# Graduate Studies Reports Access

**Graduate Course Proposal Form Submission Detail - EDF7486**

Tracking Number - **1880**

Edit function not enabled for this course.

**Current Status:**
Approved, Permanent Archive - 2005-05-04

**Campus:**

**Submission Type:**

**Course Change Information (for course changes only):**

**Comments:**

### Detail Information

- Date & Time Submitted:
**2005-01-26**

- Department: EDQ

- College: ED

- Budget Account Number: 171100000

- Contact Person: John Ferron

- Phone: 9745361

- Email: ferron@tempest.coedu.usf.edu

- Prefix: EDF

- Number: 7486

- Full Title: Application of Structural Equation Modeling in Education

- Credit Hours: 3

- Section Type: C -
Class Lecture (Primarily)

- Is the course title variable?: N

- Is a permit required for registration?: N

- Are the credit hours variable?: N

- Is this course repeatable?:

- If repeatable, how many times?: 0

- Abbreviated Title (30 characters maximum): Structural Equations in Ed

- Course Online?: -

- Percentage Online:

- Grading Option:
R - Regular

- Prerequisites: EDF 7408 or equivalent

- Corequisites: none

- Course Description: Application of structural equation modeling in educational research, including path models, confirmatory factor analysis, structural modeling with latent variables, and latent growth curve models.

- Please briefly explain why it is necessary and/or desirable to add this course: A recent study of the research reported in PsycINFO has indicated that structural equation modeling (SEM) has become the “preeminent method of data analysis” in the social sciences (Hershberger, 2003, p. 41). Research articles using structural equation m

- 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 an elective for students in Educational Measurement and Evaluation. Given the widespread application of multilevel modeling in education and related fields, it is anticipated that the majority of students in the program will choose this elective. In offerings as a Selected Topics Course, it has drawn students from educations, psychology, public health, nursing, business, etc. It is anticipated that this will continue.

- Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 5 times.

- What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) Doctoral Degree meeting departmental requirement of at least 50% of doctoral coursework in the areas of Educational Statistics, Measurement and Evaluation.
Documented training in SEM.

Documented experience using SEM in educational research.

- Objectives: The primary purpose of this course is to help students gain an understanding of the logic, concepts, methods, applications, and limitations of structural equation modeling. We will address basic educational applications of structural equation modeling include path models, confirmatory factor analysis, and structural models with latent variables. We will also consider more advanced educational applications, such as latent growth curve models, multi-group models, higher order factor analysis, multitrait-multimethod models, and models with interactions among latent variables. The course emphasis is on the application of these procedures in the context of research in education. Computer applications of the procedures will be integrated into the course.

- Learning Outcomes: The successful completion of the course requirements is expected to result in increased ability to (a) intelligently read and evaluate research literature, (b) recognize the strengths and limitations of statistical analysis in the conduct of disciplined inquiry, (c) design research studies requiring the use of structural equation modeling and (d) communicate with peers and other professionals on research issues. More specifically, students will be able to...
1. Recognize research scenarios that are amenable to SEM

2. Translate research questions into path models

3. Specify a mathematical model based on a path diagram

a) in matrix form (LISREL notation)

b) in equation form (EQS, CALIS notation)

4. Screen data for the purpose of assessing the tenability of assumptions

5. Make decisions regarding estimation based on the form of the data

6. Prepare data for the structural equation modeling analysis

7. Write the computer code necessary to run a structural equation modeling analysis

8. Recognize and make decisions regarding identification problems

9. Recognize and make decisions regarding improper solutions

10. Evaluate the overall fit of the model

11. Evaluate fit in the components of the model

12. If appropriate, compare the fit of competing models

13. If appropriate, modify the model to improve fit

14. Summarize and communicate the results of the analysis

Grades will be based on 4 projects. A brief description of the projects and the rubrics used for grading are provided below.

Project 1 (20%)

Students will complete path analyses based on data provided by the instructor. The student will specify the model(s), estimate the model(s) using appropriate software, evaluate fit, prepare path diagrams containing the unstandardized and standardized solutions, will estimate specified total, direct, and indirect effects, and will interpret and discuss the findings.

____ Correct specification of model(s)

____ Accurate program syntax

____ Acceptable evaluation of fit

____ Accurate path diagram – unstandardized

____ Accurate path diagram – standardized

____ Accurate estimate of total, direct, and indirect effects

Project 2 (20%)

Students will complete confirmatory factor analyses based on data provided by the instructor. The student will specify the model(s), estimate the model(s) using appropriate software, evaluate fit, prepare path diagrams containing the unstandardized and standardized solutions, and interpret and discuss the findings.

____ Correct specification of model(s)

____ Accurate program syntax

____ Acceptable evaluation of fit

____ Accurate path diagram – unstandardized

____ Accurate path diagram – standardized

Project 3 (20%)

Students will complete analyses of a structural equation model with latent variables based on data provided by the instructor. The student will specify the model(s), estimate the model(s) using appropriate software, evaluate fit, prepare path diagrams containing the unstandardized and standardized solutions, and interpret and discuss the findings.

____ Correct specification of model(s)

____ Accurate program syntax

____ Acceptable evaluation of fit

____ Accurate path diagram – unstandardized

____ Accurate path diagram – standardized

Project 4 (40%)

Students will complete analyses of a structural equation model based on data and models of their own choosing. The student will identify and/or gather the data, develop the model(s), specify the model(s), estimate the model(s) using appropriate software, evaluate fit, prepare path diagrams, and write a report on the study in APA format, where the results section should be of a quality suitable for publication.

____ Rationale for questions/model included

____ Appropriate level of detail regarding sample

____ Appropriate level of detail regarding variables

____ Analyses clearly described, including any modifications

____ Model(s) specified consistently with theory/rationale

____ Program syntax is accurate

____ Data screening conducted and reported

____ Appropriate choice of fit indices

____ Accurate interpretation of fit indices

____ Parameter estimates included

____ Accurate description/discussion of parameter estimates

____ Limitations noted

____ Conclusions consistent with results

____ Publishable writing of results section

- Major Topics: Orientation to Course, Introduction to SEM, Path Models
Model Specification, Equation notation

Model Specification, LISREL notation

Implied Covariance Matrices, Identification, and Equivalent Models

Model Estimation and Power

Model Fit and Interpretation Issues Non-Normality and Interpretation Issues

Latent Growth Models

- Textbooks: Orientation to Course, Introduction to SEM, Path Models
Hershberger, S. L. (2003). The growth of structural equation modeling: 1994-2001. Structural Equation Modeling, 10, 35-46

McDonald, R. P. & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses, Psychological Methods, 7, 64-82.

Boomsma, Anne. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461-483.

Model Specification, Equation notation

Wolfle, L. M. (2003). The introduction of path analysis to the social sciences, and some emergent t

- Course Readings, Online Resources, and Other Purchases:

- Student Expectations/Requirements and Grading Policy:

- Assignments, Exams and Tests:

- Attendance Policy:

- Policy on Make-up Work:

- Program This Course Supports:

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