Advanced Research Techniques I
Production Management and Marketing
Institute of Social Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Advanced Research Techniques I | UPD105 | Compulsory | Doctorate degree | 1 | Fall | 6 |
Name of Lecturer(s)
Prof. Dr. Ümit ALNIAÇIK
Learning Outcomes of the Course Unit
1) Understands the contrast between the scientific method and other ways to acquire knowledge.
2) Develops a research model and constructs hypotheses.
3) Performs statistical data analyses with computer software.
4) Interprets the analyses results.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||
| 1 | 2 | 3 | ||
| Learning Outcomes | ||||
| 1 | No relation | High | Middle | |
| 2 | Low | High | Middle | |
| 3 | No relation | High | High | |
| 4 | Low | Low | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Marketing Research Research Methods in the Social Sciences
Course Contents
The course covers detailed data collection, processing and analyses procedures in addition to the basic steps of scientific research methods. Advanced data analyses with SPSS software and interpretation of the analyses results will also be reviewed.
Weekly Schedule
1) Fundamental concepts about the science and the scientific methodology. Model development, hypothesis formulation and testing2) Statistical distributions, mean, mod, median, standard deviation, variance, covariance, standard error, confidence interval, statistical error types, scales, scale validity and reliability
3) SPSS Data entry, data transformations, descriptive statistics, graphs, controlling the basic assumptions of parametric tests
4) Paired and independent samples t tests: SPSS applications
5) One way ANOVA, Multiple ANOVA, Post-hoc tests, Paired Comparisons, SPSS Applications
6) ANCOVA, MANCOVA, Factorial ANOVA, General Linear Model (GLM), SPSS Applications
7) Correlations Analysis: Basic Assumptions, Paired Correlations, Partial Correlation, SPSS Applications
8) Regression Analysis: Basic Assumptions, Linear Regression, Multiple Regression, Hierarchic Regression, SPSS Applications
9) Regression Analysis II: Logistic Regression, SPSS Applications
10) Factor Analyses: Exploratory Factor Analysis, Principal Components Analysis, Confirmatory Factor Analysis (Basics)
11) Cluster Analysis: SPSS Applications
12) Discriminat Analysis: SPSS Applications
13) Non Parametric Tests: ChiSquare, Sign Test, Rank Test, SPSS Applications
14) Nonparametric Tests: Kruskal Wallis Test, Friedman Anova Test, SPSS Applications
15) General Overwiev
16) Final Examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Drill and Practice
4) Self Study
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
20% |
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Contribution of Final Examination to Course Grade |
80% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required