GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
STATISTICAL ANALYSIS IN SCIENCE EDUCATION RESEARCH/4310120
Course Title: STATISTICAL ANALYSIS IN SCIENCE EDUCATION RESEARCH
Credits 3 ECTS 7
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Students know basic statistical concepts, transfer data from the excel file into SPSS file, define the variables.
Students can obtain the results of descriptive statistics for the data. They tabulate frequency distributions and the others.
Students can analyze they data the simple and partial correlational and simple and multiple regression analysis techniques.
Students can analyze data with t-tests, one-way ANOVA and two-way ANOVA.
Students can analyze data one-way ANOVA for repeated measures and two-way ANOVA for mixed measures.
Students can analyze data analysis of covariance (ANCOVA).
Students can apply their data the multivariate statistical techniques (factor analysis, MANOVA, MANCOVA).
Students can apply data non-parametric statistical techniques (chi-square test, Mann-Whitney U-test, Kruskal-Wallis H-test, the Wilcoxon test).
Students are able to analyze the validity and reliability of the tests.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Introduction: Basic statistical concepts, introduction of SPSS and entering data in spss, variable description and some basic orders
2. Week  Description of data: Frequency distribution, central tendency and variability measures
3. Week  Bivariate and partial correlation
4. Week  Comparison of mean scores in independent measures: T-test, One-way ANOVA, Two way ANOVA
5. Week  Comparison of mean scores in dependent measures: T-test, One-way ANOVA for repeated measures
6. Week  Comparison of mean scores in mixed measures: Two way ANOVA for mixed measures
7. Week  Bivariate and multiple regressions
8. Week  Midterm exam
9. Week  Analysis of covariance (ANCOVA)
10. Week  Multivariate statistics: Factor analysis, MANOVA, MANCOVA
11. Week  Multivariate statistics: Factor analysis, MANOVA, MANCOVA
12. Week  Non-parametric statistics: Chi-square test, Mann Whitney U-test, Kruskal Wallis H-test, Wilcoxon signed rank test for paired samples
13. Week  Non-parametric statistics: Kruskal Wallis H-test, Wilcoxon signed rank test for paired samples
14. Week  Analysis of validity and reliability of tests
15. Week  Analysis of validity and reliability of tests
16. Week  Final exam
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
40
 Assignment
0
0
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
40
 Percentage of Final Exam to Total Score  
60
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
14
3
42
 Searching in Internet and Library
3
3
9
 Material Design and Implementation
8
3
24
 Report Preparing
3
3
9
 Preparing a Presentation
6
3
18
 Presentation
2
3
6
 Midterm Exam and Preperation for Midterm Exam
1
6
6
 Final Exam and Preperation for Final Exam
1
8
8
 Other (should be emphasized)
4
4
16
 TOTAL WORKLOAD: 
180
 TOTAL WORKLOAD / 25: 
7.2
 Course Credit (ECTS): 
7
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1X
2X
3X
4X
5X
6X
7X
8X
9X
10X
11X
12X
13X
14X
 -- NAME OF LECTURER(S)
   (Prof. Dr. Mustafa Sarıkaya)
 -- WEB SITE(S) OF LECTURER(S)
   (http://websitem.gazi.edu.tr/site/sarikaya)
 -- EMAIL(S) OF LECTURER(S)
   (sarikaya@gazi.edu.tr)