Fall 2014 SPLH 861: Applied Quantitative Methods

Schedule of Events (Printable Course Syllabus):

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).

Course Objectives, Materials, and Pre-Requisites:

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.

Academic Honesty:

As a reminder, the University of Kansas has a formal policy on academic honesty. All course assignments should be done individually.

Accommodating Students with Disabilities:

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

Course Requirements:

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.

Policy on Late Homework Assignments and Incompletes:

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.

Final grades will be determined according to the percentage of points earned out of 90 possible from homework and class participation:

>=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)

References for Course Readings (all available via "Course Documents" on Blackboard):

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 (3^rd 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.