BÄ°S768 - STRUCTURAL EQUATION MODELING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STRUCTURAL EQUATION MODELING | BÄ°S768 | 3rd Semester | 3 | 0 | 3 | 7 |
| Prequisites | Having succesfully completed the course of BÄ°S 654. | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion | |||||
| Instructor (s) | PROF. PINAR ÖZDEMİR, PhD. - PROF. ERDEM KARABULUT, PhD | |||||
| Course objective | To teach basic principles and methods used in structural equation modeling. | |||||
| Learning outcomes |
| |||||
| Course Content | Basic definitions of structural equation modeling, procedures of defining a model, path analysis and its applications, recursive and nonrecursive models, measurement models, factor analysis, structural equation modeling in single and multiple groups, assumptions of structural equation modeling, prediction, hypothesis tests and goodness of fit measures in structural equation modeling, and latent growth curve analysis. | |||||
| References | 1. Principles and practice of structural equation modeling / Rex B. Kline, New York: Guilford Press, 1998 2. Structural Equation Modeling: Foundations and extensions, David Kaplan, 2000 Sage Publications 3. Structural equation modeling : concepts, issues, and applications / Rick H. Hoyle, Thousand Oaks : Sage Publications, c1995 4. A beginner's guide to structural equation modeling / Randall E. Schumacker, Richard G. Lomax, 2004 5. Basics of structural equation modeling / Geoffrey M. Maruyama, Thousand Oaks, Calif. : Sage Publications, 1998 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Structural Equation Modeling: Basic Definitions and Terminology, regression and correlation. Introduction to software used in Structural Equation Modeling |
| Week 2 | Core Structural Equation Modeling Methods and Sofware: Model building procedures, path diagrams, graphs, model inadequacy, equivalent models and definitions of total, direct and indirect causal effects |
| Week 3 | Path Analysis: Correlation and causality, defining path models, types of path models, sample size, introduction to estimation methods, maximum likelihood estimation |
| Week 4 | Path Analysis: Detailed analysis in a recuresive model, assesing model fit, testing hierarchical models, comparison of nonhierarchical models, equivalent models, power analysis |
| Week 5 | Measurement models and Factor Analysis: Specification and identification of CFA models, estimation of CFA models, testing CFA models, equivalent CFA models, analyzing indicators with non-normal distributions |
| Week 6 | Midterm exam - Structural Equation Modeling Approach to regression, path and factor analysis, multivariate regression analysis, use of relevant sofware |
| Week 7 | Structural Equation Modeling in single and multiple samples - Characteristics of structural models, analysis of mixed models, estimation in structural models, identifying, testing and evaluating general structural equation models |
| Week 8 | Multi-sample structural models, causal inference in multi sample modeling. Statistical Assumptions of structural equation models, sampling assumptions, samples with non-normal distributions, missing data analysis, specification errors |
| Week 9 | Estimation in structural equation models: Estimation procedures, assumptions, fixed and constrained parameters, underestimated and overestimated models. |
| Week 10 | Assessing structural equation models:Goodness of fit criteria, model fit indexes, comparison of different models, selection of variables, hypothesis tests and power. |
| Week 11 | Midterm exam |
| Week 12 | Nonrecursive structural equation models: Building nonrecursive models, estimation in such models, comparision of recursive versus nonrecursive models |
| Week 13 | Multi-stage structural equation models:Multi-stage regression analysis, multi-stage path analysis, multi-stage CFA, "MIMIC" models alternative to multistage analysis |
| Week 14 | Latent Growth Curve Modeling: Introduction to and identification of mean structures, latent growth models, latent growth modeling with regard to multi-stage modeling and structural modeling, multivariate latent growth modeling |
| Week 15 | Preparation to final exam |
| Week 16 | FINAL EXAM |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 50 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 2 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 14 | 4 | 56 |
| Midterms (Study duration) | 2 | 7 | 14 |
| Final Exam (Study duration) | 1 | 14 | 14 |
| Total Workload | 45 | 34 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. A person who has a degree in Biostatistics, PhD: Has the knowledge to lead research planning, execution, and finalization, staying updated on literature and current studies. | X | ||||
| 2. Has sufficient information in the field, produces notable publications by addressing gaps in literature, both theoretically and practically. | X | ||||
| 3. Asks questions about presentations, seminars, and studies at conferences or seminars, with a critical perspective. | X | ||||
| 4. Has theoretical and practical knowledge of statistics at the level of expertise to determine the appropriate statistical analysis and examine the results in-depth. | X | ||||
| 5. Be proficient in computer use and statistical software, ensuring data suitability and recommending solutions for data management and analysis methods. | X | ||||
| 6. Effectively conducts analysis issues through active participation in discussions, exchanging information with the thesis advisor, and presenting seminars. | X | ||||
| 7. Provides method suggestions in consultancy, does research planning, prepares research reports. | X | ||||
| 8. Maintains scientific accuracy and ethical values, remaining careful against any conscious or unconscious biases throughout the study. | X | ||||
| 9. Be able to present oral presentation and poster in national or international conferences. | X | ||||
| 10. Be able to write a research project proposal independently, take part in a project, write a scientific study report. | X | ||||
| 11. Be able to attend multidisciplinary studies, collaborate professionally in group settings and gain the ability to assign individuals in the group. | X | ||||
| 12. Integrates diverse disciplines to analyze and synthesize information, offering solutions. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest