PSYC 711 - DATA ANALYTIC PROCEDURES II
Spring 1998
| Marilyn E. Demorest Office: MP 320 Email: demorest@umbc2.umbc.edu Hours: Tues. & Thurs. 10:00-11:30 Thurs. 2:30-4:00, & by appointment |
Stephanie Eischen Office: MP009E Email: eischen@umbc2.umbc.edu Hours: To be arranged & by appointment |
Primary Text:
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). New York: Harper Collins.
Supplementary Texts:
Grimm, L. G., & Yarnold, P. R. (1994). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association.
Harris, R. J. (1985). A primer of multivariate statistics (2nd ed.). Orlando, FL: Academic Press.
Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Erlbaum.
Course Objectives. The course is organized around two broad objectives. The first is to understand general principles and theory underlying multivariate analysis, including both the statistical concepts and the interrelationships among different procedures. The second objective is to be able to select, implement, and interpret the results of multivariate analyses.
Texts. The primary text by Tabachnick and Fidell is a well-organized and well written introduction to multivariate analysis, which covers the conceptual basis for the various procedures, assumptions underlying their use, and annotated examples of statistical programs and their output. The text by Grimm and Yarnold gives an excellent conceptual introduction to various methods of analysis with simple, clear examples. Although the chapters are not explicitly assigned, reading appropriate chapters of this book before and after the assigned readings may help you to integrate the material and to find the forest after seeing the trees! It is available from APA in paperback. The text by Harris has an excellent introductory chapter that both summarizes univariate statistical methods and provides an overview of their multivariate counterparts. It is assigned reading and will be distributed to you. The text by Stevens is recommended both for supplementary reading during the course and as a reference on multivariate analysis for the future. You are strongly encouraged to include the chapters from Stevens in your reading so that you can identify recurrent themes and benefit from another perspective on important issues. All reading assignments are shown in the Course Calendar.
Quizzes. Ten brief quizzes will be administered during the semester. The quizzes will be based on readings, lecture material, and handouts, and will utilize a variety of question formats such as multiple-choice, true-false, fill-in-the-blank, matching, and so on. They will emphasize theory and understanding of concepts. Quizzes will be administered using an elimination procedure that permits you to receive partial credit if you don't choose the correct answer, but can eliminate one or more of the incorrect answers.
There will be no make-ups for quizzes that are missed and no additional time will be given to students who arrive late for a quiz. However, only the best eight quiz scores will count toward the course grade; the lowest two quiz scores will be dropped. Thus, if you miss a quiz, for any reason, you can simply let that be one of the quizzes you drop.
Exams. There will also be four in-class examinations, which will be administered during the laboratory. The exams will not be cumulative, except to the extent that the material itself is. Each exam will comprise two parts, one consisting of multiple-choice questions and the other requiring several short-answer essays. The elimination procedure will be used with the multiple-choice questions. The essays will require you to define and discuss specific concepts or to contrast and compare related concepts. A list of key terms from which the essay items will be drawn will be distributed before each exam as a study guide.
Exercises. Five sets of exercises will be assigned to facilitate your mastery of the reading material and to give you experience conducting multivariate analyses and interpreting the results. Exercises will consist of study questions, computational problems, and computer assignments. Students are free to consult with one another and are encouraged to do so, but each student will receive a different set of data and must hand in his or her own complete set of exercises. Exercises are due at the beginning of class on the dates shown in the course calendar.
Laboratory. All quizzes will be administered at the beginning of the laboratory session. On some occasions, there will be a lecture during laboratory time. Other laboratory sessions will be devoted to a variety of activities including review of material covered in lectures, illustration of computer-based multivariate analyses, discussion of assigned reading from the texts, and so on. Laboratory sessions will be conducted by the teaching assistant, Stephanie Eischen.
Grading System. The course grade will be based on quizzes, exams, and exercises. Each of these components will be graded on a percentage basis, and an average will then be taken to obtain an overall percentage for the course. Together, quizzes and exams will count 75% toward the course grade; the exercises will count 25%. However, the relative weight of quizzes and exams will be determined for each student: whichever average is higher will receive double weight. For example, a student with a quiz average of 87%, an exam average of 78%, and an exercise average of 94% would receive a grade of [2(87%) + 78% + 94%]/4 = 86.5%.
The criteria for letter grades are as follows: At most, 90% will be required for a grade of A, 80% for a grade of B, 70% for a grade of C, and 60% for a grade of D. These cutoffs may be lowered at the end of the semester; they will not be raised.
| Course Calendar | ||
|---|---|---|
| Date | Lecture Topic | Reading |
| Tuesday, 1/27 | Course Overview Introduction to Multivariate Analysis |
Harris, Chapter 1, pp. 1-9 |
| Thursday, 1/29 | Multivariate Data and Descriptive Statistics | Tabachnick & Fidell, Chapter 1 |
| Friday, 1/30 | DIAGNOSTIC QUIZ Review of Univariate Statistics |
Tabachnick & Fidell, Chapter 3 Harris, Chapter 1, pp. 9-28 |
| Tuesday, 2/3 | Linear Combinations of Variables | |
| Thursday, 2/5 | Overview of Multivariate Techniques | Tabachnick & Fidell, Chapter 2 |
| Friday, 2/6 | QUIZ #1 (thru 2/3) Matrix Algebra: Basic Operations |
Tabachnick & Fidell, Appendix A, pp. 821-824 Stevens, Chapter 2, pp. 41-45 |
| Tuesday, 2/10 | Overview of Multivariate Techniques | Harris, Chapter 1, pp. 28-38 |
| Thursday, 2/12 | Data Screening | Tabachnick & Fidell, Chapter 4, pp. 57-87 |
| Friday, 2/13 | QUIZ #2 (thru 2/10) Matrix Algebra: Scalar Products & Matrix Multiplication |
Tabachnick & Fidell, Appendix A, pp. 824-826 Stevens, Chapter 2, pp. 45-49 |
| Tuesday, 2/17 | EXERCISE #1 due Data Screening |
Tabachnick & Fidell, Chapter 4, pp. 88-125 |
| Thursday, 2/19 | Multiple Regression Analysis | Tabachnick & Fidell, Chapter 5, pp. 127-165 |
| Friday, 2/20 | QUIZ #3 (thru 2/17) Matrix Algebra: Determinants |
Stevens, Chapter 2, pp. 49-56 |
| Tuesday, 2/24 | Multiple Regression Analysis | |
| Thursday, 2/26 | Matrix Algebra: Matrix Inversion | Tabachnick & Fidell, Appendix A, pp. 826-828 Stevens, Chapter 2, pp. 56-60 |
| Friday, 2/27 | EXAM #1 (thru 2/24) | |
| Tuesday, 3/3 | EXERCISE #2 due Matrix Algebra: Characteristic Equations, Eigenvalues, & Eigenvectors |
Tabachnick & Fidell, Appendix A, pp. 828-831 Stevens, Chapter 2, pp. 60-62 |
| Thursday, 3/5 | Multivariate Multiple Regression Analysis | Stevens, Chapter 3, Sections 3.14, pp. 107-115; Sections 3.19-3.20 |
| Friday, 3/6 | QUIZ #4 (thru (3/3) Illustration: Mutivariate Multiple Regession Analysis |
|
| Tuesday, 3/10 | Analysis of Covariance (ANCOVA) | Tabachnick & Fidell, Chapter 8, pp. 321-352 |
| Thursday, 3/12 | Canonical Correlation Analysis | Tabachnick & Fidell, Chapter 6, pp. 195-222 |
| Friday, 3/13 | QUIZ #5 (thru 3/10) Illustration: Canonical Correlation Analysis |
|
| Tuesday, 3/17 | Canonical Correlation Analysis | |
| Thursday, 3/19 | Oneway Multivariate Analysis of Variance and Covariance (MANOVA & MANCOVA) | Tabachnick & Fidell, Chapter 9, pp. 375-407 |
| Friday, 3/20 | QUIZ #6 (thru 3/18) Illustration: MANOVA & MANCOVA |
|
| Tuesday, 3/24 Thursday, 3/26 Friday, 3/27 |
SPRING BREAK | |
| Tuesday, 3/31 | EXERCISE #3 due Factorial MANOVA & MANCOVA |
|
| Thursday, 4/2 | Repeated Measures ANOVA Profile Analysis |
Tabachnick & Fidell, Chapter 10, pp. 441-459 Stevens, Chapter 13, pp. 450-461; 466-468; 497-500 |
| Friday, 4/3 | QUIZ #7 (thru 3/31) Illustration: Repeated Measures ANOVA |
|
| Tuesday, 4/7 | Repeated Measures MANOVA and MANCOVA | Tabachnick & Fidell, Chapter 10, pp. 459-483 Stevens, Chapter 13, pp. 500-512 |
| Thursday, 4/9 | Discriminant Function Analysis | Tabachnick & Fidell, Chapter 11, pp. 507-546 |
| Friday, 4/10 | EXAM #2 (thru 4/7) | |
| Tuesday, 4/14 | Discriminant Function Analysis | |
| Thursday, 4/16 | Logistic Regression | Tabachnick & Fidell, Chapter 12, pp. 575-609 |
| Friday, 4/17 | QUIZ #8 (thru 4/14) Illustration: Discriminant Function Analysis |
|
| Tuesday, 4/21 | EXERCISE #4 due Logistic Regression |
|
| Thursday, 4/23 | Loglinear Analysis | Tabachnick & Fidell, Chapter 7, pp. 239-285 |
| Friday, 4/24 | QUIZ #9 (thru 4/21) Illustration: Logistic Regression |
|
| Tuesday, 4/28 | Loglinear Analysis | Stevens, Chapter 14, Sections 14.1-14.11, 14.13, 14.14 |
| Thursday, 4/30 | PCA & FA | Tabachnick & Fidell, Chapter 13, pp. 635-679 |
| Friday, 5/1 | EXAM #3 (thru 4/28) | |
| Tuesday, 5/5 | PCA & FA | Stevens, Chapter 11, pp. 362-388 |
| Thursday, 5/7 | Structural Equation Modeling | Tabachnick & Fidell, Chapter 14, pp. 709-767 |
| Friday, 5/8 | QUIZ #10 (thru 5/5) Illustration: Principal Components Analysis |
|
| Tuesday, 5/12 | EXERCISE #5 due Structural Equation Modeling |
Stevens, Chapter 11, pp. 389-410, 415-418 |
| Friday, 5/15 | EXAM 4 (thru 5/12), 8:00-10:00 AM | |