SAY712 - COMPLEX SAMPLE DATA ANALYSIS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| COMPLEX SAMPLE DATA ANALYSIS | SAY712 | Any Semester/Year | 2 | 2 | 3 | 10 |
| Prequisites | SAY611 or equivalent | |||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Drill and Practice | |||||
| Instructor (s) | Prof.Dr. Ahmet Sinan Türkyılmaz | |||||
| Course objective | Learning about analyzing a survey data set with complex sample properties | |||||
| Learning outcomes |
| |||||
| Course Content | Survey estimation and inference for complex sample designs, multi-stage designs, stratification, clustering, finite population corrections, design effects, effective sample size; Sampling distributions, confidence intervals; Taylor series linearization, Jackknife repeated replications, Balanced repeated replications; Ultimate clusters; Sampling error estimation with statistical software; Subpopulation estimates; Analysis for categorical data; Linear regression for complex samples; logistic regression for complex samples; poisson and negative binomial regression. | |||||
| References | Heeringa SG, West BT, Berglund PA (2010). Applied survey data analysis. Boca Raton, FL: Chapman & Hall/CRC. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction of concepts |
| Week 2 | Survey estimation and inference for complex sample designs (Part 1) |
| Week 3 | Survey estimation and inference for complex sample designs (Part 2) |
| Week 4 | Sampling error estimation for descriptive statistics. Taylor Series linearization method. Replication Methods for Variance Estimation. Jackknife Repeated Replication (JRR). Balanced Repeated Replication (BRR) |
| Week 5 | Sampling error calculation models; ultimate clusters |
| Week 6 | Inference for percentiles. Subpopulation estimates. Functions of survey estimates |
| Week 7 | Analysis Methods for Categorical Data |
| Week 8 | Midterm Exam |
| Week 9 | Linear Regression Review |
| Week 10 | Linear Regression for Complex Samples |
| Week 11 | Logistic Regression for Complex Samples |
| Week 12 | Multinomial, Ordinal Logistic Regression |
| Week 13 | Poisson, Negative Binomial Regression |
| Week 14 | Imputation of item missing data. Multiple imputation inference for survey data |
| Week 15 | Preparation for Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 30 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 6 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 2 | 32 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 4 | 56 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 16 | 80 |
| Midterms (Study duration) | 1 | 42 | 42 |
| Final Exam (Study duration) | 1 | 90 | 90 |
| Total Workload | 37 | 154 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Gains theoretical knowledge on research methodology, concepts on quantitative research methods, quantitative sampling techniques, learning, implementing, and developing models and techniques in quantitative analysis, theory, and develops skills to transform and improve knowledge into design, implementation, analysis. | X | ||||
| 2. Gains theoretical knowledge on qualitative research methods, theory, and skills to transform and improve knowledge into design, sampling, generating data and analysis, knowledge on differences between methods and their use, improvement/combined use of these methods, evaluation with experts and institutions, focus groups and in-depth interviews, skills to implement them. | X | ||||
| 3. Gains ability to evaluate quality of researches, interpret/improve results with experts and institutions, transform the results into oral/written presentations, and present them at (inter)national meetings. | X | ||||
| 4. Develops skills to conduct generalizable research, interpret results of analyses, transform them into publications abiding by ethics, gain ability and consciousness to prepare (inter)national projects, evaluate them with experts and institutions. | X | ||||
| 5. Contributes to development of methods by producing a thesis using research methods. | X | ||||
| 6. Follows international publications, communicate with colleguaes, using English at a level no lower than the Common European Framework of References for Languages B2. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest