COURSE INTRODUCTION AND APPLICATION INFORMATION

Course Name
Advanced Quantitative Methods in Psychology
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
PSY 601
Fall
3
0
3
7.5
Prerequisites
None
Course Language
English
Course Type
Required
Course Level
Third Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Discussion
Q&A
Lecturing / Presentation
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives This course will focus on advanced statistical techniques used in psychology. This course aims to teach students to formulate complex statistical hypotheses in psychological research and aims to make students decide on the appropriate statistical analysis in testing these hypotheses. Finally it is aimed to teach students interpreting and reporting the results of the analysis.
Learning Outcomes The students who succeeded in this course;
  • will be able to describe advanced statistical concepts involved in psychology.
  • Will be able correctly identify the statistical analysis that should be used for a complex hypothesis.
  • Will be able to correctly apply statistical techniques to psychological data.
  • Will be able to correctly interpret results of analyses of psychological data.
  • Will be able to clearly convey orally and in writing the details of statistical analyses and the results.
Course Description This course consists of introduction to the R Language and the R Environment, comparing several means, linear mixed effects model and signal detection analysis.
Related Sustainable Development Goals

 



Course Category

 Core Courses
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Brief description of the course content
2 Introduction to the R Language The R Environment Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 3 Braun W. J. and Murdoch, D. J. (2007). A First Course in Statistical Programming with R. Cambridge: Cambridge University Press. Chapter 1 & 2
3 Introduction to the R Language The R Environment Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 3 Braun W. J. and Murdoch, D. J. (2007). A First Course in Statistical Programming with R. Cambridge: Cambridge University Press. Chapter 1 & 2
4 Comparing several means: ANOVA Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 10
5 Repeated-measures designs Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 13
6 Mixed designs Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 15
7 Linear Mixed Effects Analysis Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 19
8 Linear Mixed Effects Analysis Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 19
9 Linear Mixed Effects Analysis Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 19
10 Linear Growth Models Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 19
11 Linear Growth Models Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Chapter 19
12 Signal Detection Analysis Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31, 137–149.
13 Signal Detection Analysis Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31, 137–149.
14 Bin Analysis Tagliabue, M., Zorzi, M., Umilta, C., & Bassignani, F. (2000). The role of long-term-memory and short-term-memory links in the Simon effect. Journal of experimental psychology. Human perception and performance, 26 2, 648-70 .
15 Review of the Semester
16 Review of the Semester
Course Notes/Textbooks
Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. ISBN-13: 978-1446200469
ISBN-10: 1446200469.
 
Braun W. J. and Murdoch, D. J. (2007). A First Course in Statistical Programming with R. Cambridge: Cambridge University Press. ISBN 978-1-107-57646-9
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
3
40
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exam
Midterm
Final Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
4
70
Weighting of End-of-Semester Activities on the Final Grade
1
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
13
5
65
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
3
14
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exam
Midterms
Final Exams
1
40
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
 Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to develop and deepen the current and advanced knowledge in the experimental science of psychology with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.

 X
2

To be able to conceive the interdisciplinary interaction which the experimental psychology is related with, come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.

 X
3

To be able to evaluate and use new methodological knowledge in a systematic way and gain higher level of skills on research methodology of the mind, behavior, and the brain.

 X
4

To be able to develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field; research, conceive, design, adapt and implement an original subject.

 X
5

To be able to make critical analysis, synthesis and evaluation of new and complex ideas in the field of experimental psychology.

 X
6

To be able to develop new ideas and methods in the field of experimental psychology by using high level mental processes such as creative and critical thinking, problem solving and decision making.

 X
7

To be able to broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.

 X
8

To be able to organize and participate in scientific activities such as workshops, conferences, and panels to elaborate possible solutions to the problems which may be specific to experimental psychology or interdisiplinary.

 X
9

To be able to contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements by following pioneer and innovative methods and theories of the mind, behavior and the brain trilogy.

 X
10

To be able to develope effective and functional means of communication to analyze mental relations and processes in the context of experimental psychology.

 X
11

To be able to contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.

 X
12

To be able to write an original dissertation in accordance with the criteria related to the field of Experimental Psychology.

 X
13

To be able to communicate and discuss orally, in written and visually with peers by using English language.

 X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest