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University of Nebraska-Lincoln |
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Instructor: |
Dr. Lesa Hoffman |
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Email: |
Phone: |
(402) 472-6930 |
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Rooms: |
77 and 234 Burnett Hall |
Office: |
220 Burnett Hall (mailbox in 237) |
| Time: | 10:30-11:45 MWF (3 credits) | Office Hours: |
1:30-2:30 MW and by appointment |
Links under topics below are .pdf files for the lecture materials.
Versions of the .pdf files including the answers will be available after each class under "answers".
Audio links are .mp3 files taped from the class lecture.
| Week | Date | Topic and Downloads | Readings & Manuals |
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| 1 | 1/14 | Course Introduction (audio) Lecture: Integrating Two-Level Longitudinal Models into General MLM (audio1) (audio2) |
S & B ch. 3-5 |
| 1/16 | NO CLASS | ||
| 1/18 | Critical Values for Deviance Comparisons Two-Level Longitudinal Models, continued Lecture: Psuedo-R2 Effect Size in Two-Level Models (sorry, no audio) Example: BP and WP Effects and Psuedo-R2 (answers) (sorry, no audio) |
S & B ch. 7 Stoel et al. (2006) |
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| 2 | 1/21 | NO CLASS - MLK DAY | |
| 1/23 | Pseudo-R2, continued (bring handouts from 1b/c) | ||
| 1/25 | Lecture: Assumptions of Two-Level Models (audio) Example: Assumption Checking with SAS (audio) |
S & B ch. 6, 9 R & B ch. 9 |
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| 3 | 1/28 | MEET IN 234 LAB Begin Assignment #1: Checking Model Assumptions (audio) |
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| 1/30 | Lecture: Heterogeneous Variance Models (audio) Example: Differential Variation in Twins (audio) Example: Differential Daily Variation (audio) |
S & B ch. 8 Guo & Wang (2002) Litell et al. ch. 8 Hoffman (2007) |
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| 2/1 | Lecture: Alternative Metrics of Time (audio) Example: Testing Convergence across Time Metrics (audio) |
Sliwinski et al. (2003a) | |
| 4 | 2/4 | Assignment #1 due by 11:59 PM via email Testing Convergence, continued |
R & B ch. 4-5 |
| 2/6 | NO CLASS | ||
| 2/8 | Lecture: MLM for Clustered Observations (audio) Example: Two-Level Clustered Models (answers) (audio1) (audio2) |
Hoffmann & Gavin, 1998 | |
| 5 | 2/11 | General Feedback on Assignment #1 Two-Level Clustered Models, continued |
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| 2/13 | Lecture: Crossed Random Effects Models (audio1) (audio2) Example: Crossed Subjects and Items (audio, sorry got cut off early ) |
S & B ch. 11 Hox ch. 7 R & B ch. 12 |
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| 2/15 | Example: Crossed Primary and Secondary Schools (audio) Example: Changes in Nesting over Time (audio) |
Hoffman & Rovine (2007) Locker et al. (2007) Snijders & Kenny (1999) |
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| 6 | 2/18 | First Revisions of Assignment #1 due by 11:59 PM via email MEET IN 234 LAB Begin Assignment #2: Crossed Random Effects Models |
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| 2/20 | Discussion of HW1 (bring homeworks) Discussion of HW2 (bring homework handout) |
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| 2/22 | Second Revisions of Assignment #1 due by 11:59 PM via email Crossed Random Effects Examples, continued (bring 5b and 5c handouts) |
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| 7 | 2/25 | Assignment #2 due by 11:59 PM via email Crossed Random Effects Examples, continued (bring 5c handouts) Lecture: Clustered Longitudinal (Three-Level) Models (audio1) (audio2) (audio3) (audio4) (audio5) |
R & B ch. 8 |
| 2/27 | Example: Clustered Longitudinal Twin Models (answers) (audio1) (audio2) | ||
| 2/29 | Example: Clustered Longitudinal Twin Predictor Models (answers) (audio) | ||
| 8 | 3/3 | General Feedback on Assignment #2 Three-Level models, continued (bring handouts from 7a and 7c) |
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| 3/5 | More on 3-Level Models: Variances and R2 Spreadsheet and Model Syntax (audio) Three-Level models, continued (bring handouts from 7a and 7c) |
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| 3/7 | Three-Level models, continued (bring handouts from 7a and 7c) | ||
| 9 | 3/10 | Revisions of Assignment #2 due by 11:59 PM via email |
Curran & Bauer (2007) |
| 3/12 | MEET IN 227 LAB Begin Assignment #3: Three-Level Models |
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| 3/14 | MEET IN 227 LAB In-class time to work on assignment 3 |
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| 3/17 | SPRING BREAK | ||
| 3/19 | SPRING BREAK | ||
| 3/21 | SPRING BREAK | ||
| 10 | 3/24 | Lecture: Multivariate Cross-Sectional Regression Models (audio) Example: Multivariate Cross-Sectional Regression Models with Family Data (audio1) (audio2) |
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| 3/26 | Assignment #3 due by 11:59 PM via email Lecture: Multivariate Longitudinal Models (audio1) (audio2) Example: Multivariate Within-Person Variation (audio1) (audio2) Example: Multivariate vs. Time-Varying Predictors (audio) |
MacCallum et al. (1997) S & B ch. 13 Sliwinski et al. (2003b) |
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| 3/28 | Example: Multivariate Clustered Longitudinal Models (audio) Example: Multivariate Siblings Models (audio) Example: Multivariate Longitudinal Family Models (spreadsheet for figures) (audio) Example: Longitudinal Difference Score Models (audio) |
Raudenbush et al. (1995) Sayer & Klute (2005) |
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| 11 | 3/31 | General Feedback on Assignment #3 Multivariate Longitudinal Examples, continued (bring handouts from 10c) |
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| 4/2 | Multivariate Longitudinal Examples, continued (bring handouts from 10c) | ||
| 4/4 | Multivariate Longitudinal Examples, continued (bring handouts from 10c) | ||
| 12 | 4/7 | MEET IN 234 LAB Begin Assignment #4: Multivariate Models |
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| 4/9 | NO CLASS | ||
| 4/11 | NO CLASS | ||
| 13 | 4/14 | Revisions of Assignment #3 due by 11:59 PM via email MEET IN 234 LAB - In-class time to work on assignment 4 |
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| 4/16 | Assignment #4 due by 11:59 PM via email Meet in 227 Lab – Introduction to SAS NLMIXED - Negative Exponential Models (audio) |
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| 4/18 | Take-Home Final Exam Available Lecture: Generalized Models (audio1) (audio2) Example: Generalized Regression Models NOW FIXED! Spreadsheet for all generalized model example figures |
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| 14 | 4/21 | Lecture: Generalized Linear Mixed Models (audio) Example: Generalized Longitudinal Models NOW FIXED! |
S & B ch. 14 |
| 4/23 | General Feedback on Assignment #4 Generalized Linear Mixed Models, continued |
R & B ch. 10 | |
| 4/25 | Generalized Linear Mixed Models, continued Course Evaluations |
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| 15 | 4/28 | MEET IN 234 LAB SAS Macro Programming (audio1) (audio2 -- sorry, got cut off) |
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| 4/30 | Revisions of Assignment #4 due by 11:59 PM via email MEET IN 227 LAB SAS Macro Programming, continued |
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| 5/2 | Drafts of Final Exam due by 11:59 PM via email MEET IN 227 LAB SAS Macro Programming, continued |
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| 16 | 5/8 | Final Exam due by 11:59 PM via email | |
This course will illustrate the advanced uses of the General Linear Mixed Model for complex data analysis. After reviewing the basic two-level model and assumptions therein, the course will cover multiple extensions, including crossed random effects, heterogeneous variances, three-level models, multivariate models, and models for non-normal outcomes. Class time will be devoted primarily to lectures and examples. Lecture materials in .pdf format will be available for download at the website above the day prior to class, or else paper copies will be provided in class. Audio recordings of the class lectures in .mp3 format will also be posted online, but are not intended to take the place of class attendance. Because the course will have an applied focus, course sessions will also be held in the 234 Burnett computer lab (see syllabus for dates), in which participants will have opportunities for hands-on practice and to work on course assignments. SAS will be the only program utilized, and lab time will also be used to help participants become more knowledgeable and efficient SAS users.
Participants should be familiar with the general linear model (analysis of variance, regression) as well as two-level applications of the general linear mixed model (aka multilevel model, hierarchical linear model) prior to enrolling in this course. This course is not meant to take the place of an introductory multilevel modeling course. Participants who are unsure if their level of preparation will be adequate should consult the instructor as needed. Participants will need to have access to SAS software, available in rooms 234, 227, and 230 Burnett. Student licenses can be purchased from the stats department (around $40; yearly renewal required). Course assignments will include both essay questions and application of techniques discussed in class, and will utilize data sets provided by the instructor.
Course performance will be evaluated as follows. Details about each requirement will be presented throughout the semester at least one week prior to the due dates.
Final Exam:
Participants will complete a take-home final that covers conceptual issues discussed through the semester (15 points). If the opportunity for a revision is desired, the final exam must be completed by 4/30/08; otherwise, the final exam (or the revision of the final exam) is due by 5/7/08.
Course Assignments:
Four assignments (88 points) will be administered in order to give participants the practice applying techniques discussed in class and will be due as listed on the syllabus unless otherwise stated. Each assignment must be at least half-complete in order to be accepted and may be revised ONCE to earn the maximum possible points. Assignments should be submitted electronically via email as a Microsoft Word document. Please use the ‘track changes' function when revising assignments.
Assignment #1 (due 2/04/08; revision due 2/18/08) General Feedback on Assignment 1
Assignment #2 (due 2/25/08; revision due 3/10/08) General Feedback on Assignment 2
Assignment #3 (due 3/26/08; revision due 4/14/08) General Feedback on Assignment 3
Assignment #4 (due 4/16/08; revision due 4/30/08) General Feedback on Assignment 4
Final grades will be determined by the proportion earned of 105 possible points:
=97 = A+ 93-96 = A 90–92 = A- 87-89 = B+ 83-86 = B 80-82 = B- < 80 = C
Policy on assigning incompletes:
A grade of “incomplete” will be assignment ONLY in the case of extenuating circumstances that prevent participants from completing course requirements in a timely manner. If an incomplete is assigned, then all course requirements must be completed within ONE MONTH of the end of the course or else the incomplete will turn into whatever grade has been earned at that point.
Policy on late assignments:
If other obligations or circumstances will prevent you from completing any course requirements, please come talk to me so that we can create a solution. Don't wait until you are behind! If you contact me at least two weeks prior to a due date we may be able to extend the deadline to accommodate any extenuating circumstances. Otherwise, late assignments will be docked .5 points per day in order to encourage participants to keep up with the course. Points lost to lateness will not be returned.
As a reminder, the University has a policy on academic honesty (see the Graduate Studies Bulletin).
ALL COURSE ASSIGNMENTS SHOULD BE DONE INDIVIDUALLY.
Students with disabilities are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation. It is the policy of the University of Nebraska-Lincoln to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.
S & B : Snijders, T. A. B., & Bosker, R. (1999). Multilevel analysis. Thousand Oaks, CA: Sage.
Book Chapters:
R & B: Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.
Curran, P. J., & Bauer, D. J. (2007). Building path diagrams for multilevel models. Psychological Methods, 12 (3), 283-297.
Guo, G., & Wang, J. (2002). The mixed or multilevel model for behavior genetic analysis. Behavior Genetics, 32(1), 37-49.
Hoffman, L. (2007). Multilevel models for examining individual differences in within-person variation and covariation over time. Multivariate Behavioral Research, 42 (4), 609-629.
Hoffman, L., & Rovine, M. J. (2007). Multilevel models for experimental psychologists: Foundations and illustrative examples. Behavior Research Methods, 39 (1), 101-117.
Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623-641.
Locker Jr., L., Hoffman, L., & Bovaird, J. A. (2007). The use of multilevel modeling in the analysis of psycholinguistic data. Behavior Research Methods, 39 (4), 723-730.
MacCallum, R. C., Kim, C., Malarkey, W. B., & Kiecolt-Glaser, J. K. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32(3), 215-253.
Raudenbush, S.W., Brennan, R.T., & Barnett, R.C. (1995). A multivariate hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9(2), 161-174.
Sayer, A. G., & Klute, M. M. (2005). Analyzing couples and families. In V. L. Begtson, A. Acock, K. R. Allen, P. Dilworth-Anderson & D. M. Klein (Eds.), Sourcebook of Family Theory and Research (pp. 289-313). Thousand Oaks, CA: Sage.
Sliwinski, M. J., Hofer, S. M., & Hall, C. B. (2003b). Correlated and coupled cognitive change in older adults without preclinical dementia. Psychology and Aging, 18 (4), 672-683.
Sliwinski, M. J., Hofer, S. M., Hall, C. B., Buschke, H., & Lipton, R. B. (2003a). Modeling memory decline in older adults: The importance of preclinical dementia, attrition, and chronological age. Psychology and Aging, 18 (4), 658-671.
Snijders, T. A. B., & Kenny, D. A. (1999). The social relations model for family data: A multilevel approach. Personal Relationships, 6, 471-486.
SPSS:
SAS:
Burlew, M.M. (1998). SAS macro programming made easy. Cary, NC: SAS Institute.