Session: Spring 2015 (Jan 5 - Apr 2)
Meeting Time: MTh 11:30 a.m. - 12:50 p.m.
Meeting Place: HSD 160 LAB
| Instructor | Dr. Andrea Piccinin | Dr. Andrey Koval |
|---|---|---|
| Office | Cornett A263 | Cornett B335d |
| Hours | T 4-5, Th 3:30-4:30 | T 2:30-3:30 or by appointment |
| piccinin at uvic dot ca | andkov at uvic dot ca | |
| Phone | 853-3861 | 853-3862 |
| Meeting | Week | Class | Topic | Reading | Assignment |
|---|---|---|---|---|---|
| 05 Jan | 1 | 1 | Overview | AMA 1 | |
| 08 Jan | 2 | Multivariate Methods History; Lab 1 : R Intro | |||
| 12 Jan | 2 | 3 | Matrix Algebra | AMA 2 | |
| 15 Jan | 4 | Lab 2 : matrix algebra: basic operations | HM1 Matrix Algebra assigned | ||
| 19 Jan | 3 | 5 | Review Univariate GLM | ||
| 22 Jan | 6 | Lab 3 : Matrix Algebra to GLM | HM1 Matrix Algebra due Jan 23 | ||
| 26 Jan | 4 | 7 | Eigenvalues & Eigenvectors | ||
| 29 Jan | 8 | Lab 4 : Eigenvalues, decomposition, & inverses | HM2 Eigen & EDA assigned | ||
| 02 Feb | 5 | 9 | Missing data & data screening | AMA 3, 12 | |
| 05 Feb | 10 | Lab 5 : EDA & data prep | HM2 Eigen & EDA due Feb 6 | ||
| 09 Feb | 6 | Reading Week | |||
| 13 Feb | Reading Week | ||||
| 16 Feb | 7 | 11 | Principal Components Analysis | AMA 7 | |
| 19 Jan | 12 | PCA/ Common Factor Models | AMA 8 | ||
| 23 Feb | 8 | 13 | Common Factor Models (EFA) | ||
| 26 Feb | 14 | Lab 6 : EFA | HM3 PCA & EFA assigned | ||
| 02 Mar | 9 | 15 | Confirmatory Factor Analysis | AMA 9, SEM 4 | |
| 05 Mar | 16 | CFA | HM3 PCA & EFA due Mar 6 | ||
| 09 Mar | 10 | 17 | Hypothesis Testing | SEM 1 | |
| 12 Mar | 18 | Lab 7 : CFA | SEM 2 (as needed) | HM4 CFA assigned | |
| 16 Mar | 11 | 19 | Path Analysis | SEM 3 | |
| 19 Mar | 20 | Path Analysis / SEM | SEM 5 | HM4 CFA due Mar 20 | |
| 23 Mar | 12 | 21 | SEM | ||
| 26 Mar | 22 | Lab 8 : SEM | HM5 SEM assigned | ||
| 30 Mar | 13 | 23 | More SEM | ||
| 02 Apr | 24 | Wrap Up | HM5 SEM due Apr 2 |
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An Introduction to Applied Multivariate Analysis (Raykov, T. & Marcoulides, G.A , 2008. New York, NY: Routledge)
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A First Course in Structural Equation Modeling (Raykov, T. & Marcoulides, G.A ,2006. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.)
This course will introduce you to a variety of multivariate techniques, primarily those with the goal of data reduction (principal components and factor analysis) and complex hypothesis testing (structural equation modeling).
Learning statistical methods is like learning a language. One course will improve your ability to read and understand other peoples work. Becoming comfortable with the methods will require your use of them with data meaningful to you. In this course we will work on understanding when, why and how to implement a particular analysis, as well as how to interpret the product of the analysis. Assignments are intended to help you become more comfortable with using syntax in statistical analysis software.
| Evaluation Format | Weight | Due Date |
|---|---|---|
| Assignments | ||
| HM1 | 10 | Jan 23 |
| HM2 | 10 | Feb 6 |
| HM3 | 10 | Mar 6 |
| HM4 | 10 | Mar 20 |
| HM5 | 10 | Apr 2 |
| Exams | ||
| Midterm | 25 | Feb 19 |
| Final | 25 | Apr 9 |
| TOTAL | 100 |
Grades will be assigned according to the following scale:
| Grade | Lowest | Highest | Description |
|---|---|---|---|
| A+ | 90 | 100 | exceptional |
| A | 85 | 89 | excellent |
| A- | 80 | 84 | strongly competent |
| B+ | 77 | 79 | competent |
| B | 73 | 76 | barely competent |
| B- | 70 | 72 | incompetent |
| C+ | 65 | 69 | incompetent |
| C | 60 | 64 | incompetent |
| D | 50 | 59 | incompetent |
| F | 0 | 49 | incompetent |