BÄ°S605 - BIOSTATISTICS

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
BIOSTATISTICS BÄ°S605 1st Semester 3 0 3 7
PrequisitesNone
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Discussion
 
Instructor (s)Prof. Erdem Karabulut, PhD - Prof. Pınar Özdemir, PhD - Assoc. Prof. Osman DAÄž, PhD 
Course objectiveTo teach basic statistical concepts and methods to students by examples and applications in health sciences, to provide understanding and evaluating the statistical analysis used in the literature of their own study fields. 
Learning outcomes
  1. After completing the course the students will be able to: decide to proper basic statistical analysis
  2. do calculations and analysis individually
  3. interpret the findings
  4. understand statistical analysis used in the literature of their own study fields
  5. criticize statistical analysis used in the literature of their own study fields
  6. have sufficient theoretical and practical basis required in advanced statistical courses
Course Content1. Basic statistical concepts
2. Descriptive statistics
3. Theoretical distributions, normal distribution
4. Sampling distributions
5. Basic research designs
6. Basic sampling methods
7. Hypothesis tests
8. Measures of association
9. Linear regression analysis
10. Measures of risk, evaluation of diagnostic tests 
References1. Sümbüloglu K ve Sümbüloğlu V. Biyoistatistik. Seçkin Yayıncılık, Ankara, 2010.
2. Özdamar K. Pasw ile Biyoistatistik. Kaan Kitabevi, Eskişehir, 2013.
3. Alpar R. Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle UYGULAMALI İSTATİSTİK ve GEÇERLİK-GÜVENİRLİK. Detay Yayıncılık, Ankara, 2018.
4. Daniel, Wayne W. Biostatistics 9th Edition, New York: John Wiley&Sons, 2013. 

Course outline weekly

WeeksTopics
Week 1Basic statistical concepts such as: statistics and biostatistics, population, sample, statistic, parameter, data, variable, types of data, and etc.
Week 2Descriptive statistics: grouping the data, measures of central tendency, measures of location, histogram, bar graph, stem & leaf graph, box-plot, etc.
Week 3Descriptive statistics: measures of dispersion, error bar graph, etc.
Week 4Examining the association among the variables by tables and graphics: Cross tables, tables regarding descriptive statistics (mean, s. deviation, etc.), multivariate applications of basic graphics, scatter plots, etc.
Week 5Standardization (z and T scores). Theoretical distributions: Normal distribution, and normality transformations. Tests and graphs for normality. Sampling distributions and confidence intervals
Week 61st Midterm Examination
Week 7Research methods. Introduction to hypothesis tests: Aims and stages of hypothesis tests, possible types of errors, p and alpha values, power, effect size, decision process
Week 8Hypothesis tests: Parametric and nonparametric one sample tests. Parametric and nonmparametric independent two sample tests.
Week 9Hypothesis tests: Parametric and nonparametric k independent sample tests.
Week 10Hypothesis tests: : Parametric and nonparametric dependent two-sample tests). Parametric and nonparametric k dependent sample tests .
Week 112nd Midterm Examination
Week 12Measures of association: Pearson correlatin coefficient, Spearman correlation coefficient, Phi, Cramer V, Eta, etc.
Week 13Simple and multiple linear regression analysis.
Week 14Simple and multiple linear regression analysis and examining an article.
Week 15Preparation to final exam
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments1230
Presentation00
Project00
Seminar00
Midterms220
Final exam150
Total100
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation000
Project000
Homework assignment12448
Midterms (Study duration)21020
Final Exam (Study duration) 11616
Total Workload4339210

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has current medical nursing knowledge in an expert level     
2. Interprets, analyzes and synthesizes medical nursing knowledge with different disciplines? knowledge to create original knowledge     
3. Plans, implements, reports, presents and publishes research which produces solutions to problems in medical nursing   X 
4. Uses statistical programs effectively, uses proper statistical methods, interprets results    X
5. Crafts strategic solutions to actual and unpredictable complex issues needed medical nursing expertise, resolves responsibly, evaluates outcomes   X 
6. Adopts and uses personal development and lifelong learning principles in the field   X 
7. Critiques articles, follows evidence-based practices, conducts research to create evidence for medical nursing   X 
8. Analyzes concepts&theories that supports evidence-based research&advanced nursing practices   X 
9. Reports and presents own knowledge, current developments and studies in medical nursing to national/international peers  X  
10. Oversees and teaches social, scientific and ethical values among data collection, interpretation, and presentation  X  
11. Actively participates in disciplinary studies with healthcare knowledge and skills     
12. Knows the importance of ethical principles&committees for individual and society, acts ethically     
13. Assumes leadership roles, thinks critically, engages in professional activities     
14. Acts sensitively to public health, follows and contributes to relevant health-social policies     

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest