STA 6127

Statistical Methods for Social Research 2

Spring 2009

Section 7317 – Anderson 134 – MWF 1

Section 7314 – Anderson 134 – MWF 2

 

 

Instructor: Dr. Larry Winner

 

Office: 228 Griffin/Floyd,    392-1941x230

 

Office Hours: TBA (See class website)

 

e-mail:  winner@stat.ufl.edu  (use sparingly)

 

Text: Statistical Methods for the Social Sciences, 4th Ed,  Agresti and Finlay

 

Course Description: Statistical methods commonly applied in social science research, with a single response (dependent) variable and one or more explanatory/control (independent) variable(s). Cases covered include when the response and prediction variables are of interval, nominal, and ordinal scales. Models, interpretations, and computing (SPSS) will be stressed.

 

 

Course Topics

  • Multiple Linear Regression
    • Model
    • Regression Coefficient Estimates, Standard Errors, t-tests
    • Analysis of Variance, F-test
    • Computer Output
    • Multiple Correlation, and R2
    • Modeling Interactions
    • Comparing Models
    • Partial Correlation
    • Standardized Regression Coefficients
  • Comparing Groups: Analysis of Variance Methods
    • Comparing More than 2 Group Means, F-test
    • Multiple Comparisons
    • ANOVA using Regression Approach
    • 2-Factor ANOVA
    • Randomized Block Design
    • Repeated Measures ANOVA
    • Assumptions and Violations
  • Analysis of Covariance/Predictors of Different Scales
    • Comparing Means and Regression Lines across Groups
    • Regression with Quantitative and Categorical Predictors
    • Interactions Between Quantitative and Categorical Predictors
    • Inference for Models with Quantitative and Categorical Predictors
    • Adjusted Means
  • Model Building for Multiple Regression
    • Automated Selection Methods
    • Diagnostics
    • Multicollinearity
    • Generalized Linear Models
    • Polynomial Regression
    • Exponential Growth Models
  • Logistic Regression for Categorical Responses
    • Logistic Regression for Binary Responses
    • Multiple Logistic Regression
    • Inference for Logistic Regression Models
    • Ordinal Response Models
  • Introduction to Advanced Methods (Time permitting)
    • Longitudinal Data Analysis
    • Hierarchical Models
    • Factor Analysis
    • Structural Equation Models

 

Exam Dates:

  • Exam 1: February 6
  • Exam 2: March 4
  • Exam 3: April 22

 

Notes:

  • Homework problems will be assigned from textbook, but not graded.
  • Homework projects will be posted on class website and will be taken up and graded. Projects must be handed in (hard copy), e-mail will not be accepted.
  • Exams are 1-hour. You may bring a copy of the t,c2,F table with hand written notes (8.5x11”)
  • Homework will count 25% of your course grade, highest exam will be 30%, lowest exam 20%, median exam 25%
  • Grades are not negotiable.