Information Package
baskent.edu.tr

Master's of Computer and Instructional Technologies Education without Thesis (Distance Education)

BOTU612

ADVANCED STATISTICS

Course Code
BOTU612
Semester
Second Term (Spring)
Theory+Practice
2 + 2
ECTS
6
Course TypeCompulsory Course
Course LevelMaster's Degree Without Thesis
Year of Study1
SemesterSecond Term (Spring)
ECTS6
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Will be able to explain advanced statistical methods,
  2. will be able to use univariate and multivariate statistical methods,
  3. will be able to make statistical applications in the appropriate package program,
  4. Will be able to determine and apply the appropriate statistical analysis techniques according to the research purpose and interpret the results of these analyzes correctly,
  5. Will be able to know which tests will be used in which situations, will be able to interpret the results of the analysis by making calculations about them.
Mode of DeliveryE-Learning
PrerequisitesNo
Recommended Optional Programme ComponentsBÖTU611
Course DescriptionCarry out research and training required to perform a detailed analysis of advanced statistical methods to learn. Frequency distributions, the concept of correlation, analysis of variance and covariance properties, confirmatory factor analysis methods used in modeling the structural equality.
Course Contents
WEEK TOPICS
1st Week Basic Concepts
2nd Week Regression Analysis
3rd Week Multiple Frequency Analysis
4th Week Analysis of Covariance (ANCOVA)
5th Week Multivariate Analysis of Variance (MANOVA)
6th Week Logistic Regression
7th Week SPSS Application
8th Week SPSS Application
9th Week Cluster Analysis
10th Week Discriminant Analysis
11th Week Exploratory Factor Analysis
12th Week Structural Equation Modeling
13th Week Confirmatory Factor Analysis
14th Week SPSS Application
15th Week
Recommended or Required ReadingArıcı,Hüsnü. (1984) İstatistik Yöntemler ve Uygulamalar. Ankara: Meteksan Matbaacılık
Büyüköztürk, (2006). Soysal Bilimler İçin Veri analizi El Kitabı. Ankara: Pegem Yayınları.
Büyüköztürk, Ş., Çokluk, Ö. ve Köklü, N. (2011). Sosyal Bilimler İçin İstatistik (7.baskı). Türkiye. Pegem.
Cula, S., & Muluk, Z. (2006). Temel istatistik yöntemleri. Başkent Üniversitesi.
Freund, J. E., & Perles, B. M. (2007). Modern elementary statistics. Prentice Hall.
Gravetter F. & Wallnau L., B., (1985)Statistics for the Behavivioral Sciences. West Publishing co. Minnesota, USA.
Hinkle, D.E.,Wiersma, W., ve Jurs, S.G. (1988) Applied Statistics for the Behavioral Sciences. Boston: Houghton Mifflin Company
Tavşancıl E. (2006). Tutumların Ölçülmesi ve SPSS ile Veri Analizi, Nobel Yayın Dağıtım, 3. Baskı.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Discussion
  • Questions/Answers
  • Problem Solving
  • Project
Assessment Methods and Criteria
Quantity Percentage (%)
Assignment 2 20
Total(%) 20
Contribution of In-term Studies to Overall Grade(%) 20
Contribution of Final Examination to Overall Grade(%) 80
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam 1 2 2
Preparation for Quiz
Individual or group work 12 3 36
Preparation for Final exam 1 30 30
Course hours 14 4 56
Preparation for Midterm exam 1 20 20
Laboratory (including preparation)
Final exam 1 3 3
Homework 2 15 30
Total Work Load177
Total Work Load / 30 (h)5.9
ECTS Credit of the Course6
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5
K1 X X
K2 X
K3
K4
K5 X X X X X
K6 X X X X
K7 X X X X X
K8 X X X
K9 X X X X
K10
K11 X
K12
K13 X X X
K14
K15 X
K16
K17
K18