Course Syllabus

Title of Course [Missing Data Analysis]

Prefix and Number [ED_PSYCH 580]

Semester and Year [Fall 2026]

Number of Credit Hours [3]

Prerequisites [ED_RES 565 Quantitative Research]

Recommended Preparation [ED_PSYCH 569 Seminar in Quantitative Techniques in Education]

Course Details

Day and Time: Thursday, 4:10 PM – 7:00 PM

Meeting Location: Cleveland Hall 63

Instructor Contact Information

Instructor Name: Shenghai Dai

Instructor Contact Information:

  • Office: Cleveland Hall 354
  • Phone: (509) 335-0958
  • Email: s.dai@wsu.edu

Instructor Office Hours: Thursdays: 2:00 – 4:00 PM or by appointment

TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [tbd]

Course Description

Ed Psych 580 aims to introduce classical and novel methods for handling missing data and their applications in broad educational contexts. Major topics include missing data patterns and mechanisms, common imputation methods, missing data with full information maximum likelihood, multiple imputation, handling missing data across contexts including survey and assessment data, multilevel modeling, structural equation modeling, and longitudinal data.

The course will primarily consist of lectures and practical exercises based on the assigned readings and examples. Each topic will be introduced with a lecture, followed by a prompted real data example and reflection on the topic discussed. During the semester, you are expected to complete readings prior to class, engage in practice and discussion during class periods, and complete several assignments. You are expected to select a topic of your interest within your area of research and provide in-depth analysis in both written and oral forms (see more detail below under Research Project). You are expected to actively participate and build on the knowledge previously acquired and to gain technical foundations necessary to handle missing data appropriately across contexts.

Course Materials 

Books

  • Van Buuren, S., & Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). Boca Raton, FL: CRC press. Free online access: https://stefvanbuuren.name/fimd

Other Materials: 

Other/Alternate Textbooks (Only Optional)

  • Graham, J. W. (2012). Missing data: Analysis and design. Springer.
  • Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). John Wiley & Sons.
  • Enders, C. K. (2022). Applied missing data analysis. Guilford Publications.

Recommended Other Book

  • American Psychological Association (2019). Publication manual of the American Psychological Association (7th ed.). Washington, D. C.: Author.

Suggested Readings (for assigned readings, see Tentative Class Schedule)

  • Overall and General Issues of Missing Data
    • Enders, C. K. (2023). Missing data: An update on the state of the art. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000563
    • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147.
    • Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74(4), 525–556.
    • Peng, C.-Y. J., Harwell, M., Liou, S.-M., Ehman, L. H., & others. (2006). Advances in missing data methods and implications for educational research. Real Data Analysis, 31–78.
    • Dong, Y., & Peng, C.-Y. J. (2013). Principled missing data methods for researchers. SpringerPlus, 2(1), 222. https://doi.org/10.1186/2193-1801-2-222
    • Cheema, J. R. (2014). Some general guidelines for choosing missing data handling methods in educational research. Journal of Modern Applied Statistical Methods, 13(2), 3.
    • Little, R. J., & Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods & Research, 18(2–3), 292–326.
  • Multiple Imputation
    • Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8(1), 3–15.
    • Rubin, D. B. (1988, August). An overview of multiple imputation. In Proceedings of the survey research methods section of the American Statistical Association (Vol. 79, p. 84).
    • Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206-213.
  • Missing Data in Survey and Assessment Data (Factor Analytic Models)
    • Goretzko, D. (2022). Factor retention in exploratory factor analysis with missing data. Educational and Psychological Measurement, 82(3), 444-464.
    • Mirzaei, A., Carter, S. R., Patanwala, A. E., & Schneider, C. R. (2022). Missing data in surveys: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy, 18(2), 2308-2316.
    • Bernaards, C. A., & Sijtsma, K. (2000). Influence of imputation and EM methods on factor analysis when item nonresponse in questionnaire data is nonignorable. Multivariate Behavioral Research, 35(3), 321–364.
    • Chen, S.-F., Wang, S., & Chen, C.-Y. (2012). A simulation study using EFA and CFA programs based the impact of missing data on test dimensionality. Expert Systems with Applications, 39(4), 4026–4031.
    • Dai, S. (2021). Handling missing responses in psychometrics: Methods and software. Psych, 3, 673-693. 
    • Dai, S., Vo, T., Kehinde, O.J., He, H., Xue, Y., Demir, C., & Wang, X. (2021). Performance of polytomous IRT models with rating scale data: An investigation over sample size, instrument length, and missing data. Frontiers in Education – Assessment, Testing and Applied Measurement. 6, 721963. 
  • Missing Data in Structure Equation Modeling
    • Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52(3), 431–462.
    • Enders, C. K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6(4), 352.
    • Enders, C. K. (2013). Analyzing structural equation models with missing data. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 493–520). Information Age Publishing, Inc.
    • Lee, T., & Shi, D. (2021). A comparison of full information maximum likelihood and multiple imputation in structural equation modeling with missing data. Psychological methods, 26(4), 466.
  • Missing Data in Multilevel Modeling
    • Lüdtke, O., Robitzsch, A., & Grund, S. (2017). Multiple imputation of missing data in multilevel designs: A comparison of different strategies. Psychological methods, 22(1), 141.
    • Grund, S., Lüdtke, O., & Robitzsch, A. (2018). Multiple imputation of missing data for multilevel models: Simulations and recommendations. Organizational Research Methods, 21(1), 111-149.
    • Van Buuren, S. (2011). Multiple imputation of multilevel data. In Handbook of advanced multilevel analysis (pp. 173-196). Routledge.
    • Drechsler, J. (2015). Multiple imputation of multilevel missing data—Rigor versus simplicity. Journal of Educational and Behavioral Statistics, 40(1), 69-95.
    • Black, A. C., Harel, O., & Betsy McCoach, D. (2011). Missing data techniques for multilevel data: Implications of model misspecification. Journal of Applied Statistics, 38(9), 1845-1865.
  • Missing Data in Longitudinal Research
    • Enders, C. K. (2011). Analyzing longitudinal data with missing values. Rehabilitation psychology, 56(4), 267.
    • Twisk, J., & de Vente, W. (2002). Attrition in longitudinal studies: How to deal with missing data. Journal of clinical epidemiology, 55(4), 329-337.
    • Huque, M. H., Carlin, J. B., Simpson, J. A., & Lee, K. J. (2018). A comparison of multiple imputation methods for missing data in longitudinal studies. BMC medical research methodology, 18, 1-16.
    • Nooraee, N., Molenberghs, G., Ormel, J., & Van den Heuvel, E. R. (2018). Strategies for handling missing data in longitudinal studies with questionnaires. Journal of Statistical Computation and Simulation, 88(17), 3415-3436.

Software

Computer lab work is a component of the course. This will give the student the opportunity to apply what is discussed in class. The R software (https://cran.r-project.org) is used as the major tool for the class. Other software (SPSS, Jamovi) will also be introduced as time allows or need arises. All the software tools are free of cost. To get familiar with R modeling with missing data, the following resources can be useful.

  • R website: https://cran.r-project.org/
  • R::mice package site: https://github.com/amices/mice
  • Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1-67.
  • Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Multiple imputation of multilevel missing data: an introduction to the R package pan. Sage Open, 6(4), 2158244016668220.

Fees: [NA]

Course Format

The class will include a variety of activities: individual work, class discussions, lab practices, small group work and presentations. Please come to class prepared to discuss reading and other assignments.

Student Learning Outcomes (SLOs) 

Course Learning Outcomes (students will be able to:)

 Activities Supporting the Learning Outcomes Assessment of the Learning Outcomes
Understand and articulate different patterns and mechanisms of missing data.

Week 1: missing data patterns and mechanisms

Lab practice; Homework #1

Examine and impute univariate and multivariate missing data using covered methods (predictions, predictive mean matching, joint modeling,  and fully conditional specification).

Weeks 2-3: Examine and impute univariate and multivariate missing data

 Lab practice; Homework #1

Critically review education research with missing data, compare different missing data approaches and FIML.

Weeks 4-6: Review of missing data in educational research, compare different missing data methods vs. FIML

Lab practice; Homework #2

Understand the patterns of missing data in survey and assessment data, and impute for missing responses for factor analysis and psychometric modeling.

Weeks 7-8: missing data in survey and assessment data

Lab practice; Homework #3

Apply multiple imputation and conduct analysis using imputed data.

Weeks 9-10: multiple imputation and analysis of imputed data

Lab practice; Homework #4

Handling missing data in structural equation modeling.

Week 11: missing data in structural equation modeling

 Lab practice; Homework #5

Handling missing data in multilevel modeling.

Week 12: missing data in multilevel modeling

 Lab practice; Homework #5

Handling missing data in longitudinal data design and analysis.

Week 13: missing data in longitudinal designs and analysis

 Lab practice; Homework #5

Implement different missing data methods in your area of research, propose and accomplish a research project (proposal, presentation, and research paper).

