Instructor:  Dr. Lesa Hoffman  Email:  Lesa@ku.edu 
Rooms:  3049 Dole  Office:  3042 Dole 
Time:  1:152:30 Mondays and Wednesdays  Office Hours:  2:304:00 Mondays and Wednesdays in 3049 or 3042 Dole or by appointment 
GTA:  Jihong Zhang  Email:  jihong.zhang@ku.edu 
Office Hours:  1:004:00 Thursdays in 3049 Dole or by appointment  
CLDP 948 Online Homework Portal is now available! 
Mplus Website (for examples, documentation, and other resources) Mplus Online Manual Lesa's Mplus Guide from www.PilesofVariance.com 
Week 
Date 
Course Materials 
Readings 

1  8/20  Course Introduction Lecture 1: Introduction to Latent Trait Measurement Models Lecture 1 Part 1: Video 
John & BenetMartinez (2014) Tay & Jeff (2018) 
8/22  Lecture 1, continued Lecture 1 Part2: Video 
Davidson et al. (2016)  
8/24  NO HOMEWORK DUE  
2  8/27  Lecture 2: Exploratory Factor Analysis and Principal Components Analysis Lecture 2: Video Lecture 3: Classical Test Theory for Scale Reliability and Validity Lecture 3 Part 1: Video 
Brown ch. 2 Preacher & McCollum (2003) 
8/29  Lecture 3, continued Lecture 3 Part 2: Video NO OFFICE HOURS 
McDonald (1999) ch. 57 

8/31  HOMEWORK 0 DUE BY 11:59 PM ONLINE: 3 points extra credit for testing the online homework system 

3  9/3  NO CLASS OR OFFICE HOURS  
9/5  QUIZ 1 DUE BY 1:00 PM VIA BLACKBOARD Example 3: Classical Items Analysis in SPSS and SAS Example 3: Video Lecture 4: Confirmatory Factor Analysis (CFA) Models Lecture 4 Part 1: Video 
McNeish (2017) Brown ch. 3 

9/7  HOMEWORK 1 DUE BY 11:59 PM VIA BLACKBOARD: Background Check for Your Instrument 

4  9/10  Lecture 4, continued Lecture 4 Part 2: Video Example 4: Confirmatory Factor Models in Mplus and SAS Mixed (CFA spreadsheet) (Mplus input and output files) 
Brown ch. 45 
9/12  Lecture 4 and Example 4, continued Example 4 Part 1: Video 
Ferrando (2009)  
9/14  NO HOMEWORK DUE  
5  9/17  Lecture 4 and Example 4, continued Lecture 4 Part 3: Video Lecture 4 Part 4: Video 
McNeish et al. (2018) Shi et al. (2018) 
9/19  QUIZ 2 DUE BY 1:00 PM VIA BLACKBOARD Lecture 4 and Example 4, continued Example Loglikelihood Spreadsheet Lecture 4 and Example 4 Part 2: Video 
Enders (2010) ch. 35  
9/21  HOMEWORK 1 REVISION DUE BY 11:59 PM VIA BLACKBOARD  
6  9/24  Lecture 4 and Example 4, continued Lecture 4 and Example 4 Part 3: Video 

9/26  Lecture 4 and Example 4, continued Lecture 4 and Example 4 Part 4: Video 

9/28  NO HOMEWORK DUE  
7  10/1 
Lecture 4 and Example 4, continued Lecture 4 and Example 4 Part 5: Video 

10/3  Lecture 5: Latent Trait Measurement Models for Binary Responses HW2 Questions and Lecture 5 Part 1: Video 
E & R (2000) ch. 34, 78 Mungas & Reed (2000) 

10/5  HOMEWORK 2 DUE BY 11:59 PM ONLINE: Practice with CFA 

8  10/8  Lecture 5, continued Lecture 5 Part 2: Video 
Wirth & Edwards (2007) 
10/10  QUIZ 3 DUE BY 1:00 PM VIA BLACKBOARD Example 5: Binary Item Response Models in Mplus (Spreadsheet) (Mplus output files) Lecture 5 and Example 5 Part 3: Video 
Paek et al. (2018)  
10/12  NO HOMEWORK DUE  
9  10/15  NO CLASS OR OFFICE HOURS  
10/17  Lecture 5 and Example 5, continued Lecture 5 and Example 5 Part 4: Video 

