Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ADVANCE STATISTICS IN BEHAVIORAL SCIENCES 600306802141 0 + 0 3.0 10.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors Celal Deha DOĞAN
Assistants
Goals It's aimed to reconsider the basic statistical proficiency and to introduce the common multivariate statistical techniques and their application on education.
Course Content This class contains the conceptional framework of common multivariate and advanced statistical techniques and their usage on scientific research processes.
Learning Outcomes 1) Understanding the nature of the multivariate statistics
2) Testing the basic assumptions of the multivariate statistical techniques
3) Applying the multivariate statistical techniques on a sample data set
4) Defining the appropriate statistical technique on a given problem situation
5) Reporting the results of the statistical outputs according to the reporting standarts
6) Interpreting the outputs of multivariate statistics
7) Evaluating the multivariate techniques and their results on published researches

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction and Planning Lecture; Question Answer; Problem Solving; Discussion

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
2. Week Conceptual framework of multivariate statistics and their usage on educational studies Lecture; Question Answer; Discussion

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
3. Week The Steps of statistical hypothesis tests and applications Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
4. Week Statistical Assumptionas and Testing of Them Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
5. Week Missing Data Analysis Lecture; Question Answer; Problem Solving; Discussion; Case Study; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
6. Week ANCOVA, MANCOVA, MANOVA Lecture; Question Answer; Problem Solving; Discussion; Case Study; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
7. Week Discriminant Analysis Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
8. Week Logistic Regression Lecture; Question Answer; Problem Solving; Discussion; Case Study; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
9. Week Multiple and Multivariate Regression Lecture; Question Answer; Problem Solving; Discussion; Case Study; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
10. Week Canonical Correlation Lecture; Question Answer; Problem Solving; Discussion; Case Study; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
11. Week Exploratory and Confirmatory Factor Analysis Lecture; Question Answer; Problem Solving; Discussion; Case Study; Role Play; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
12. Week Cluster Analysis Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
13. Week Structural Equation Modeling Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
14. Week Measurement Invariance Lecture; Question Answer; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
15. Week General view of the techniques for big data sets (CHAID analysis, Decision Trees, Data Mining) Lecture; Question Answer; Problem Solving; Discussion; Demonstration

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)

Sources Used in This Course
Recommended Sources
Anastasi, A. (1988). Psychological testing (6th ed.). New York: Macmillan.
Crocker, L. ve Algina, J. (1986). Introduction to Classical and Modern Test Theory. Belmont CA: Wadsworth Thomson Learning Company.
Guilford, J. P. ve Fruchter, B. (1978). Fundamental statistics in psychology and education (6th ed.). New York: McGraw-Hill.
Jöreskog, K.G. ve Sörbom, D. (1993). Lisrel 8: Structural Equating Modeling with the Simplis Command Language. Lincolnwood: Scientific Software International, Inc.
Little, R.J.A ve Rubin, D.B. (1987). Statistical analysis with missing data, 2nd ed. New York: John Wiley & Sons, Inc.
Tabachnick, B. G. ve Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.).Boston: Pearson, Allyn & Bacon.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 15 3
Work Hour outside Classroom (Preparation, strengthening) 15 4
Homework 2 5
Presentation (Including Preparation Time) 15 3
Report (Including Preparation and presentation Time) 2 10
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 15 1
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 15 2
Quiz 4 2
Time to prepare for Quiz 4 5
Final Exam 1 2
Time to prepare for Final Exam 1 10
1 2
1 2
2 10
2 5
1 2
1 2
1 2
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload