# UNIVERSITY OF SOUTHERN CALIFORNIA SCHOOL OF SOCIAL WORK MULTIPLE REGRESSION FOR SOCIAL WORK RESEARCH SOWK 761L, SPRING 2012

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7 Tips for Maximizing Your Learning Experience in this Course Complete required readings and assignments BEFORE coming to class. BEFORE coming to class, review the materials from the previous session AND the current session, AND scan the topics to be covered in the next session. Come to class prepared to ask any questions you might have. Participate in class discussions. AFTER you leave class, review the materials assigned for that session again, along with your notes from that session. If you don't understand something, ask questions! Ask questions in class, during lab, office hours, and/or through . Keep up with the assigned readings. Don t procrastinate or postpone working on assignments. COURSE EXPECTATIONS Students will be expected to complete the assignments and use them as the basis for informed and active participation in class discussions throughout the semester. Assignments are designed to enable students to integrate and apply class content. All written work will be assessed in light of clarity of presentation, organization, and the ability to integrate and apply the various concepts presented through readings, lectures, labs, and class discussions.

8 Detailed Course Content and Assignments : Session 1 Class & Lab Cancelled : Session 2 Introduction to Course, Correlations Required Reading: Kachigan Chapter 10 p : Session 3 ANOVA Required Reading: Agresti & Finlay: p (Sections 12.1 & 12.2) Problem set 1 due : Session 4 Introduction to Simple Linear Regression; The Regression Equation; Objectives and Model Assumptions; Descriptive Methods in Regression and Correlation Required Reading: Agresti & Finlay, p (Sections ) Problem set 2 due : Session 5 Simple Linear Regression; Inferential Methods in Simple Linear Regression Required Reading: Agresti & Finlay, p (Sections ) Problem set 3 due : Session 6 Introduction to Multivariate Relationships; Conceptual Models Required Reading: Agresti & Finlay, p (Sections )

9 Problem set 4 due Assignment 1: Selecting a Data Set due : Session 7 Multiple Regression and Interpretation of Results; Required Reading: Tabachnick & Fidell pp , Problem set 5 due : Session 8 Evaluation of the Multiple Regression Model -- Model Assumptions 1 Required Reading: Tabachnick & Fidell pp , Problem set 6 due Assignment 2: Conceptual Model due : Session 9 Evaluation of the Multiple Regression Model -- Model Assumptions 2 Required Reading: Tabachnick & Fidell pp , Problem Set 7 due : SPRING RECESS : Session 10 Analytic Strategies for Multiple Regression, Relative Model Fitting Required Reading: Tabachnick & Fidell, p Assignment 3: Data Transformations and Model Assumptions due

10 : Session 11 Moderation models and interactions in multiple regression Required Reading: Agresti & Finlay, p , in class handout Problem set 8 due : Session 12 Mediation models with multivariate regression Required Reading: Agresti & Finlay, p in class handout Problem set 9 due : Session 13 Path Analysis Required Reading: in class handout Lab Assignment 5: Multivariate Model Results due : Session 14 Introductions to Advanced Linear Modeling Strategies Problem set 10 due : Session 15 Mini Conference on your work for the semester : Final Papers Due noon.

11 Week Topic Assignments 1/11 Class Cancelled 1/18 Introduction & Correlations 1/25 ANOVA Problem Set 1 2/1 Simple Linear Regression Problem Set 2 (descriptive) 2/8 Simple Linear Regression Problem Set 3 (inferential) 2/15 Multivariate Relationships, Conceptual Models Problem Set 4 Assignment 1: Data Set 2/22 Multiple Regression, Model Problem Set 5 Interpretation 2/29 Multivariate Regression Model Assumptions 1 Problem Set 6 Assignment 2: Conceptual Model 3/7 Multivariate Regression Problem Set 7 Model Assumptions 2 3/14 Spring Break 3/21 Analytic Strategies for Assignment 3: Data Cleaning and Model Assumptions Multivariate Regression 3/28 Moderation Models and Problem Set 8 Interaction Effects 4/4 Mediation Models Problem Set 9 4/11 Path Analysis Lab Assignment 5: Multivariate Results 4/18 Introductions to Advanced Problem Set 10 Linear Modeling Strategies 4/25 Mini-Conference on Your Work for the Semester In Class Presentations of Papers

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