10/19  HOMEWORK 3 DUE BY 11:59 PM VIA BLACKBOARD: CFA Using Your Own Data 

10  10/22  Lecture 5 and Example 5, continued Sorry, no video today! 
MaydeuOlivares (2015) 
10/24  QUIZ 4 DUE BY 1:00 PM VIA BLACKBOARD Lecture 6: Latent Trait Measurement Models for Other Item Responses Example 6a: Graded Response Models for Ordinal Responses in Mplus (Spreadsheet) (Mplus output files) Lecture 6 and Example 6a Part 1: Video 
E & R ch. 5  
10/26  NO HOMEWORK DUE  
11  10/29  Lecture 6 and Example 6a, continued Example 6b: Measurement Models for SemiOrdered (Not Applicable) Responses in Mplus Lecture 6 and Example 6b Part 2: Video Bonus Example 6c: Measurement Models for Other NonNormal Outcomes in Mplus (Spreadsheet) (Mplus output files) Video from 2016 for end of Lecture 6 and Example 6c (starts at 12:30) 
HugginsManley et al. (2017) Bauer & Hussong (2009) 
10/31  OFFICE HOURS END AT 3:00 Lecture 7: Measurement Invariance in CFA and Differential Item Functioning in IRT/IFA Example 7a: MultipleGroup Measurement Invariance in CFA using Mplus (spreadsheet) (Mplus output files) Lecture 7 and Example 7a Part 1: Video 
Brown ch. 7 Vandenberg & Lance (2000) 

11/2  NO HOMEWORK DUE  
12  11/5 
ONLY GTA OFFICE HOURS FROM 1:154:00
HOMEWORK 4 DUE BY 11:59 PM ONLINE: Practice with IRT/IFA 

11/7 
Lecture 7 and Example 7a, continued Example 7b: Longitudinal Measurement Invariance in CFA using Mplus (spreadsheet) (Mplus output files) Lecture 7 and Example 7a Part 2: Video 
Edwards & Wirth (2009)  
11/9  HOMEWORK 3 REVISION DUE BY 11:59 PM VIA BLACKBOARD  
13  11/12  QUIZ 5 DUE BY 1:00 PM VIA BLACKBOARD Lecture 7 and Example 7b, continued Lecture 7 and Example 7b, 7c Part 3: Video Example 7c: MultipleGroup Measurement Invariance in IFA using Mplus WLSMV (spreadsheet) (Mplus output files) 
Curran et al. (2014) 
11/14  Example 7d: MultipleGroup Measurement Invariance in IFA using Mplus ML (spreadsheet) (Mplus output files) Lecture 7 and Example 7c, 7d Part 4: Video 

11/16  HOMEWORK 5 DUE BY 11:59 PM VIA BLACKBOARD: IRT/IFA Using Your Own Data 

14  11/19  NO CLASS OR OFFICE HOURS  
11/21  NO CLASS OR OFFICE HOURS  
11/23  NO HOMEWORK DUE  
15  11/26  NO CLASS OR OFFICE HOURS 

11/28  Lecture 8: HigherOrder and Method Factor Models Example 8: HigherOrder CFA and IFA Models in Mplus (spreadsheet) (Mplus output files) Lecture 8 Part 1: Video (sorry, my microphone died! ) 
Brown ch. 8 MaydeuOlivares & Coffman (2006) Chen et al. (2006) Reise (2012) 

