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Graduate Course Proposal Form Submission Detail - NGR7843
Tracking Number - 1750

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Current Status: Approved, Permanent Archive - 2006-12-05
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Detail Information

  1. Date & Time Submitted: 2006-06-13
  2. Department: Nursing
  3. College: NR
  4. Budget Account Number: 6201-000-0
  5. Contact Person: Jason Beckstead
  6. Phone: 9747667
  7. Email: jbeckste@health.usf.edu
  8. Prefix: NGR
  9. Number: 7843
  10. Full Title: Statistical Methods in Nursing Research III
  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): Stats Methd in Nur Resh III
  19. Course Online?: -
  20. Percentage Online:
  21. Grading Option: R - Regular
  22. Prerequisites: NGR 7842
  23. Corequisites:
  24. Course Description: Focus on advanced multivariate statistical methods in nursing research; emphasizing multiple regression and correlational analysis.

  25. Please briefly explain why it is necessary and/or desirable to add this course: Students in the PhD program in Nursing require additional coursework in statistical methods, particularly in multivariate methods.
  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 is required for all doctoral students in Nursing and is the final course in a series of three.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? This course has never been offered in the College.
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) Doctoral degree with a strong background in research methods and statistical analysis is required
  29. Objectives: Objectives:

    1) Provide the researcher, as distinct from the statistician, with a survey of several of the more

    commonly used multivariate procedures, including multiple regression, path analysis, and factor

    analysis;

    2) Introduce a conceptual framework of measurement and structural modeling with which to apply

    and interpret multivariate statistics in the research process;

    3) Illustrate how to conduct multivariate analyses using SPSS and LISREL programs.

  30. Learning Outcomes: Objectives:

    1) Provide the researcher, as distinct from the statistician, with a survey of several of the more

    commonly used multivariate procedures, including multiple regression, path analysis, and factor

    analysis;

    2) Introduce a conceptual framework of measurement and structural modeling with which to apply

    and interpret multivariate statistics in the research process;

    3) Illustrate how to conduct multivariate analyses using SPSS and LISREL programs.

  31. Major Topics: The Multivariate Perspective & Introduction

    Simple Linear Regression

    Multiple Regression w/ 2 Independent Variables

    Multiple Regression: General Method w/ Matrix Algebra

    Multiple Regression using Categorical Predictors

    Trend Analysis: Linear & Curvilinear Regression

    Introduction to Factor Analytic Methods

    Exploratory Factor Analysis

    Technical Aspects of Factor Analysis

    Introduction to Path Analysis

    Path Analysis using SPSS

    Introduction to Structural Equation Modeling

    Path Analysis using LISREL

    Confirmatory Factor Analysis using LISREL

  32. Textbooks: Tabachnick, B.G. & Fidell, L.S. (2001). Using multivariate statistics. 4th ed. Allyn & Bacon:

    Needham Heights, MA. ---or latest edition---

  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.