Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| ADVANCED STATISTICS | PSY4215541 | Spring Semester | 3+0 | 3 | 6 |
| Course Program | Çarşamba 14:30-15:15 Çarşamba 15:30-16:15 Çarşamba 16:30-17:15 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Büşra AKTAŞ |
| Name of Lecturer(s) | Assist.Prof. Büşra AKTAŞ, Lect. Metin Ege SALTER |
| Assistant(s) | |
| Aim | The primary goal of the course is to instill a basic understanding of statistical modeling in students before introducing them to some advanced level multivariate statistical methods. In this context, the first half of the term will lay the groundwork with conventional statistical methods to build statistical understanding, refresh, and enhance previously learned knowledge (analyses such as ANOVA, multiple regression, etc., through SPSS and Jamovi). This will also set the stage for the advanced statistical topics to be covered in the second half of the term. During the second half, multivariate statistical modeling (such as path, exploratory, and confirmatory factor analyses) will be examined through basic level Structural Equation Modeling (SEM) using the MPlus program. |
| Course Content | This course contains; Data Screening,ANOVA,ANCOVA,MANOVA + Assignment #1,MANCOVA,Linear Regression,Multiple Regression I,Multiple Regression II,Introduction to Structural Equation Modelling & Mplus I,Introduction to Structural Equation Modelling & Mplus II,Path Analysis I,Path Analysis II,Exploratory & Confirmatory Factor Analysis I,Exploratory & Confirmatory Factor Analysis II. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Competence in theory/model-based measurement, testing, and analysis methods.
2. Understanding why structural equation modeling and related statistical methods are useful in social sciences.
3. Proficiency in SPSS, Jamovi, and MPlus programs.
4. Awareness and ability to identify the limitations of structural equation modeling applications in articles and other publications. | 1, 2, 4, 6 | C |
| Teaching Methods: | 1: Mastery Learning, 2: Project Based Learning Model, 4: Inquiry-Based Learning, 6: Experiential Learning |
| Assessment Methods: | C: Multiple-Choice Exam |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Data Screening | |
| 2 | ANOVA | |
| 3 | ANCOVA | |
| 4 | MANOVA + Assignment #1 | |
| 5 | MANCOVA | |
| 6 | Linear Regression | |
| 7 | Multiple Regression I | |
| 8 | Multiple Regression II | |
| 9 | Introduction to Structural Equation Modelling & Mplus I | |
| 10 | Introduction to Structural Equation Modelling & Mplus II | |
| 11 | Path Analysis I | |
| 12 | Path Analysis II | |
| 13 | Exploratory & Confirmatory Factor Analysis I | |
| 14 | Exploratory & Confirmatory Factor Analysis II | |
| Resources |
| Tabachnick, B. G., & Fidell, L. S. (2011). Multivariate statistics. Boston: Allyn and Bacon.
Hoyle, R.H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. London: Sage.
Kaplan, D. (2000). Structural Equation Modeling: Foundations and Extensions. Advanced Quantitative Techniques in the Social Sciences Series.
Klein, R.B. (2004). Principles and Practice of Structural Equation Modeling. Psychology Press.
Muthén, L.K. and Muthén, B.O. (1998-2010). Mplus User’s Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén.
Byrne, B. (2011). Structural Equation. Modeling with Mplus Basic Concepts, Applications, and Programming. Taylor & Francis.
|
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Knows the basic concepts of research and application-oriented sub-fields of psychology and the basic theories of these fields. | | | | | X |
| 2 | Can compare theories and schools in the history of psychology, and relate new developments with this knowledge. | X | | | | |
| 3 | Can recognize and interpret the problems they encounter and offer solutions using their expert knowledge. | | | | | X |
| 4 | Can investigate a problem with scientific methods, interpret findings and turn the results into a scientific publication. | | | | | X |
| 5 | Can lead the project, plan and manage the activities in a team established to solve the problems related to their field. | | | X | | |
| 6 | Can question and criticize new ideas from a scientific point of view without taking sides. | | X | | | |
| 7 | They adopt the principle of lifelong learning and can follow new developments in their field. | | | X | | |
| 8 | Can share their findings, knowledge and solution suggestions about a problem with colleagues or people outside of their field in written or oral form, in an appropriate language. | | X | | | |
| 9 | They have a sense of social responsibility and can use their professional achievements in solving problems in their near and far surroundings. | X | | | | |
| 10 | Speaks English at least at B1 level to follow international professional developments. | | | | | |
| 11 | Has basic computer skills and can communicate with colleagues on up-to-date platforms. | | | | | X |
| 12 | Knows the basic tools of psychology used in assessment and evaluation and can use these tools. | | | | | |
| 13 | Knows professional responsibilities, authorization, and limits, recognizes psychological problems, can make the right referral for their solution, and abides by ethical principles in research and practice. | | | | | |
| 14 | They consider individual and cultural differences in research and practice and take these differences into account while evaluating the research results. | | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 40 |
| Rate of Final Exam to Success | | 60 |
| Total | | 100 |
| ECTS / Workload Table |
| Activities | Number of | Duration(Hour) | Total Workload(Hour) |
| Course Hours | 14 | 3 | 42 |
| Guided Problem Solving | 14 | 3 | 42 |
| Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 |
| Term Project | 1 | 20 | 20 |
| Presentation of Project / Seminar | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Midterm Exam | 1 | 25 | 25 |
