PSYC 711  DATA ANALYTIC PROCEDURES II
Spring 1998
Marilyn E. Demorest Office: MP 320 Email: demorest@umbc2.umbc.edu Hours: Tues. & Thurs. 10:0011:30 Thurs. 2:304:00, & 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 wellorganized 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 multiplechoice, truefalse, fillintheblank, 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 makeups 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 inclass 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 multiplechoice questions and the other requiring several shortanswer essays. The elimination procedure will be used with the multiplechoice 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 computerbased 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. 19 
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. 928 
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. 821824 Stevens, Chapter 2, pp. 4145 
Tuesday, 2/10  Overview of Multivariate Techniques  Harris, Chapter 1, pp. 2838 
Thursday, 2/12  Data Screening  Tabachnick & Fidell, Chapter 4, pp. 5787 
Friday, 2/13  QUIZ #2 (thru 2/10) Matrix Algebra: Scalar Products & Matrix Multiplication 
Tabachnick & Fidell, Appendix A, pp. 824826 Stevens, Chapter 2, pp. 4549 
Tuesday, 2/17  EXERCISE #1 due Data Screening 
Tabachnick & Fidell, Chapter 4, pp. 88125 
Thursday, 2/19  Multiple Regression Analysis  Tabachnick & Fidell, Chapter 5, pp. 127165 
Friday, 2/20  QUIZ #3 (thru 2/17) Matrix Algebra: Determinants 
Stevens, Chapter 2, pp. 4956 
Tuesday, 2/24  Multiple Regression Analysis  
Thursday, 2/26  Matrix Algebra: Matrix Inversion  Tabachnick & Fidell, Appendix A, pp. 826828 Stevens, Chapter 2, pp. 5660 
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. 828831 Stevens, Chapter 2, pp. 6062 
Thursday, 3/5  Multivariate Multiple Regression Analysis  Stevens, Chapter 3, Sections 3.14, pp. 107115; Sections 3.193.20 
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. 321352 
Thursday, 3/12  Canonical Correlation Analysis  Tabachnick & Fidell, Chapter 6, pp. 195222 
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. 375407 
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. 441459 Stevens, Chapter 13, pp. 450461; 466468; 497500 
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. 459483 Stevens, Chapter 13, pp. 500512 
Thursday, 4/9  Discriminant Function Analysis  Tabachnick & Fidell, Chapter 11, pp. 507546 
Friday, 4/10  EXAM #2 (thru 4/7)  
Tuesday, 4/14  Discriminant Function Analysis  
Thursday, 4/16  Logistic Regression  Tabachnick & Fidell, Chapter 12, pp. 575609 
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. 239285 
Friday, 4/24  QUIZ #9 (thru 4/21) Illustration: Logistic Regression 

Tuesday, 4/28  Loglinear Analysis  Stevens, Chapter 14, Sections 14.114.11, 14.13, 14.14 
Thursday, 4/30  PCA & FA  Tabachnick & Fidell, Chapter 13, pp. 635679 
Friday, 5/1  EXAM #3 (thru 4/28)  
Tuesday, 5/5  PCA & FA  Stevens, Chapter 11, pp. 362388 
Thursday, 5/7  Structural Equation Modeling  Tabachnick & Fidell, Chapter 14, pp. 709767 
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. 389410, 415418 
Friday, 5/15  EXAM 4 (thru 5/12), 8:0010:00 AM 