11/30  NO HOMEWORK DUE  
16  12/3  HOMEWORK 6 DUE BY 11:59 PM ONLINE: Practice with Measurement Invariance Lecture 8, continued Lecture 8 Part 2: Video Lecture 9: Path Modeling and Structural Equation Modeling Example 9a: Path Modeling with Mediation in Mplus (Mplus output files) Lecture 9 and Example 9a Part 1: Video 
MacKinnon (2008) ch. 6 Asparouhov & Muthén (2010) 
12/5  QUIZ 6 DUE BY 1:00 PM VIA BLACKBOARD Lecture 9, continued Example 9b: Path Modeling with NonNormal Outcomes in Mplus (in Lecture 9) Example 9c: Structural Equation Modeling in Mplus (Mplus output files) Lecture 9 and Example 9c Part 2: Video 
Curran et al. (2018)  
12/7  HOMEWORK 5 REVISION DUE BY 11:59 PM VIA BLACKBOARD  
17  12/12  ALL HOMEWORK AND HOMEWORK REVISIONS DUE BY 11:59 PM 
This course will contemporary approaches to measurement, expanding from classical test theory into confirmatory factor models, item response models, and their use within structural equation models. In addition to the statistical models, the course will also focus on the measurement concepts behind these models and how they relate to each other with respect to scale construction and evaluation. 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 can be requested. Video recordings of the class lectures will also be available online, but are not intended to take the place of class attendance. Selected book chapters and journal articles will be assigned for each specific topic as needed; the initial list of readings below may be updated. Because the course will have an applied focus using Mplus software, instructor and GTA office hours will be held in the 3049 Dole computer lab, in which participants will have opportunities to work on assignments and receive immediate software assistance. Participants should be comfortable with the general linear model (analysis of variance, regression) prior to enrolling in this course.
As a reminder, the University of Kansas has a formal policy on academic honesty. All assignments should be done individually without exception.
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.
Participants will have the opportunity to earn up to 100 total points in this course. Up to 82 points can be earned from submitting 6 homework assignments. Up to 18 points may be earned from submitting 6 outsideofclass quizzes. Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the online syllabus for more information). There may be other opportunities to earn extra credit at the instructor’s discretion.
In order to be able to provide prompt feedback and class discussion of homework problems, late homework assignments will incur a 3point penalty. However, extensions will be granted as needed for extenuating circumstances (e.g., conferences, family obligations) if requested at least two weeks in advance of the due date. Late or incomplete outsideofclass quizzes will incur a 1point penalty when submitted. Finally, a final grade of “incomplete” will only be given in the event of dire circumstances and at the instructor's discretion. Homework assignments that involve individual writing will have the opportunity to be revised ONCE to earn the maximum total points. Written assignments must be at least ¾ complete to be accepted, and late revisions will incur a 1point penalty. No late points will be returned through the revision process. Please use “track changes” and retain all original instructor comments (unless otherwise instructed) so that I can easily see how your revisions address the comments.
>92 = A, 90–92 = A, 8789 = B+, 8386 = B, 8082 = B, <80 = C or no pass
Participants will need to have access to Mplus software, which is available in 3049 Dole and in the GIS and Data Lab in 425 Watson Library. Individual student licenses can also be purchased from the statmodel.com ($350 each; no expiration date).
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: Guilford.
Asparouhov, T., & Muthén, B. (2010). Plausible values for latent variables using Mplus. Retrieved from http://www.statmodel.com/download/Plausible.pdf.
Bauer, D. J., & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14(2), 101125.
Chen, F., F., West, S. G., & Sousa, K. H. (2006). A comparison of befactor and secondorder models of quality of life. Multivariate Behavioral Research, 41, 189225.
Curran, P. J., Cole, V. T., Bauer, D. J., Rothenberg, A., & Hussong, A. M. (2018). Recovering predictor–criterion relations using covariateinformed factor score estimates. Structural Equation Modeling. Retrieved August 2018, https://doi.org/10.1080/10705511.2018.1473773.
Curran, P. J., McGinley, J. S., Bauer, D. J., Hussong, A. M., Burns, A., Chassin, L., Sher, K., & Zucker, R. (2014). A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivariate Behavioral Research, 49(3), 214231.
Davidson, C. A., Hoffman, L., & Spaulding, W. D. (2016). Schizotypal personality questionnaire – brief revised (updated): An update of norms, factor structure, and item content in a large nonclinical young adult sample. Psychiatry Research, 238, 345355.
Edwards, M. C., & Wirth, R. J. (2009). Measurement and the study of change. Research in Human Development, 62(23), 7496.
E & R: Embretson, S. E., & Reise, S. T. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Ferrando, P. J. (2009). Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Applied Psychological Measurement, 33(1), 924.
HugginsManley, A. C., Algina, J. & Zhou, S. (2018). Models for semiordered data to address not applicable responses in scale measurement. Structural Equation Modeling, 25(2), 230243.
John, O. P., & BenetMartinez, V. (2014). Measurement: Reliability, construct validation, and scale construction . In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 3473503, 2nd ed.). New York, NY: Cambridge University Press.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Routledge Academic.
MaydeuOlivares, A. (2015). Evaluating the fit of IRT models. In S. P. Reise & D. A. Revicki (Eds.), Handbook of item response theory modeling (pp. 111127). New York, NY: Taylor & Francis.
MaydeuOlivares, A., & Coffman, D. L. (2006). Random intercept item factor analysis. Psychological Methods, 11, 344362.
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.
McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000144
McNeish, D., An J., & Hancock, G. R. (2018). The thorny relation between measurement quality and fit index cutoffs in latent variable models. Journal of Personality Assessment.
Mungas, D., & Reed, B. R. (2000). Application of item response theory for development of a global functioning measure of dementia with linear measurement properties. Statistics in Medicine, 19, 16311644.
Paek, I., Cui, M., Gübes, N. O., & Yang, Y. (2018). Estimation of an IRT model by Mplus for dichotomously scored responses under different estimation methods. Educational and Psychological Measurement, 78(4), 569588.
Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding Statistics, 2(1), 1343.
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667696.
Sheu, C.F., Chen, C.T., Su, Y.H., & Wang, W.C. (2005). Using SAS PROC NLMIXED to fit item response theory models. Behavior Research Methods, 37(2), 202218.
Shi, D., Lee, T., & MaydeuOlivares, A. (2018). Understanding the model size effect on SEM fit indices. Educational and Psychological Measurement.
Tay, L., & Jebb, A. T. (2018). Establishing construct continua in construct validation: The process of continuum specification. Advances in Methods and Practices in Psychological Science, retrieved July 2018. https://doi.org/10.1177/2515245918775707.
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 469.
Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 5879.