Course Information

Course Information
Course Title Code Language Type Semester L+U Hour Credits ECTS
-- SKY650 Turkish Compulsory 3 + 0 3.0 10.0
Prerequisite Courses
Course Level Graduate
Mode of delivery Theoretical and Presentation
Course Coordinator Prof. Dr. Yalçın KARAGÖZ
Instructor(s)
Goals Purpose of this Lesson; Understanding of Nonparametric Analysis Techniques. Non-Parametric Analysis Techniques to Be Explained; Analysis of Single Sample Data, Analysis of Two Independent Samples, Analysis of More than Two Independent Samples, Analysis of Two Paired Samples, Analysis of More than Two Related Samples, Chi-Square in Cross Tables, Association Measures, Logistic Regression (Logit Models) Analysis, Clustering Analysis are the subjects. The aim is to make analyzes and interpretations regarding these issues
Course Content Nonparametric Analysis Techniques; Analysis of Single Sample Data, Analysis of Two Independent Samples, Analysis of More than Two Independent Samples, Analysis of Two Paired Samples, Analysis of More than Two Related Samples, Chi-Square in Cross Tables, Association Measures, Logistic Regression (Logit Models) Analysis, Clustering Analysis Topics Will Be Explained
Learning Outcomes
# Öğrenme Kazanımı
1 Student; Nonparametric Analysis Techniques; Analysis of Single Sample Data, Analysis of Two Independent Samples, Analysis of More than Two Independent Samples, Analysis of Two Paired Samples, Analysis of More than Two Related Samples, Chi-Square in Cross Tables, Association Measures, Logistic Regression (Logit Models) Will be able to analyze and interpret analysis and clustering analysis topics
Lesson Plan (Weekly Topics)
Week Topics/Applications Method
1. Week Basic Concepts (Variable and its types, measurement and scale levels, establishing hypotheses, null and alternative hypotheses, significance level, power of the test, selection of appropriate statistical techniques, classification of analysis techniques (classification according to the number of variables, data characteristics classification), comparison of parametric and nonparametric analysis techniques) Research, Practice, Interview
2. Week Analysis of Single Sample Data (Chi Square Test, Wald-Wolfowitz Runs, Kolmogorov-Smirnov Fit (Goodness of Fit)) Research, Practice, Interview
3. Week Analysis of Two Independent Samples (Mann-Whitney U Test, Kolmogorov-Smirnov Test, Moses Test, Wald-Wolfowitz Sequence Numbers Test) Research, Interview, Practice
4. Week Analysis of More Than Two Independent Samples (Kruskal Wallis One-Way Analysis of Variance, Mood Median Test, Jonckheere-Terpstra Ordered Alternatives Test) Practice, Interview, Research
5. Week Analysis of Two Related (Conjugate-Pair) Samples (Wilcoxon Conjugate-Pair Test, Sign Test, McNemar Test, Marginal Homogeneity Test) Research, Practice, Interview
6. Week Analysis of More than Two Related Samples (Friedman Test, Kendall's W Coefficient of Agreement Test, Cochran Q Test) Interview, Research, Practice
7. Week Chi-Square in Cross Tables (Chi-square Independence Test, Yates Chi-square Test (Continuity Correction), Fisher's Exact Test, Homogeneity Test) Interview, Research, Practice
8. Week Chi-Square Originated Nominal Association Measures (Pearson's Contingency Coefficient, Phi Coefficient, Cramer's V Coefficient) Research, Practice, Interview
9. Week Nominal Relationship Coefficients Based on Reducing Forecast Error (Goodman-Kruskal's Lambda Coefficient, Theil's Uncertainty Coefficient) Interview, Practice, Research
10. Week Ordinal Scale Correlation Coefficients (Goodman-Kruskal's Gamma (Y) Coefficient, Somers' d Coefficient (Somers' d)) Interview, Research, Practice
11. Week Ordinal Scale Correlation Coefficients (Kendall's Tau-b Coefficient (Kendall's Tau-b), Kendall-Stuart's Tau-c Coefficient (Kendall's Tau-c), Spearman Rank Correlation Coefficient) Practice, Research, Interview
12. Week Eta Coefficient, Kappa, Risk and McNemar, Chochran's and Mantel-Haenszel Statistics Interview, Research, Practice
13. Week Logistic Regression (Logit Models) Analysis (Logistic Regression Types, Binary Logistic Regression (BLOGREG) Analysis, Ordered Logistic Regression (OLAGREG) Analysis, Nominal Logistic Regression (NLOGREG) Analysis) Interview, Practice, Research
14. Week Clustering Analysis Interview, Practice, Research
*Midterm and final exam dates are not specified in the 14-week course operation plan. Midterm and final exam dates are held on the dates specified in the academic calendar with the decision of the University Senate.
The Matrix for Course & Program Learning Outcomes
No Program Requirements Level of Contribution
1 2 3 4 5
3 Ability to conduct economic and financial analysis of healthcare services
6 Ability to assess and enhance health outcomes
7 Proficiency in using statistical analysis, reporting, interpretation, and decision-making
Relations with Education Attainment Program Course Competencies
Program Requirements DK1
PY3 3
PY6 3
PY7 3
Recommended Sources
Ders Kitabı veya Notu Ders Kitabı veya Ders Notu bulunmamaktadır.
Diğer Kaynaklar
  • SPSS AMOS META Applied Biostatistics (2021)., Karagöz Yalçın, Nobel Academic Publishing, Number of Editions: 3, Number of Pages 1296, ISBN: 978-625-439-583-3
  • SPSS AMOS META Applied Statistical Analysis (2019), Karagöz Yalçın, Nobel Academic Publishing, Number of editions: 3, Number of Pages 1336, ISBN: 978-605-320-547-0
  • Biostatistics (2019)., Sümbüloğlu, Kadir, Sümbüloğlu Vildan, Hatiboğlu Publishing House, Number of editions: 20, Number of pages 302, ISBN: 9789757527121
ECTS credits and course workload
ECTS credits and course workload Quantity Duration (Hour) Total Workload (Hour)
Sınavlar
Midterm 1 1 40 40
Quiz 1 1 40 40
Quiz 2 1 40 40
Final 1 40 40
Classroom Activities 5 19 95
Total Workload 255
*AKTS = (Total Workload) / 25,5 ECTS Credit of the Course 10.0