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PSK 602 - Advanced Statistics

Graduate Education Institute · Clinical Psychology · Doctorate

ECTS: 7.5 T+P+L: 3+0+0 Compulsory
Coordinator: Dr. Öğr. Üyesi Gülşah BALABAN

Course Objective

The primary aim of this course is to introduce students to advanced statistical analysis techniques and to enhance their theoretical understanding in this field. The course is designed to help students move beyond classical regression models and gain a comprehensive understanding of Structural Equation Modeling (SEM), including its fundamental assumptions, components, and areas of application. It also aims to develop students’ skills in modeling, operationalizing, and testing complex research questions. In addition, practical instruction will be provided on the use of the R programming language for conducting statistical analyses, enabling students to gain proficiency in statistical software tools.

Course Content

This course combines the theoretical foundations and practical applications of advanced statistical methods. The topics covered throughout the semester include:

  • Review of basic statistical concepts

  • Introduction to regression analysis and classical multiple regression models

  • Theoretical foundations of Structural Equation Modeling (SEM)

  • Developing and operationalizing conceptual models in SEM

  • Measurement models: Confirmatory Factor Analysis (CFA)

  • Structural models: Testing causal relationships

  • Model fit and interpretation of fit indices

  • Modeling with mediating and moderating variables

  • Conducting SEM analyses using R (e.g., with the lavaan package)

  • Applied data analysis exercises

  • Reporting and interpreting results in APA format

  • Scientific article writing: developing research questions, writing method sections, conducting analysis, and writing discussions

Throughout the course, both theoretical instruction and hands-on practice are emphasized. Students are expected to conduct their own data analyses and transform them into a scientific article format.


 

Required Resources

Shaughnessy, J. J., Zechmeister, E. B. ve Zechmeister, J. S. (2015). Psikolojide Araştırma Yöntemleri (İlyas Göz, Çev.). İstanbul: Nobel Yayınları. Kazdin, A. E. (2003).

Research Design in Clinical Psychology (4. Baskı). ABD: Allyn & Bacon.

Eldoğan, D., Korkmaz, L., Helvacı, E., Yeniçeri, Z. ve Kökdemir, D. (2015). Akademik Yazım Kuralları Kitapçığı (4. Baskı). Ankara: Başkent Üni. Psi. Bölümü / http://psk.baskent.edu.tr/docs/AYKK_04.pdf

Course Learning Outcomes

  1. Upon successful completion of this course, students will be able to: Explain the fundamental differences between classical regression models and structural equation modeling (SEM). Define the theoretical foundations and key concepts of SEM. Develo

Core Area Distribution

(31) Social and Behavioural Science%50 (46) Mathematics and Statistics%50

Teaching Methods

ExpressionQuestion-AnswerDiscussionExercise and PracticeBrain StormingExperiment - Test / Lab/ Workshop / Field PracticeSelf study

Assessment & Evaluation

HomeworkPerformance Assignment ( Lab / Workshop / Field Work / Seminar / Presentation / Completion Study / Thesis

ECTS / Workload

ActivityQuantityDuration (h)Total Workload
Course Duration (Including Exam Week)14342
Out of Class Study Period14570
Midterm000
Quiz000
Assignment13030
Practice000
Final14040

Course Schedule

WeekSubjectPreparation
1IntroductionSyllabus
2Internal and External ValidityKazdin (2017) Chapter 2
3Construct and Data-Evaluation ValidityKazdin (2017) Chatper 3
4Research ideasLecture notes and Kazdin (2017) Chapter 4
5Experimental designsKazdin (2017) Chapter 5
6Non-experiment designsShaughnessy et al. (2015) / Kazdin (2017) Chapter 6-7
7Singl case designsKazdin (2017) Chapter 8
8Mid-term examsMid-term exams
9Qualitative designs IKazdin (2017) Chapter 9
10Qualitative designs IIKazdin (2017) Chapter 9
11Measurement IKazdin (2017) Chapter 10
12Measurement IIKazdin (2017) Chapter 11
13Statistics ILecture notes
14Statistics IILecture notes
15Article ReviewLecture notes
16Final examsFinal exams