Lesa's course directory

Previous version of this course (Spring 2019)
Instructor: Professor Lesa Hoffman (she, her, hers)
Educational Measurement and Statistics Program
Department: Psychological and Quantitative Foundations
Office: South 361 Lindquist Center
DEO: Dr. Megan Foley Nicpon
Zoom Access: Meeting ID: 5044356512
https://uiowa.zoom.us/my/lesahoffmaniowa
Mobile Access: +13126266799,,5044356512#
Instructor Email: Lesa-Hoffman@UIowa.edu
Course Time: Tuesdays and Thursdays 2:00–3:15 PM Instructor Phone: 319-384-0522
Zoom-Only
Office Hours:
Tuesdays and Thursdays 3:15–4:45 PM
as a group or individually by appointment
SAS MLM
Resources:

STATA MLM
Resources:
Lesa's SAS guide from PilesOfVariance.com
SAS MIXED Online Manual

Lesa's Stata guide from PilesOfVariance.com
STATA MIXED Online Manual
Online Homework:

Link to UIowa Virtual Desktop

Link to Homework Portal (still available!)

For help getting started with the online homework system, Virtual Desktop, SAS, or STATA, please see the videos from this class

Textbook and Supplemental Materials:

Longitudinal Analysis:
Modeling Within-Person Fluctuation and Change

Full Syntax Examples at PilesOfVariance.com


Schedule of Events (Printable Syllabus; last updated 5/2/2021)

Week

Date

Topics and Course Materials

Suggested Readings
for Each Topic

1 1/25 NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
1/26 Lecture 1: Introduction to the Course and Analysis of Longitudinal Data
Lecture 1 Part 1 (slides 1-13): Video
Hoffman (2015) ch. 1
Willett (1989)
1/28 Lecture 1, continued
Lecture 1 Part 2 (slides 14-24): Video
 
       
2 2/1 NO HW OR FA DUE  
2/2 Lecture 2: Review of General Linear Models
For more examples, see lecture/examples 6 and 7 from this class
Lecture 2 Part 1 (slides 1-31): Video
Lecture 2 Part 2 (slides 29-40 outside of class): Video
Hoffman (2015) ch. 2
2/4 Lecture 3: Introduction to Within-Person Analysis and RM ANOVA
Example 3a: Between vs. Within-Person Models
Data, Syntax, and Output Files for Example 3a (SAS, STATA, and R; same data as chapter 3a in text)
Lecture 3 (slides 1-15) and Example 3a (pages 1-3) Part 1: Video
Hoffman (2015) ch. 3
       
3 2/8 HW0 (3 points extra credit) DUE ONLINE BY 11:59 PM  
2/9 Lecture 3 and Example 3a, continued
Lecture 3 (slides 11-16) and Example 3a (pages 3-9) Part 2: Video

2/11 Example 3b: Repeated Measures Analysis of Variance (RM ANOVA) -- updated 2/11/21
Data, Syntax, and Output Files for Example 3b (SAS, STATA, and R; same data as chapter 3a in text)
Lecture 3 (slides 17-30) and Example 3b (pages 1-5) Part 3: Video
 
       
4 2/15 FA1 DUE VIA ICON BY 11:59 PM  
2/16 Lecture 4 and Example 4:
Describing Within-Person Fluctuation over Time via Alternative Covariance Structure Models
Syntax and Output Files for Example 4 (SAS, STATA, and R; NOT the same data as chapter 4 in text)
Lecture 4 (slides 1-14) Part 1: Video
Hoffman (2015) ch. 4
2/18 Lecture 4 and Example 4, continued
Lecture 4 (slides 15-17) and Example 4 (pages 1-7) Part 2: Video
 
       
5 2/22 NO HW OR FA DUE  
2/23 Lecture 4 and Example 4, continued
Lecture 4 (slides 15-22) and Example 4 (pages 8-17) Part 3: Video

2/25 Lecture 4, continued
Lecture 4 (slides 24-28) Part 4: Video

Lecture 5 and Example 5 (updated 3/17/21):
Introduction to Random Effects of Time and Model Estimation
Data, Syntax, and Output Files for Example 5 (SAS, STATA, and R; same data as chapter 5 in text; updated 3/17/21)
Lecture 5 (slides 1-10) Part 1: Video



Hoffman (2015) ch. 5
       
6 3/1 NO HW OR FA DUE  
3/2 NO CLASS OR OFFICE HOURS
 
3/4 HW1 (based on Lecture/Examples 3-4) DUE ONLINE BY 11:59 PM
Lecture 5 and Example 5, continued
Lecture 5 (slides 10-26) Part 2: Video

       
7 3/8 FA2 DUE VIA ICON BY 11:59 PM  
3/9 Lecture 5 and Example 5, continued
Lecture 5 (slide 10) and Example 5 (pages 1-7) Part 3: Video
Enders (2010) ch. 3-5
3/11 Lecture 5 and Example 5, continued
Lecture 5 (slide 10) and Example 5 (page 4-11) Part 4: Video
(recording from class had no audio; was re-recorded offline)
McNeish (2017)
       
8 3/15 NO HW OR FA DUE  
3/16 Lecture 5 and Example 5, continued
Lecture 5 (slides 24-37) and Example 5 (pages 9-14) Part 5: Video
McNeish & Matta (2018)
3/18 HW2 (based on Lecture/Example 5) DUE ONLINE BY 11:59 PM
Lecture 5, continued
Lecture 5 (slides 34-62) Part 6: Video
Stoel et al. (2006)
Yuan et al. (2019)
       
9 3/22 FA3 DUE VIA ICON UNDER ASSIGNMENTS BY 11:59 PM: Practice with MLM Notation  
3/23 FA3 Answer Key

Lecture 6: Describing Within-Person Change
Data, Syntax, and Output Files for Examples 6a-6d (SAS, STATA, and R; same data as chapter 6 in text; updated 4/5/21)
Example 6a: Modeling Change over Time with Polynomial Trends (updated 4/5/21)
SAS Program to Simulate Polynomial Trends

Lecture 6 (slides 1-9) and Example 6a (pages 1-5) Part 1: Video
Hoffman (2015) ch. 6
3/25 Lecture 6 and Example 6a, continued
Lecture 6 (none) and Example 6a (pages 5-9) Part 2: Video
 
       
10 3/29 NO HW OR FA DUE  
3/30 Lecture 6 and Example 6a, continued
Lecture 6 (slides 11-25) and Example 6 (none) Part 3: Video
 
4/1 Lecture 6 and Example 6a, continued
Lecture 6 (slide 25) and Example 6a (pages 9-18) Part 4: Video
 
       
11 4/5 FA4 DUE VIA ICON BY 11:59 PM  
4/6 Lecture 6, continued
Example 6b: Modeling Change over Time with Piecewise Trends (updated 4/5/21)
Lecture 6 (slides 26-33) and Example 6b (pages 1-8) Part 1: Video
Tuliao et al. (2017)
4/8 Lecture 6 and Example 6b, continued
Example 6d: Modeling Change over Time Using Log Time to Approximate Exponential Trends (updated 4/5/21)
Lecture 6 (slides 36-41), Example 6b (pages 9-16), and Example 6d (all) Part 2: Video

Example 6c: Modeling Change over Time with Truly Exponential Trends (updated 4/12/21)
Example 6c (all; recorded outside of class): Video
Preacher & Hancock (2015)
Johnson & Hancock (2019)
McNeish (2019)
       
12 4/12 HW3 (based on Lecture/Example 6a Polynomial Models) DUE ONLINE BY 11:59 PM  
4/13 Lecture 7a: Review of Unconditional Models of Time
Lecture 7a (slides 1-8) Part 1: Video
Walters & Hoffman (2017)
4/15 Lecture 7b and Example 7b (updated 5/4/21):
Time-Invariant Predictors in Longitudinal Models
Data, Syntax, and Output Files for Example 7b (SAS, STATA, and R; NOT the same data as chapter 7 in text; updated 4/20/21)

Lecture 7a (slides 9-15) Part 2 and Lecture 7b (slides 1-15) Part 1: Video
Hoffman (2015) ch. 7
       
13 4/19 FA5 DUE VIA ICON BY 11:59 PM  
4/20 Lecture 7b and Example 7b, continued
Lecture 7b (slides 14-22) and Example 7b (none yet) Part 2: Video
Rights & Sterba (2019, 2020)
4/22 Lecture 7b and Example 7b, continued
Lecture 7b (slides 21-30) and Example 7b (none yet) Part 3: Video
 
       
14 4/26 HW4 (based on Lecture/Example 6b Piecewise Models) DUE ONLINE BY 11:59 PM  
4/27 Lecture 7b and Example 7b, continued
Lecture 7b (slides 31-36) and Example 7b (pages 1-5) Part 4: Video
 
4/29 Example 7b, continued
Example 7b (pages 4-17) Part 5: Video
 
       
15 5/3 FA6 DUE VIA ICON BY 11:59 PM  
5/4 Example 7b, continued
Example 7b (pages 18-27) Part 6: Video
 
5/6 Example 7b, continued
Stories of Time-Invariant Predictors
Stories with Answers

Example 7b (pages 23-36) Part 7 and Stories: Video

For everything we didn't cover this semester, please visit this previous KU class on advanced MLM and the first workshop on this page: "Modeling Within-Person Associations in Longitudinal Data"


