PSYC 6190 LONGITUDINAL DATA ANALYSIS. Course Outline Fall 2015/2016

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1 PSYC 6190 LONGITUDINAL DATA ANALYSIS Course Outline Fall 2015/2016 Instructor: Rob Cribbie Office: 334 Behavioural Sciences Building Phone: (x88615) Office Hours: By appointment Text: Singer, J. D. & Willett, J. B. (2003). Applied longitudinal data analysis. New York, NY: Oxford University Press, Inc. Course Objective: This course is designed to educate the student in statistical methods for analyzing data from longitudinal psychology studies. The course material will focus on providing the student with a broader perspective with regards to the available options for analyzing longitudinal data, as well as the necessary skills for conducting the appropriate analyses with statistical software. Statistical Software Package: The primary software package for this course will be R. R is a free software environment for statistical computing and graphics. However, students are free to use any other software that they are comfortable with, as long as it is capable of conducting the required analyses. Method of Evaluation: Final grades will be comprised of marks earned on: 1) Applied Research Article Presentation (10%) 2) Lab Exercise Solutions Presentation (5%) 3) Lab Assignments (2 X 20% = 40%) 4) Written Paper (35%) 5) Presentation on Written Paper (10%)

2 1) Applied Research Article Presentation Each student will be required to give one presentation on a research article that analyzes longitudinal data. Your presentation can focus on methods that were previously discussed in the course, or on methods that were not discussed in the course. In the latter case, be sure to describe the nature of the analyses conducted and how they relate to the methods covered in the class. The presentation should be approximately 15 minutes in length. 2) Lab Exercise Solutions Presentation Each student will be required to discuss the solutions to questions from a lab exercise. The presentation should outline all important statistical decisions made, present and discuss the statistical software approach adopted, and summarize the conclusions of the exercise. 3) Lab Assignments The laboratory assignments will require students to conduct statistical analyses of the methods discussed in class using statistical software packages. There will be two assignments throughout the term. Due Dates: Assignment #1: October 14, 2015 Assignment #2: November 18, ) Written Paper Each student will be required to write a 10 page (excluding references) paper focusing on a topic in longitudinal data analysis that is either not covered in this course, or is an extension of a topic covered in this course. The paper should consist of approximately 5 pages of introduction to the analysis, and 5 pages of presentation of an example using either real or simulated data. Due Date: November 25, ) Written Paper Presentation Each student is required to give a short (12-15 minute) summary of the analyses discussed in their paper. Given the brief time for the presentation, it is important to try to provide the audience with a clear take home message regarding the findings of the paper.

3 Final Grading System: Percent Letter Grade A A A B B

4 Lecture Schedule Date Material S&W Reading(s) Sep. 16 Introduction to the Course, Lab & Software Sep. 23 Traditional Approaches for Analyzing Ch. 1, Handout Longitudinal Designs Sep. 30 Comparing Change Across Groups in Ch. 2, Handout Pre-Post Studies / Exploring Longitudinal Data Oct. 7 Introducing the Multilevel Model for Ch.3, Handout Change Melissa Parlar/Odette Weiss Areeba Adnan(Q1)/Saeid Chavoshi(Q2) Oct. 14 Conducting Analyses with the Multilevel Ch. 4, Handout Model Ingrid Galfi-Pechenkov, Jessica Jeong Elizabeth van Monsjou(Q1), Lorinda Mak(Q2) Oct. 21 Treating Time for Flexibly Ch. 5, Handout Julia Riddell, Massimo Di Domenico Joshua Quinlan(Q1), OdetteWeiss(Q2)

5 Date Material S&W Reading(s) Oct. 28 Discontinuous and Nonlinear Change Ch. 6, Handout Elizabeth van Monsjou, AreebaAdnan Sheila Konanur(Q1), Jessica Jeong(Q2) Nov. 4 Error Covariance Structures in Multilevel Ch. 7, Handout Models Lorinda Mak, Maria Ayala Melissa Parlar(Q1), Yang Jie(Q2) Nov. 11 Introduction to Latent Growth Curve Ch. 8, Handout Modeling Saeid Chavoshi, Joshua Quinlan Ingrid Galfi-Pechenkov(Q1), Massimo Di Domenico(Q2) Nov. 18 Extensions of Latent Growth Curve Handout Modeling Sheila Konanur, Yang Jie Julia Riddell(Q1), Maria Ayala(Q2) Nov. 25 Dec. 2 Presentations Presentations