|Instructor:||Dr. Lesa Hoffman|
|Room:||3049 Dole||Office:||3042 Dole|
|Time:||Wednesdays 12:00-2:30||Office Hours:||Wednesdays 2:30-4:00 in 3049 Dole|
|How to Use the KU Advanced Computing Facility (last updated 5/15/2015)
STATA test files
SAS test files
|Link to SAS University Edition|
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.
MP4 files were recorded from the class lecture (right-click, use save target/link as).
|1||8/27||Discussion using your Data Analysis Outline|
|8/29||COMPLETED DATA ANALYSIS OUTLINE DUE BY 11:59 PM VIA EMAIL|
|2||9/3||Lecture 1: A Re-Introduction to General Linear Models
Example 1: Practice with Main Effects in General Linear Models
Example 1: Data, Syntax, and Output
Lecture 1 and Example 1: Flash Video
|Hoffman (2014) ch. 2 sec. 1
Enders (2011) ch. 3 p. 57-71
|9/5||NO HOMEWORK DUE|
|3||9/10||Lecture 2: Interactions among Continuous Predictors
Example 2: Practice with Interactions among Continuous Predictors
Example 2: Data, Syntax, Output, and Excel
Lecture 2 and Example 2: No Video, sorry
|Hoffman (2014) ch. 2 sec. 2|
|9/12||HW1 DUE BY 11:59 PM VIA BLACKBOARD: Download HW1
General Feedback on HW1
|4||9/17||Example 2, continued: Flash Video
and M4V Video
Lecture 3: Interactions among Categorical Predictors
Example 3: Practice with Interactions among Categorical Predictors
Example 3: Data, Syntax, and Output
Lecture 3 and Example 3: Flash Video
|Hoffman (2014) ch. 2 sec. 3-8|
|9/22||HW2 DUE BY 11:59 PM VIA BLACKBOARD: Download HW2|
|5||9/24||Lecture 4: "Other" Kinds of Effects in General Linear Models
Lecture 4: Flash Video
Class discussion of own data for HW4: Notes taken in class
Class discussion: Flash Video
|9/26||HW3 DUE BY 11:59 PM VIA BLACKBOARD: Download HW3|
|6||10/1||Lecture 5: Introduction to Multivariate and Repeated Measures Models
Lecture 5: Flash Video
Example 5: Piecewise Effects of Age Younger and Older Adults in Repeated Measures Data
Example 5: Data, Syntax, Output, and Excel
|Hoffman ch. 3
Enders (2011) ch. 3 p. 71-85
Enders (2011) ch. 4
|10/3||HW4 ON OWN DATA DUE BY 11:59 PM VIA BLACKBOARD: Download HW4|
|7||10/8||NO CLASS OR OFFICE HOURS|
|10/10||REVISIONS TO HW1, HW2, and HW3 BY 11:59 PM VIA BLACKBOARD|
|8||10/15||Example 5, continued
Example 5: Flash Video
|10/17||NO HOMEWORK DUE|
|9||10/22||Lecture 6: Introduction to Crossed Random Effects Models
Example 6: Crossed Random Effects Models for Trials nested within Subjects and Items
Example 6: Data, Syntax, Output, and Excel
Lecture 6 and Example 6: Flash Video
|Hoffman (2014) ch. 12|
|10/24||REVISIONS TO HW4 DUE BY 11:59 PM VIA BLACKBOARD|
|10||10/29||Lecture 7: Introduction to Clustered Data Models
Example 7: Two-Level Clustered Data Example: Students within Schools
Example 7: Syntax, Output, and Excel
Lecture 7 and Example 7: Flash Video
|Raudenbush & Bryk (2002) ch. 4-5|
|10/31||HW5 DUE BY 11:59 PM VIA BLACKBOARD: Download HW5|
|11||11/5||Lecture 8: Introduction to Path Analysis and Mediation
Lecture 8: Flash Video
Example 8: Path Analysis for Mediation
Example 8: Syntax and Output
Example 8: (no video, sorry)
|Enders (2011) ch. 5
Kline (2004) ch. 5-6
MacKinnon (2008) ch. 6
|11/7||HW6 DUE BY 11:59 PM VIA BLACKBOARD: Download HW6|
|12||11/12||Lecture 9: Introduction to Generalized Linear Models for Non-Normal (Binary) Data
Example 9: Predicting Binary Outcomes
Example 9: Data, Syntax, Output, and Excel
Lecture 9 and Example 9: Flash Video
|Cohen, Cohen, Aiken, & West (2002) ch. 13
Hoffman ch. 13 sec. 2
|11/14||HW7 ON OWN DATA DUE BY 11:59 PM VIA BLACKBOARD: Download HW7|
|13||11/19||Lecture 10: Generalized Linear Models for Proportions and Categorical Outcomes
Example 10a: Binomial (Repeated Measures) Models for Percent Correct
Example 10b: Ordinal and Nominal Models for Categorical Outcomes
Example 10a and 10b: Syntax, Output, and Excel
Lecture 10 and Examples 10a and 10b: Flash Video
|Hox (2010) ch. 6-7|
|11/21||HW8 DUE BY 11:59 PM VIA BLACKBOARD: Download HW8|
|14||11/26||NO CLASS OR OFFICE HOURS|
|12/1||REVISONS TO HW5 AND HW6 DUE MONDAY 12/1 BY 11:59 PM VIA BLACKBOARD|
|15||12/3||Lecture 11: Generalized Models for Count, Skewed, and "If and How Much" Outcomes
Example 11: Modeling Count Outcomes
Example 11: Syntax, Output, and Excel
Lecture 11 and Example 11: Flash Video
|Atkins & Gallop (2007)|
|12/5||HW9 AND REVISIONS TO HW7 DUE BY 11:59 PM VIA BLACKBOARD: Download HW9|
|16||12/10||Review and Requests for Special Topics
Example 12: Path Models with Generalized Outcomes
Example 12: Mplus Syntax and Output
Example 12: Flash Video
|12/12||LAST DAY TO TURN IN ANY FIRST DRAFTS OF HW 1-9
HW10 ON OWN DATA DUE BY 11:59 PM VIA BLACKBOARD: Download HW10
|12/19||LAST DAY TO TURN IN ANY REVISIONS TO HW1-7
REVISIONS TO HW8, HW9, AND HW10 DUE BY 11:59 PM VIA BLACKBOARD
The goal of this graduate seminar is give participants direct experience with applications of quantitative methods for data analysis. More specifically, this will include instruction on the process of matching data types and research questions to the statistical models than be used to answer them, followed by estimation, interpretation, and revision of those models as needed. Participants will be expected to enter the course with an individual data analytic need for a current research project. Instruction will then be tailored to meet these needs to the extent possible, and will include traditional lecture, in-class discussion, tutorials in statistical software, individual time for data analysis, and guidance in preparing results for dissemination (e.g., in manuscripts or professional presentations). Participants should have previous or concurrent coursework in general linear models (e.g., regression and analysis of variance) prior to enrolling in this seminar.
As a reminder, the University of Kansas has a formal policy on academic honesty. 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.
Participants will have the opportunity to earn up to 90 points by completing approximately 11 homework assignments. Completing the initial data outline is worth 3 bonus points. Finally, 10 points may be earned through class participation across the semester. This may include answering questions posed by the instructor as well as asking questions of your own. Submitting your log as proof that you learned how to install and use SAS university edition or how to use SAS or STATA from the KU Advanced Computing Facility is worth 2 bonus points.
Revision on 11/ 22: Given that only 10 homework assignments will be administered, the total points from homework will be 80, such that the total possible for the course will be 90 (+5 bonus points possible). Thus, a percentage grade out of 100 will be used to assign final grades.
In order to be able to provide the entire class with prompt feedback, late homework assignments will incur a 2-point penalty, and late revisions will incur a 1-point penalty. In addition, homework assignments must be at least 3/4 complete to be accepted as a first draft. However, extensions will be granted as needed for extenuating circumstances (e.g., conferences, family obligations) if requested at least three weeks in advance of the due date. Finally, a grade of “incomplete” will only be given in the event of extremely dire circumstances and at the instructor's discretion.
>=97 = A+, 93-96 = A, 90-92 = A-, 87-89 = B+, 83-86 = B, 80-82 = B-, < 80 = C or no pass (77-79 = C+, 73-76 = C, 70-72 = C-, 67-69 = D+, 63-66 = D, 60-62 = D-, < 60 = F)
Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21, 726-735.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). New York, NY: Routledge Academic.
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Hoffman, L. (2014, in press). Longitudinal analysis: Modeling within-person fluctuation and change. NY, NY: Routledge Academic.
Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed). NY, NY: Routledge Academic.
Kline, R. B. (2004). Principles and practice of structural equation modeling (2nd Ed.). New York, NY: Guilford.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Routledge Academic.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.