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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| STATISTICS (MASTER) (WITH THESIS) | |
| Code | ISB571 |
| Name | Categorical Data Analysis |
| Term | 2026-2027 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. SELMA TOKER KUTAY |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of this course is to equip students with the knowledge and skills to define, classify, and analyze categorical variables using statistical methods.
Course Content
Fundamental concepts related to categorical data, probability distributions, maximum likelihood estimators, chi-square tests for independence, two-way contingency tables and logarithmic linear models and parameter estimations for these tables, three-way contingency tables and logarithmic linear models and parameter estimations for these tables.
Course Precondition
None
Resources
1. Agresti, A., An Introduction Categorical Data Analysis, John Wiley &Sons, 2007 2. Azen, R. And Walker, C.M., Categorical Data Analysis for the Behavioral and Social Sciences, John Wiley&Sons, 2011 3. Lawal, B., Categorical Data Analysis with SAS and SPSS Applications, Lawrence Erlbaum Associates, 2003
Notes
Lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learns principle components of categorical data analysis. |
| LO02 | Learns frequently used models to model categorical variables and theory underlying these models. |
| LO03 | Gains the ability to apply categorical data analysis methods using statistical software. |
| LO04 | Evaluates model suitability and performance. |
| LO05 | Understands the mathematical principles of logistic regression and log-linear models. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have in-depth theoretical and practical knowledge about Probability and Statistics | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | They have the knowledge to make doctoral plans in the field of statistics. | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge about analysis and modeling methods used in statistics. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge of methods used in statistics. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Make scientific research on Mathematics, Probability and Statistics. | |
| PLO06 | Bilgi - Kuramsal, Olgusal | Indicates statistical problems, develops methods to solve. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply innovative methods to analyze statistical problems. | 3 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Designs and applies the problems faced in the field of analytical modeling and experimental researches. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Access to information and do research about the source. | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Develops solution approaches in complex situations and takes responsibility. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Has the confidence to take responsibility. | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | They demonstrate being aware of the new and developing practices. | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | He/She constantly renews himself/herself in statistics and related fields. | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Communicate in Turkish and English verbally and in writing. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Transmits the processes and results of their studies clearly in written and oral form in national and international environments. | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | It considers the social, scientific and ethical values in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. | 4 |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Uses the hardware and software required for statistical applications. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Basic concepts related to categorical data | Literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Probability distributions and maximum likelihood estimation for categorical data | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Defining contingency tables of types 2×2, I×2, and I×J | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Comparing odds, relative risk, proportional risk, odds ratio | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Independence of categorical variables | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Testing independence in ordinal data, drawing definitive conclusions for small samples. | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Three way contingency tables, partial tables | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | General review | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
| 9 | Conditional and marginal odds ratios, conditional independence tests | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Generalized linear models for binary data | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Generalized linear models for countable data | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Interpretation of the logistic regression model | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Logarithmic linear models for two way tables | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Logarithmic linear models for three way tables | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Presentatitons of the projects | Review of the previous lecture and literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | General review | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
| 17 | Term Exams | General review | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||