At the end of this course, the students; 1) understand and apply univariate and multivariate statistical analyses in analyzing large scale assessment results, 2) evaluate research papers especially in educational administration and supervision, 3) carry out data analyses in large scale assessment programs in order to put forth education policy decisions, 4) use SPSS package program effectievely, 5) prepare educational policy papers based on the data analyses results.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
No
COURSE DEFINITION
This course has been designed to provide comprehensive information about univariate and multivariate statistical analyses in evaluating the data sets obtained in national and international large scale assessments. The major focus will be on research studies which can be used to provide results for education policy decisions.
COURSE CONTENTS
WEEK
TOPICS
1st Week
General overview of the desciptive statistics and central limit theorem
2nd Week
General overview of the univariate tests
3rd Week
Type I, Type II error rates, statistical power and effect size
4th Week
Analysis of variance models: one-way, two way, three way and repeated ANOVA models
5th Week
Practicum on ANOVA models
6th Week
Introduction to multivariate designs
7th Week
Two group multivariate analysis of variance (MANOVA)
8th Week
k-group MANOVA
9th Week
Regression models and their use in education policy research
10th Week
Simple lineer and multiple regression analysis
11th Week
Bivariate regression and path analysis
12th Week
Latent variable models
13th Week
HLM regression models, data mining
14th Week
Practicum on regression models
15th Week
Practicum on regression models
RECOMENDED OR REQUIRED READING
Stevens, J. (2009). Applied Multivariate Statistics fort he Social Sciences. New York: Routledge
Hinkle, D. H. (2003). Applied Statistics in Behavioral Sciences. New York: Houghton Mifflin Company
Joreskog,K.G., Sorbom,D. (1993). Structural equation modeling with the SIMPLIS command language. Lincolnwood: Scientific Software International, Inc.
Raudenbush,S.W., Bryk,A.S., Cheong, Y.F., Congdon Jr. R. (2001).Hierarchical linear and non-linear modeling. Lincolnwood: Scientific Software International, Inc.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Dönem boyunca öğrencilerin veri analizi projelerini ve eğitim politikası raporunu başarılı bir şekilde tamamlamaları beklenmektedir. Veri analizi projesinde PISA, TIMSS ya da OECD, diğer ulusal uluslararası geniş ölçekli durum belirleme uygulamalarına yönelik çalışmalarından elde edilecek veri setinin analiz edilmesini içermektedir. Analizlerden elde edilen sonuçlar eğitim politikası raporu oluşturmada kullanılacaktır. Bu iki proje öğrencilerin performansını değerlendirmede kullanılacaktır.
Dönem boyunca öğrencilerden projelerini sunmaları da beklenmektedir. Dersin devamlılığı ve konuların sürekliliği için derse ve sınıf içi uygulamalara düzenli katılım gösterilmesi önerilmektedir.
,Lecture,Problem Solving,Questions/Answers,Project,Report Preparation,Presentation
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Practice
1
50
Project
1
50
Total(%)
100
Contribution of In-term Studies to Overall Grade(%)
100
Contribution of Final Examination to Overall Grade(%)
0
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
12
12
Preparation for Quiz
3
3
9
Individual or group work
14
4
56
Preparation for Final exam
1
30
30
Course hours
14
1
14
Preparation for Midterm exam
1
30
30
Laboratory (including preparation)
14
2
28
Final exam
1
15
15
Homework
6
6
36
Quiz
5
2
10
Report writing
10
6
60
Total Workload
300
Total Workload / 30
10
ECTS Credits of the Course
10
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
