Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
STA 5107	META ANALYSIS	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR IDIL YAVUZ

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

Meta analysis is used for statistically combining information from various studies. It provides researchers with tools that allow them to statistically synthesize the different findings into a single statistical outcome. Also, it allows the researchers to assess the dispersion of effects and distinguish between real dispersion and spurious dispersion which as a result can put light on hidden effects taking part in a particular result that could not be seen in a single study. In this course, the students will learn the steps involved in conducting a meta analysis, commonly used effect sizes and their sampling distributions, converting effect sizes, fixed effect and random effects meta analysis models, heterogeneity, subgroup analysis, meta-regression, power analysis for meta analysis and publication bias. R programming language will be used for applications throughout the course.

Learning Outcomes of the Course Unit

1	Choose appropriate effect sizes for various research questions and perform inference on them
2	Convert effect sizes to each other
3	Choose appropriate modeling strategies for various research hypothesis
4	Perform fixed effect meta analysis to obtain a combined effect size
5	Perform random effects meta analysis to obtain a combined effect size
6	Quantify and infer about the degree of heterogeneity present among the effects
7	Perform subgroup analysis on effect sizes
8	Fit a meta-regression model
9	Perform power analysis on the meta analysis model when necessary
10	Check for publication bias
11	Use R to perform meta analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction, steps involved in meta-analysis
2	Effect sizes based on means and correlations
3	Effect sizes based on binary data
4	Converting among effect sizes
5	Fixed effect model
6	Random effects model
7	Heterogeneity statistics
8	Applications of meta analysis models and heterogeneity checks in R
9	Subgroup analysis
10	Meta regression
11	Application of subgroup analysis and meta regression in R
12	Power analysis for meta analysis
13	Publication bias, Simpson s paradox
14	Application of publication bias checks in R

Recomended or Required Reading

Textbook(s):Introduction to Meta-Analysis, Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein, Wiley, 2009

Supplementary Book(s):The Handbook of Research Synthesis and Meta-Analysis, Harris Cooper, Larry V. Hedges, Jeffrey C. Valentine, Russell Sage Foundation Publications, 2009

Planned Learning Activities and Teaching Methods

Lecture, homework, presentation

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	ASG	ASSIGNMENT
3	FIN	FINAL EXAM
4	FCG	FINAL COURSE GRADE	MTE * 0.35 + ASG * 0.30 + FIN * 0.35
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.35 + ASG * 0.30 +RST * 0.35

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homework assignments, presentation and final exam

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr.

Contact Details for the Lecturer(s)

e-mail: idil.yavuz@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	3	42
Preparations before/after weekly lectures	14	2	28
Preparation for final exam	1	60	60
Preparing assignments	3	20	60
Preparing presentations	1	15	15
Final	1	2	2
Midterm	1	2	2
TOTAL WORKLOAD (hours)			209

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5	PO.6	PO.7	PO.8	PO.9	PO.10
LO.1		5	5	5	5
LO.2		5	5	5	5
LO.3		5				5	5
LO.4	5	5				5	5
LO.5	5	5				5	5
LO.6	5				5			5
LO.7	5				5	5			5
LO.8	5			5		5			5
LO.9			5	5			5
LO.10			5	5			5
LO.11	5								3	3

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION