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Graduate Course Proposal Form Submission Detail - SOW7410
Tracking Number - 1842

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Current Status: Approved, Permanent Archive - 2006-05-05
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Detail Information

  1. Date & Time Submitted: 2005-08-10
  2. Department: Social Work
  3. College: AS
  4. Budget Account Number: 126100
  5. Contact Person: William S. Rowe/Gregory J. Paveza
  6. Phone: 8139742706
  7. Email: wrowe@cas.usf.edu
  8. Prefix: SOW
  9. Number: 7410
  10. Full Title: Advanced Statistics in Social Work Research
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?:
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): Advanced Social Work Statistic
  19. Course Online?: -
  20. Percentage Online:
  21. Grading Option: R - Regular
  22. Prerequisites: Must be admitted to the graduate Ph.D. social work program. This course is restricted to majors only. SOW 6405 or equivalent.
  23. Corequisites: MSW
  24. Course Description: This course provides students a detailed and practical understanding of Adv. Statistical techniques that are of use to Social Work Academicians, Administrators, and Researchers as they conduct critical research into policy, practice, and social issues.

  25. Please briefly explain why it is necessary and/or desirable to add this course: 3rd in a series of research courses for the Ph.D. program in social work.
  26. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? This course will be part of the required program courses. All graduate students in the Ph.D. program will take it. It will not be offered to others outside of the School of Social Work.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? yes, once.
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) To teach this course, the instructor must have at a minimum a Ph.D.
  29. Objectives: After the completion of this course the student will:

    Have an understanding of the advanced quantitative measures used in research studies.

    Have an understanding of issues of the use of polynomials and dummy variables in multivariate techniques and how to construct such variables.

    Skill objectives:

    Will demonstrate the ability to use SPSS for the computation of advance analytic techniques.

    Ability to report results based on multivariate techniques.

    Ability to write a data analysis section for a grant application using multivariate techniques

    Value objectives:

    Understanding of issues related to the appropriate interpretation and presentation of multivariable models.

  30. Learning Outcomes: This course builds on Quantitative Methods and develops more completely the subject matter covered in that class. During this intensive course students will be provided with the needed knowledge and practical skills to determine the appropriate multivariate technique to use in their research. Areas of focus will include underlying principles of Multivariable models, an introduction to these models and issues related to modeling including concepts such as dummy variables, and polynomials as a means of controlling variability. Areas of focus will include issues related to variable selection, ANOVA, Multivariable regression and its relation to linear regression, Multiple Analysis of Variance, Discriminant Function Analysis, Factor Analysis, Logistic Analysis, Survival Analysis, Hierarchical Analysis and Structural equations. Lastly the course will touch briefly and non-linear models.
  31. Major Topics: Introduction and Classification of Variables, Review of Basic Statistics, Introduction to Regression Analysis, Straight Line Regression, The Correlation Coefficient and Straight Line Regression, The Analysis of Variance Table, Multiple Regression Analysis General Considerations, Testing Hypotheses with Multiple, Regression,

    Correlations, Multiple, Partial and Multiple-Partial, Confounding and Interaction in Regression, Regression Diagnostics, Polynomial Regression, Dummy Variables in Regression, Analysis of Covariance and other methods, for Adjusting Continuous Data, Selecting the Best Regression Equation, One-Way ANOVA, A Brief Discussion of Non-Linear Multivariate Techniques or When to Call your friendly Statistician, Two-Way ANOVA with Equal Cells, Two-Way ANOVA with unequal cells, Analysis of Repeated Measures, The Method Of Maximum Likelihood, Cox and Logistic Regression, Poisson Regression, Survival Analysis, Discriminant Function Analysis, Factor Analysis, Hierarchical Analysis, Structural Equations, Randomized Blocks A Special Case of Two-Way ANOVA.

  32. Textbooks: REQUIRED TEXTBOOK(S)

    Kleinbaum, David G, Lawrence L. Kupper, Keith E. Muller, Azhar Nizam. Applied Regression Analysis and other Multivariable Methods, 3rd Edition. Duxbury Press, Pacific Grove, CA: 1998.

    SUPPLEMENTAL SOURCES

    Babbie, Earl, Fred Halley, Jeanne Zanio. Adventures in Social Research: Data Analysis Using SPSS for Windows with Student Version of SPSS, 5th Edition. Pine Forge Press, Thousand Oaks, CA: 2003.

    Kleinbaum, David G. Logistic Regression: A Self-Learning Text. Springer-Verlag, New York: NY, 1994.

    Kleinbaum, David G. Survival Analysis: A Self-Learning Text. Sp

  33. Course Readings, Online Resources, and Other Purchases:
  34. Student Expectations/Requirements and Grading Policy:
  35. Assignments, Exams and Tests:
  36. Attendance Policy:
  37. Policy on Make-up Work:
  38. Program This Course Supports:
  39. Course Concurrence Information:


- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.