| General Exam | 1 | 30 | 30 |
| Performance Task, Maintenance Plan | 0 | 0 | 0 |
| Total Workload(Hour) | 159 |
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(159/30) | 5 |
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| ADVANCED STATISTICS | PSY4215541 | Spring Semester | 3+0 | 3 | 6 |
| Course Program | Çarşamba 14:30-15:15 Çarşamba 15:30-16:15 Çarşamba 16:30-17:15 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Büşra AKTAŞ |
| Name of Lecturer(s) | Assist.Prof. Büşra AKTAŞ, Lect. Metin Ege SALTER |
| Assistant(s) | |
| Aim | The primary goal of the course is to instill a basic understanding of statistical modeling in students before introducing them to some advanced level multivariate statistical methods. In this context, the first half of the term will lay the groundwork with conventional statistical methods to build statistical understanding, refresh, and enhance previously learned knowledge (analyses such as ANOVA, multiple regression, etc., through SPSS and Jamovi). This will also set the stage for the advanced statistical topics to be covered in the second half of the term. During the second half, multivariate statistical modeling (such as path, exploratory, and confirmatory factor analyses) will be examined through basic level Structural Equation Modeling (SEM) using the MPlus program. |
| Course Content | This course contains; Data Screening,ANOVA,ANCOVA,MANOVA + Assignment #1,MANCOVA,Linear Regression,Multiple Regression I,Multiple Regression II,Introduction to Structural Equation Modelling & Mplus I,Introduction to Structural Equation Modelling & Mplus II,Path Analysis I,Path Analysis II,Exploratory & Confirmatory Factor Analysis I,Exploratory & Confirmatory Factor Analysis II. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Competence in theory/model-based measurement, testing, and analysis methods.
2. Understanding why structural equation modeling and related statistical methods are useful in social sciences.
3. Proficiency in SPSS, Jamovi, and MPlus programs.
4. Awareness and ability to identify the limitations of structural equation modeling applications in articles and other publications. | 1, 2, 4, 6 | C |
| Teaching Methods: | 1: Mastery Learning, 2: Project Based Learning Model, 4: Inquiry-Based Learning, 6: Experiential Learning |
| Assessment Methods: | C: Multiple-Choice Exam |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Data Screening | |
| 2 | ANOVA | |
| 3 | ANCOVA | |
| 4 | MANOVA + Assignment #1 | |
| 5 | MANCOVA | |
| 6 | Linear Regression | |
| 7 | Multiple Regression I | |
| 8 | Multiple Regression II | |
| 9 | Introduction to Structural Equation Modelling & Mplus I | |
| 10 | Introduction to Structural Equation Modelling & Mplus II | |
| 11 | Path Analysis I | |
| 12 | Path Analysis II | |
| 13 | Exploratory & Confirmatory Factor Analysis I | |
| 14 | Exploratory & Confirmatory Factor Analysis II | |
| Resources |
| Tabachnick, B. G., & Fidell, L. S. (2011). Multivariate statistics. Boston: Allyn and Bacon.
Hoyle, R.H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. London: Sage.
Kaplan, D. (2000). Structural Equation Modeling: Foundations and Extensions. Advanced Quantitative Techniques in the Social Sciences Series.
Klein, R.B. (2004). Principles and Practice of Structural Equation Modeling. Psychology Press.
Muthén, L.K. and Muthén, B.O. (1998-2010). Mplus User’s Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén.
Byrne, B. (2011). Structural Equation. Modeling with Mplus Basic Concepts, Applications, and Programming. Taylor & Francis.
|
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Knows the basic concepts of research and application-oriented sub-fields of psychology and the basic theories of these fields. | | | | | X |
| 2 | Can compare theories and schools in the history of psychology, and relate new developments with this knowledge. | X | | | | |
| 3 | Can recognize and interpret the problems they encounter and offer solutions using their expert knowledge. | | | | | X |
| 4 | Can investigate a problem with scientific methods, interpret findings and turn the results into a scientific publication. | | | | | X |
| 5 | Can lead the project, plan and manage the activities in a team established to solve the problems related to their field. | | | X | | |
| 6 | Can question and criticize new ideas from a scientific point of view without taking sides. | | X | | | |
| 7 | They adopt the principle of lifelong learning and can follow new developments in their field. | | | X | | |
| 8 | Can share their findings, knowledge and solution suggestions about a problem with colleagues or people outside of their field in written or oral form, in an appropriate language. | | X | | | |
| 9 | They have a sense of social responsibility and can use their professional achievements in solving problems in their near and far surroundings. | X | | | | |
| 10 | Speaks English at least at B1 level to follow international professional developments. | | | | | |
| 11 | Has basic computer skills and can communicate with colleagues on up-to-date platforms. | | | | | X |
| 12 | Knows the basic tools of psychology used in assessment and evaluation and can use these tools. | | | | | |
| 13 | Knows professional responsibilities, authorization, and limits, recognizes psychological problems, can make the right referral for their solution, and abides by ethical principles in research and practice. | | | | | |
| 14 | They consider individual and cultural differences in research and practice and take these differences into account while evaluating the research results. | | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 40 |
| Rate of Final Exam to Success | | 60 |
| Total | | 100 |
Numerical Data
Ekleme Tarihi: 05/10/2023 - 15:20Son Güncelleme Tarihi: 05/10/2023 - 15:21
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