Weeks 1-16;

(Week 15: project work and consultation; Week 16: project presentation)

Project proposal; presentation;

Research project.

Course Schedule

[Please note that a WSU semester is 15 weeks + Thanksgiving/Spring Break. The schedule below does not include the break.]
Dates Lesson Topic Assignment Assessment

Week 1
[08-27]

  Missing data patterns and mechanisms   Van Buuren (2018, CH1);
  Enders (2023);
  Schafer & Graham (2002)		   HW#1
Week 2
[09-03]

Univariate missing data and handling - predictions and predictive mean matching

 Van Buuren (2018, CH3);
 Little & Rubin (1989)
Week 3
[09-10]		  Multivariate missing data and imputation – joint modeling and fully conditional specification

 Van Buuren (2018, CH4)
Week 4
[09-17]

Review of missing data issues in education research

 Peng et al. (2006);
 Dong & Peng (2013);
 Cheema (2014)

   HW#2
Week 5
[09-24]

Lab – comparison of different imputation methods
Week 6
[10-01]

Missing data and FIML
Week 7
[10-08]

Missing data in survey and assessment data – attrition, compliance, and imputation (I)

 Goretzko (2002);
 Mirzaei et al. (2022);
 Bernaards & Sijtsma (2000);
 Chen et al. (2012);
 Dai (2021); Dai et al. (2021)

  HW#3
Week 8
[10-15]

Missing data in survey and assessment data – factor analysis and IRT models (II)
Week 9
[10-22]

Multiple imputation

 Van Buuren (2018, CH2);
 Schafer (1999);
 Graham et al. (2007)

    HW#4
Week 10
[10-29]

Analysis of imputed data

 Van Buuren (2018, CH5)
Week 11
[11-05]

Missing data in structural equation modeling

 Muthén et al. (1987);
 Enders (2001);
 Enders (2013);
 Lee & Shi (2021)

    HW#5
Week 12
[11-12]

 Missing data in multilevel modeling

 Van Buuren (2018, CH7);
 Lüdtke et al. (2017);
 Grund et al. (2018);
 Black et al. (2011)
Week 13
[11-19]

 Missing data in longitudinal designs and analysis

 Van Buuren (2018, CH11);
 Enders (2011);
 Twisk & de Vente (2002);
 Nooraee et al. (2018)
Week 14
[12-03]
  Project work and consultation      NA Research Project
Week 15
[12-10]
  Project Presentations    NA  Presentation

Notes: HW = homework

  • The instructor reserves the right to adjust the schedule as needed.
  • Additional readings may be assigned.

Expectations for Student Effort 

For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class. I expect all students to (a) attend class on time, (b) participate actively in class discussions, (c) read all assigned readings, and (d) turn in assignments on time. We will try to have lab time each class session to practice what we have discussed. If you are unable to attend class, please notify me in advance. You are responsible for the information missed during your absence. No late assignments are accepted for credit.

Grading 

Assignment Breakdown
Type of Assignment (tests, papers, etc) Points Percent of Overall Grade
Homework Assignment 50 50%
Research Project Proposal 10 10%
Research Project Presentation 10 10%
Research Project Paper 30  30%

Grades will be based on (a) homework assignments (50%) and (b) research project (50%). You are encouraged to work together on the data analysis part of the assignment and assist each other with the course material. However, all the other parts of the assignments, including article critique, results write-up for the analysis, and responses to the questions, should be your own work. Academic honesty is expected. Late Policy: Assignments turned in after the due date will not be eligible for credit toward the final grade you earn. Late assignments will be worth 0 points.

The course grade a student earns is determined by the following combination of assessments of the objectives listed above. I also note that you should expect to spend a few hours on homework assignments and work throughout the semester on your project. A last-day effort on assignments is not a robust strategy for mastering the content.

Homework Assignments [50%]

There will be a total of five homework assignments throughout the course [10% each]. The assignments will consist of practices or problems in related topics discussed in class, but also may include other learning/practice experiences. The data for the assignments and further assignment information will be provided. No make-up assignments will be offered. Late assignments are not accepted for credit. Assignments are due at the beginning of class, with no exceptions.

