Chap 16,17 Chap 18,19 Chap 20 Chap 21

Notes ppt