PhD in Healthcare Management with a bachelor's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

SAY613

Course Name:

Advanced Biostatistics

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	15

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi DUYGU AYDIN HAKLI

Course Lecturer(s):

Öğr.Gör. ŞİRİN YILMAZ
Dr.Öğr.Üyesi NEVZAT BİLGİN

Course Assistants:

Course Objective and Content

Course Objectives:	To teach the subject how to approach within the scope of multivariate statistics, how to analyze with basic multivariate approaches and to gain knowledge and experience on how to interpret the findings. To learn how to use in scientific publications.
Course Content:	Basic Biostatistics Concepts Introduction to Multivariate Analysis Multiple Regression Canonical Correlation Logistic Regression Analysis Covariance Analysis Multivariate Analysis

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Knows the basic concepts of biostatistics
2) Student knows how to be able to approach an existing problem with using multivariate statistical methods.
3) Student knows the properties, aims and usage of multivariate statistical methods (matrix algebra, multivariate descriptive statistics, multivariate graphics, multivariate normal distribution, missing data analysis, similarity and dissimilarity measures, multiple regression analysis, factor analysis).
4) Student knows the assumptions of multivariate statistical methods (matrix algebra, multivariate descriptive statistics, multivariate graphics, multivariate normal distribution, missing data analysis, similarity and dissimilarity measures, multiple regression analysis, factor analysis).
5) Student knows how to apply basic multivariate analysis (matrix algebra, multivariate descriptive statistics, multivariate graphics, multivariate normal distribution, missing data analysis, similarity and dissimilarity measures, multiple regression analysis, factor analysis) to data
6) Student interprets findings of the multivariate analysis (matrix algebra, multivariate descriptive statistics, multivariate graphics, multivariate normal distribution, missing data analysis, similarity and dissimilarity measures, multiple regression analysis, factor analysis).
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Basic concept of statistics
2)	Data matrix and descriptive statistics in multivariate analysis
3)	Multivariate graphics.
4)	Standardization, multivariate normal distribution and examining the normality.
5)	Multivariate outliers, similarity and dissimilarity measures
6)	Missing value analysis.
7)	Multivariate hypothesis tests, multivariate test of population mean, testing homogeneity of variance-covariance matrices (Box M), Bartlett?s test of sphericity. Hotelling's T2.
8)	MANOVA (Multivariate one way analysis of variance). Multivariate two-way analysis of variance, variance analysis for repeated measures.
9)	Multiple Linear Regression Analysis.
10)	Article critisicm
11)	Explanatory factor analysis: Aim, importance and usage. Factor extraction methods, determining the number of factors, factor loadings, eigenvalues, factor scores and interpretation of factor scores. Factor rotation and factor rotation methods. Factor analysis and construct validity.
12)	Canonical Correlation. Aims of canonical correlation, calculating and interpreting the canonical coefficients, etc.
13)	Logistic regression. Aims of logistic regression, calculating and interpreting the coefficients, etc.
14)	Final exam- Article critisicm

Sources

Course Notes / Textbooks:	Ders notları haftalık bazda verilecektir. Her hafta makale kritiği yapılacaktır.
References:	1. Andy Field, Discovering Statistics Using SPSS, SAGE Publications,2009.

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5	6
Program Outcomes

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

Program Outcomes

Level of Contribution

Learning Activity and Teaching Methods

Expression
Lesson
Reading
Q&A / Discussion
Application (Modelling, Design, Model, Simulation, Experiment etc.)

Assessment & Grading Methods and Criteria

Presentation

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Presentation	2	% 50
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	28	8	224
Presentations / Seminar	2	40	80
Homework Assignments	3	3	9
Midterms	1	50	50
Final	1	50	50
Total Workload			455