|
| Course Description |
|
| Course Name |
: |
Applications of Multivariate Analysis in Economic and Social Researches |
|
| Course Code |
: |
TE-553 |
|
| Course Type |
: |
Optional |
|
| Level of Course |
: |
Second Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Fall (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. TUNA ALEMDAR
|
|
| Learning Outcomes of the Course |
: |
Be able to describe main concepts, theories and methods used in multivariate statistical analyses
to be able to determine the most appropriate multivariate analysis technique for various research problems
to be able to apply main multivariate research methods in economic and social fields, to be able to get use of appropriate software and to interprete the results
to be able to critically interprete scientific articles written on actual problems of economic and social sciences under the light of the knowledge acquired from multivariate analysis methods
to be able to transfer results of multivariate analyses to others by using verbal, written and visual tools
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
Dimesion reduction in multivariate data sets in economic and social researches, examining interdependency structure among data, classification of observations and variables, conducting statistical tests |
|
| Course Contents |
: |
Exploratory and confirmatory mutltivariate statistical analyses;relations and variables; categorical variables; dimension reduction in data; techniques of grouping and classifying; main multivariate analysis techniques; principal components analysis; factor analysis, discriminant analysis, cluster analysis, correspondence analysis, multiple regression analysis, canonical corelation, multidimensional scaling, structural equations modelling and applications of other multidimensional analyses and interpreting analysis results |
|
| Language of Instruction |
: |
Turkish |
|
| Work Place |
: |
Class |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Data types, classification of multivariate analysis methods |
Lecture notes and related sections of resources suggested |
Lecture |
|
2 |
Data preparation for multivariate analyses, check for normality, concept of multivariate normality |
Lecture notes and related sections of resources suggested |
Lecture |
|
3 |
Correlation, partial correlation |
Lecture notes and related sections of resources suggested |
Lecture |
|
4 |
Principle Components Analysis, difference from factor analysis |
Lecture notes and related sections of resources suggested |
Lecture |
|
5 |
Factor analysis, exploratory and confirmatory factor analyses, factor scores, matrix rotations, application areas |
Lecture notes and related sections of resources suggested |
Lecture |
|
6 |
Cluster analysis |
Lecture notes and related sections of resources suggested |
Lecture |
|
7 |
Discriminant analysis |
Lecture notes and related sections of resources suggested |
Lecture |
|
8 |
Multiple regression and logistic regression analyses |
Lecture notes and related sections of resources suggested |
Lecture |
|
9 |
Multidimensional Analysis of Variance (MANOVA); t tests; comparion of ANOVA with MANOVA |
Lecture notes and related sections of resources suggested |
Lecture |
|
10 |
MIDTERM EXAM |
Lecture notes and related sections of resources suggested |
Written exam |
|
11 |
Canonical correlation; multidimensional scaling |
Lecture notes and related sections of resources suggested |
Lecture |
|
12 |
Correspondence analysis |
Lecture notes and related sections of resources suggested |
Lecture |
|
13 |
Conjoint analysis |
Lecture notes and related sections of resources suggested |
Lecture |
|
14 |
Introduction to Structural Equations Models |
Lecture notes and related sections of resources suggested |
Lecture |
|
15 |
General review |
Lecture notes and related sections of resources suggested |
Lecture |
|
16/17 |
FINAL EXAM |
All lecture notes and resources |
Written exam |
|
|
|
Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Course notes and slides prepared and regularly updated by the instructor
|
| |
| Required Course Material(s) |
Hair Joseph F., Black B., Babin B., Anderson E., Tatham R.L. (2006) Multivariate Data Analysis, 6th Edition. Upper Saddle River. New Jersey: Prentice Hall.
Manly Bryan F.J. (2005) Multivariate Statistical Methods: A Primer. Boca Raton FL. Chapman and Hall/ CRC Press
|
|
|
|
Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
|
Homeworks/Projects/Others |
0 |
40 |
|
Total |
100 |
|
Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
|
Rate of Final Assessments to Success
|
60 |
|
Total |
100 |
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Able to further develop and deepen knowledge acquired based on the undergradute level proficiencies in the fields of farm management and agricultural policy |
2 |
|
2 |
Able to comprehend interactions among related disciplines and field of agricultural economics |
0 |
|
3 |
Able to use theoretical and practical knowledge of agricultural economics in their specialization area |
0 |
|
4 |
Able to prepare reports on developments in national economy and agricultural sector; able to critically evaluate historical and actual developments in agriculture and economy; able to observe and interpret economics related publications |
0 |
|
5 |
Able to use software widely used in agricultural economics |
0 |
|
6 |
Able to combine data of actual developments with his knowledge, data and findings obtained in various disciplines and interpret them while supporting them with qualitative and quantitative data and also forming new knowledge through synthesis |
5 |
|
7 |
Ability to take the lead in multidisciplinary teams and work in teams |
0 |
|
8 |
Able to critically evaluate specialized knowledge and abilities acquired in agricultural economics and direct his/her own learning process |
0 |
|
9 |
Able to transfer research results using verbal, written and visual tools |
4 |
|
10 |
Able to develop analytical approaches in order to solve complicated problems that cannot be forecast beforehand in applications of agricultural economics and policy; able to design research process; able to produce solutions by taking on responsibility and to evaluate and justify solutions |
4 |
|
11 |
Constantly adapt himself to new scientific developments |
0 |
|
12 |
Able to use acquired and digested agricultural economics knowledge in multidisciplinary studies, able to explain them, to transfer them to others, able to examine conclusions critically |
0 |
|
13 |
Able to collect data according to scientific methods in order to solve economic problems, able to supervise and interprete data collected while taking into consideration social, scientific and ethical values |
5 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
|
|
| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
4 |
56 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
4 |
56 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
0 |
0 |
0 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
18 |
18 |
|
Final Exam |
1 |
20 |
20 |
|
Total Workload: | 150 |
| Total Workload / 25 (h): | 6 |
| ECTS Credit: | 6 |
|
|
|