Research Project & Presentation [50%]

The purpose of the project is to provide you with an opportunity to apply skills learned in this class by investigating a problem of your interest through simulated or real data analysis. The project involves conducting a latent variable and structural equation modeling based study on data that are of interest to you. The dataset can be obtained from one of your professors, colleagues, or one that you have collected. A methodological study (i.e., simulation study) of an aspect of methodology is also encouraged. If you have questions about a data source, please ask. I can also generate data for you but need sufficient time to do so (i.e., 3-4 weeks). Projects will be presented to the class at the end of the semester. The written report is due 11: 59 PM on the Friday of the final exam week.

There are three graded components of the project:

  • Project proposal (10%)

The project proposal should focus mainly on the research question, a brief literature review, and the study design (1-3 pages);

  • Project presentation (10%)

Each individual or group will present their project at the end of the semester. This will be in the conference presentation style. Each team will have 10-12 minutes to present the project and a chance to answer questions from their colleagues.

  • Project paper (30%)

The project report must be typed (double-spaced) and follow APA format (7th edition). The APA style manual is available at the bookstore and in the reference section of the library. Font size should be no smaller than 10 or larger than 12 point. Page margins should be 1.0 inch. The paper should be written in a form suitable for publication or submission for a conference paper in your area with a limit of 800 -2000 words, excluding references, tables, and figures. The range of possible projects is very broad and each paper should include: a) title page ( title ≤ 15 words), b) abstract (≤ 250 words), c) introduction (theoretical rationale, literature review, purpose statement, & hypothesis), d) method and study design, e) analysis and results, f) discussion (implications, limitations, etc.), and g) references. Computer programs (e.g., R code) and sample output from the analysis must be provided with the paper. More details will be given in class. Please proofread your work carefully. Incorrect grammar, misspelled words, and not following APA format are unacceptable. Projects given to me after the due date will not be eligible for credit toward your final grade.

Grading Schema
Grade Percent Grade Percent
A

100.00-93.00%

 C

76.99-73.00%
A- 

92.99-90.00%

 C-

72.99-70.00%
B+

89.99-87.00%

 D+

69.99-67.00%
B

86.99-83.00%

 D

66.99-60.00%
B-

82.99-80.00%

 F

59.99% or below
C+

79.99-77.00%

  

Note: I reserve the right to change the scale if in favor of the student and I round to the nearest whole number.

Attendance and Make-Up Policy 

Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and makeup class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in a reduction in the overall grade in the class. 

Assigning Incompletes: University policy (Acad. Reg. #90) states that Incompletes may only be awarded if: "the student is unable to complete their work on time due to circumstances beyond their control."

Academic Integrity Statement

You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law.

Academic integrity is the cornerstone of higher education. As such, all members of the university community share responsibility for maintaining and promoting the principles of integrity in all activities, including academic integrity and honest scholarship. Academic integrity will be strongly enforced in this course. Students who violate WSU’s Academic Integrity Policy (identified in Washington Administrative Code (WAC) 504-26-010(3) and -404) will result in action of failing the assignment and/or course, depending on the nature of the offense in accord with the policy, will not have the option to withdraw from the course pending an appeal, and will be reported to the Office of Student Conduct.

Cheating includes, but is not limited to, plagiarism and unauthorized collaboration as defined in the Standards of Conduct for Students, WAC 504-26-010(3). You need to read and understand all of the definitions of cheating: http://app.leg.wa.gov/WAC/default.aspx?cite=504-26-010. If you have any questions about what is and is not allowed in this course, you should ask course instructors before proceeding.

If you wish to appeal a faculty member's decision relating to academic integrity, please use the form available at conduct.wsu.edu.”

Attention to this policy is particularly important in a course like ED_PSYCH 580, in which collaboration with other students is encouraged. Specifically, you can only make use of the following sources when working on your homework assignments and exams. Other sources such as asking or paying others to do the work or similar are not acceptable and will be treated as violations of the WSU academic code (WAC 504-26-010).

  • Lecture and lab notes, course materials, or other public-available text-based sources. These materials should not be used by copying and pasting, and should be credited appropriately (e.g., cited).
  • Discussion with peers in class. If you work with other students during the planning, execution, or interpretation of your data analyses – a process that I support – you should make sure that the other students’ contributions are recognized explicitly in your written account.
  • Asking me for help.

If you cheat in your work in this class you will:

  • Fail the course
  • Be reported to the Center for Community Standards
  • Have the right to appeal my decision
  • Not be able to drop the course or withdraw from the course until the appeals process is finished.

If you have any questions about what you can and cannot do in this course, ask me.

If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.

University syllabus statement and link

Students are responsible for reading and understanding all university-wide policies and resources pertaining to all courses (for instance: accommodations, care resources, policies on discrimination or harassment), which can be found in the university syllabus.