Advanced Research Techniques
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 | UPD102 | Compulsory | Doctorate degree | 1 | Spring | 6 |
Name of Lecturer(s)
Prof. Dr. Ümit ALNIAÇIK
Learning Outcomes of the Course Unit
1) Make multivariate data analyses by using statistical software
2) Interpret analyses results
3) Prepare research report in appropriate format
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||
| 1 | 2 | 3 | ||
| Learning Outcomes | ||||
| 1 | Low | Low | High | |
| 2 | Low | Middle | High | |
| 3 | Low | Low | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Advanced Research Methods 1
Course Contents
Course contents include multivariate data analyses with SPSS and AMOS software. Factor, cluster, discriminant analyses, logistics regression analysis, mediator anad moderator analyses, confirmatory factor analysis, and path analysis with structural equation modeling will be covered in this course.
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
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required