Hoffman (2015) ch. 8; Arend & Schäfer (2019); Hoffman (2015) ch. 9; Hoffman (2019); Lüdtke et al. (2008); Preacher et al. (2010, 2011, 2016); Berry & Willoughby (2017); Curran et al. (2014)
       
16 5/11 NO CLASS, but office hours will be held from 12:30-4:45 PM  
5/13 NO CLASS, but office hours will be held from 12:30-4:45 PM  
5/14 HW5 (based on Lecture/Example 7b) DUE BY 5:00 PM ONLINE
ALL OUTSTANDING WORK MUST BE COMPLETED BY 5:00 PM
 

Schedule of Topics and Events:

This course will meet synchronously on zoom. The planned schedule of topics and events may need to be adjusted throughout the course. The online syllabus above will always have the most current schedule and corresponding due dates (i.e., the printable syllabus will not be updated unless noted).

Course Objectives, Pre-Requisites, and Materials:

This course will illustrate the uses of multilevel models (i.e., general linear mixed-effect models, hierarchical linear models) for the analysis of longitudinal (and other repeated measures) data. The course is organized to take participants through each of the cumulative steps in a longitudinal analysis involving time-invariant and time-varying predictors: deciding which type of model is appropriate, organizing the data and coding predictor variables, evaluating fixed and random effects and/or alternative covariance structures, predicting multiple sources of variation, and interpreting and presenting empirical findings. Participants should be comfortable with general linear models (i.e., analysis of variance, regression) prior to enrolling in this course.

Class time will be devoted primarily to lectures, examples, and spontaneous review, the materials for which will be available for download at the course website. Attendance via zoom is encouraged but not required; I intend to make video recordings of each class available online, and supplemental videos for specific topics may be posted as well. Readings have been suggested for each topic and may be updated later. There will be no required sessions held outside the regular class time noted above (i.e., no additional midterm or final exam sessions). However, because the course will have an applied focus requiring the use of statistical software, participants are welcome to attend group-based office hours, in which multiple participants can receive immediate assistance on homework assignments simultaneously.

Course Requirements:

Participants will have the opportunity to earn up to 100 total points in this course. Up to 88 points can be earned from homework assignments (approximately 6 in total)—these will be graded for accuracy. Up to 12 points may be earned from submitting outside-of-class formative assessments (approximately 6 in total); these will be graded on effort only—incorrect answers will not be penalized. Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the above syllabus). There may be other opportunities to earn extra credit at the instructor's discretion.

UPDATE POSTED 3/15/21: HW6 has been cancelled, leading to a total of 72 possible homework points instead of 88.

Policy on Late Assignments, Formative Assessments, and Incompletes:

In order to provide participants with prompt feedback, late homework assignments will incur a 1-point penalty. However, extensions will be granted as needed for extenuating circumstances (e.g., conferences, comprehensive exams, family obligations) if requested at least two weeks in advance of the due date. Late or incomplete outside-of-class quizzes will incur a 0.5-point penalty. A final grade of "incomplete" will only be given in dire circumstances and entirely at the instructor's discretion. All work must be submitted by Friday, May 14, 2021 at 5:00 PM.

Final grades will be determined according to the proportion earned of the total possible points:

>96 = A+, 93–96 = A, 90–92 = A−, 87–89 = B+, 83–86 = B, 80–82 = B−, 77–79 = C+, 73–76 = C, 70–72 = C− (PASS),
67–69 = D+, 63–66 = D, 60–62 = D−, <60 = F

Course Software:

Participants will need to have access to software that can estimate the models presented. Although the course will feature SAS as its primary package, examples may also include other software packages (e.g., STATA, R), which could also be used to complete homework assignments. These three packages are freely available to University of Iowa members through the UIowa Virtual Desktop. A second free option for software off-campus is the Research Remote Desktop, although this option is not meant for course work. A third free option is SAS university edition. Finally, as a fourth option, 6-month student licenses for STATA may be purchased for $48 here.

Course Textbook:

Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.
University of Iowa library link to textbook

Other Course Readings (available via "Files" in ICON):

Note—I know this is A LOT of readings, but we are covering a lot of material! I encourage you to prioritize reading the textbook, as it will map most closely onto what we cover in class. Then should come class participation and completing course work, followed by these extra readings as time permits (included to give you some additional background and/or exposure to current best-practices in each topic).

Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological Methods, 24(1), 1–19. https://doi.org/10.1037/met0000195

Berry, D., & Willoughby, M. (2017). On the practical interpretability of cross ‐ lagged panel models: Rethinking a developmental workhorse. Child Development, 88(4), 1186–1206. https://doi.org/10.1111/cdev.12660

Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82(5), 879–894. https://doi.apa.org/doi/10.1037/a0035297

Enders, C. K. (2010; chapters 3–5). Applied missing data analysis. New York, NY: Guilford.

Hoffman, L. (2019). On the interpretation of parameters in multivariate multilevel models across different combinations of model specification and estimation. Advances in Methods and Practices in Psychological Science, 2(3), 288–311. https://doi.org/10.1177%2F2515245919842770

Johnson, T. L., & Hancock, G. R. (2019). Time to criterion latent growth models. Psychological Methods, 24(6), 690–707. https://doi.org/10.1037/met0000214

Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13(3), 203–229. https://doi.org/10.1037/a0012869

McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction. Multivariate Behavioral Research, 52(5), 661–670. https://doi.org/10.1080/00273171.2017.1344538

McNeish, D. (2020). Relaxing the proportionality assumption in latent basis models for nonlinear growth. Structural Equation Modeling, 27(5), 817–824. https://doi.org/10.1080/10705511.2019.1696201

McNeish, D., & Matta, T. (2018) Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50, 1398–1414. https://doi.org/10.3758/s13428-017-0976-5

Preacher, K. J., & Hancock, G. R. (2015). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Psychological Methods, 20(1), 84–101. https://doi.org/10.1037/met0000028

Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling, 18(2), 161–182. https://psycnet.apa.org/doi/10.1080/10705511.2011.557329

Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2016). Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychological Methods, 21(2), 189–205. https://doi.org/10.1037/met0000052

Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209–233. https://doi.apa.org/doi/10.1037/a0020141

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309–338. https://doi.org/10.1037/met0000184

Rights, J. D., & Sterba, S. K. (2020). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research, 55(4), 568–599. https://doi.org/10.1080/00273171.2019.1660605

Stoel, R. D., Garre, F. G., Dolan, C., & van den Wittenboer, G. (2006). On the likelihood ratio test in structural equation modeling when parameters are subject to boundary constraints. Psychological Methods, 11(4), 439–455. https://doi.org/10.1037/1082-989X.11.4.439

Tuliao, A. P., Hoffman, L. , & McChargue, D. E. (2017). Measuring individual differences in responses to date-rape vignettes using latent variable models. Aggressive Behavior, 43(1), 60-73. https://doi.org/10.1002/ab.21662

Walters, R. W., & Hoffman, L. (2017). Applying the hierarchical linear model to longitudinal data / La aplicación del modelo lineal jerárquico a datos longitudinales, Cultura y Educación, 29(3), 666–701. https://doi.org/10.1080/11356405.2017.1367168

Willett, J.B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49, 587-602. https://doi.org/10.1177%2F001316448904900309

Yuan, K.-H., Zhang, Z., & Deng, L. (2019). Fit indices for mean structures with growth curve models. Psychological Methods, 24(1), 36–53. https://doi.org/10.1037/met0000186

Academic Misconduct:

As a reminder, the University of Iowa College of Education has a formal policy on academic misconduct, which all students in this course are expected to follow. Please consult the instructor if you have questions.

Accommodating Students with Disabilities:

Students with disabilities or who have other special needs are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.

Respect for Diversity:

It is the instructor's intent that students from ALL backgrounds and perspectives feel welcome and encouraged to participate in this course. There is no such thing as a "stupid" question (or answer). All course participants—enrolled students and auditing visitors—should always feel welcome to ask whatever questions will be helpful in helping them understand and follow the course content. You may do so during class, in office hours, over email, or in individual appointments with the instructor (available by request).

With respect to zoom class sessions, please provide the name you wish for me to call you inside your zoom account (i.e., so that it appears on your window while in use) along with a picture—it can be a real picture, an avatar, or a cartoon—just something by which I can use differentiate you. Student use of cameras and microphones during class is also encouraged but not required (out of respect for your privacy and/or limited bandwidth). Please note that class video recordings streamed to YouTube will NOT include any video from course participants (only the zoom audio and screen share from the instructor will be captured).

The University of Iowa is committed to making the classroom a respectful and inclusive space for people of all gender, sexual, racial, religious, and other identities. Toward this goal, students are invited in MyUI to optionally share the names and pronouns they would like their instructors and advisors to use to address them. The University of Iowa prohibits discrimination and harassment against individuals on the basis of race, class, gender, sexual orientation, national origin, and other identity categories set forth in the University's Human Rights policy. For more information, contact the Office of Equal Opportunity and Diversity.

Respect for The Rest of Your World:

The instructor realizes that this course is not your only obligation in your work or your life. If work or life events (expected or unexpected) may compromise your ability to succeed in this course, PLEASE contact the instructor for a confidential discussion (in person or over email, as you prefer) so that we can work together to make a plan for your success. Please do not wait to do so until you are too far behind to catch up!