1.	FOUN 7930 Directed Study
	3 credit hours

2.	FS 2011
	Meeting Time:  TBA
	Room:		TBA
	Instructor:	Margaret E. Ross
			4018 Haley Center
			(334) 844-3084
			rossma1@.auburn.edu   (the first 1 = one)

3.  	Texts: 

Multilevel Analysis, Techniques and Applications by Joop Hox, 2002. Published by Lawrence Erlbaum Associates.
Hierarchical Linear Models: Applications and Data Analysis Methods, 2001, Stephen W. Raudenbush, Anthony S. Bryk, Sage Publications, Inc; 2nd edition. 

4.	Course Description

This course is designed to provide the student with an understanding of multilevel modeling (also known as hierarchical liner modeling) which is a statistical approach for modeling parameters at different levels. To facilitate understanding of the statistics, emphasis of the course is placed on the conceptual application and on the mathematical derivation of the estimation procedures. Additionally, students will become proficient with using the hierarchical linear modeling (HLM) statistical software package. specifically designed to run multilevel models on nested data. 

5.	Course Objectives

Students will:

•	Gain an understanding of HLM procedures.
•	Apply knowledge of HLM through analyzing research literature articles which utilize these methods. 
•	Apply knowledge of HLM procedures using the HLM statistical software. (Technology)
•	Apply knowledge of HLM procedures by interpreting results of statistical analyses.

6.  	Tentative Course Content and Schedule

Week 1 
•	Introduction to multilevel modeling of nested data and the HLM approach 
•	Review of ANOVA through regression procedures
•	Review of standard and sequential regression procedures

Readings:	Multilevel theories (Ch. 1, Hox)
		Advantages of HLM models (Ch. 1, Bryk & Raudenbush)
		Regression and ANOVA procedures (Ch.'s 12 and 13, Ross & Shannon book)
		
Week 2 
•	Discussion of readings
•	Introduction to HLM models
•	Interclass correlations
•	Introduction to the HLM statistical package

Readings:	HLM two-level models (Ch. 2, Hox; Ch. 3, Bryk & Raudenbush)

Week 3 
•	Discussion of readings
•	Analyzing the HLM One-way ANOVA with Random Effects Model (computer application)
•	Interpretation of the HLM One-way ANOVA with Random Effects Model output

Readings: An illustration of the HLM One One-way ANOVA with Random Effects Model 
		(Ch. 4,	Bryk & Raudenbush)

Assignment 1: Critical analysis of refereed journal article in which the HLM One-way ANOVA with Random Effects Model is used.
Assignment 2: Submit a results section of HLM One-way ANOVA with Random Effects Model based on output from class.

Week 4 
•	Discussion of readings
•	Analyzing the Regression with Means-as-Outcomes HLM model (computer application)
•	Interpretation of the Regression with Means-as-Outcomes HLM model output

Readings:	An illustration of the Regression with Means-as-Outcomes HLM model  
		(Ch. 4,	Bryk & Raudenbush)

Assignment 3. Critical analysis of refereed journal article in which the Regression with Means-as-Outcomes HLM model is used.
Assignment 4. Submit a results section of Regression with Means-as-Outcomes HLM model based on output from class
Week 5 
•	Discussion of readings
•	Analyzing the Random Coefficients Model
•	Interpretation of the Random Coefficients Model output

Readings:	An illustration of the Random Coefficients HLM model  
		(Ch. 4 Bryk & Raudenbush)

Assignment 5: Critical analysis of refereed journal article in which the Random Coefficients HLM model is used. 
Assignment 6:  Results section of The Random Coefficients HLM model based on class output.

Week 6
•	Discussion of readings
•	Analyzing the Intercept-and-Slopes-as-Outcomes HLM model (computer application)
•	Interpretation of the Intercept-and-Slopes-as-Outcomes HLM model output

Readings:	An illustration of the Intercept-and-Slopes-as-Outcomes HLM model 
		(Ch. 4,	Bryk & Raudenbush)

Assignment 7: Critical analysis of refereed journal article in which the Intercept-and-Slopes-as-Outcomes HLM model is used.
Assignment 8: Results section of Intercept-and-Slopes-as-Outcomes HLM model based on output from class

Week 7 
•	Discussion of readings
•	Review of Repeated Measures and Analyzing the Repeated Measures Models
•	Interpretation of Repeated Measures  output

Readings: Repeated Measures (Repeated Measures Chapters in Ross & Shannon)

Assignment 9: Critical analysis of refereed journal article in which repeated measures is used.
Assignment 10: Results section of repeated measures designs based on output from class.

Week 8 
•	Discussion of readings
•	Analyzing the Repeated Measures Models through HLM
•	Interpretation of Repeated Measures HLM  output

Readings:	The Repeated Measures HLM model (Ch. 6, Bryk & Raudenbush; Ch. 5, Hox)

Assignment 11: Critical analysis of refereed journal article in which a repeated measures HLM model is used.
Assignment 12: Results section of repeated measures HLM design based on output from class.

Week 9 
•	Discussion of readings
•	Meta-analysis procedures

Readings: Chapters 2 and 6 in Meta-Analysis in Social Research (Gene V. Glass, Barry McGaw, & Mary Lee Smith)

Assignment 13: Critical analysis of a refereed journal meta-analysis article.

Week 10 
•	Discussion of readings
•	Meta-analysis through HLM (computer application)
•	Interpretation of  the HLM meta-analysis  output

Readings: The Meta-analysis HLM model (Ch. 7, Bryk & Raudenbush)

Assignment 14: Critical analysis of refereed journal article in which a HLM meta-analyses is used.

Week 11 
•	Discussion of readings
•	Methodological and Statistical Issues, sample size, and power analysis

Readings: Chapters 4 and 10 in Hox

Week 12 
•	Discussion of Readings
•	Critical analysis of refereed journal article (of choice) in which HLM is used considering methodological and statistical issues, sample size, and power.

Readings: HLM two-level models (Ch. 2, Hox; Ch. 3 Bryk & Raudenbush)

Week 13 and 14

•	Application with real data
•	Entering data
•	Deciding on the analysis
•	Interpreting output
•	Results section based on output

7.	Course Requirements/Evaluation

Learning Methods
Discussions, readings, exercises and lab assignments

Student Assessment
•	Informal assessment of learning from discussion of readings during meetings (not part of grade)
•	Portfolio with following components
o	Notes on Assigned Readings
o	Results Sections
o	Journal Articles
o	Critical Analysis of Statistical Methods Used in Research Literature
	
Grading Scale
A:	Completion of all assigned work (in portfolio) with accuracy in 90% of assignments and attendance of at least 12 of 14 weekly meetings
B:	Completion of all assigned work (in portfolio) with accuracy in 80% of assignments and attendance of 11 of 14 weekly meetings
C:	Completion of all assigned work (in portfolio) with accuracy in 70% of assignments and/or 3 to 4 absences
D:	Completion of all assigned work (in portfolio) with accuracy in less than 70% of assignments and/or more than 4 absences
F: 	Incomplete portfolio and/or more than 4 absences