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| Course Description |
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| Course Name |
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Data Collection Techniques in Social Sciences |
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| Course Code |
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TE-542 |
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| Course Type |
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Optional |
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| Level of Course |
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Second Cycle |
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| Year of Study |
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1 |
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| Course Semester |
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Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
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Prof.Dr. DİLEK BOSTAN BUDAK
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| Learning Outcomes of the Course |
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1. Able to define data types, scales and data gathering methods.
2. Able to draw up a research.
3. Able to organize a survey and put it into practise.
4. Able to analyze datas gathered due to their types.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
TE-542 Data Collection Techniques in Social Sciences
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| Recommended Optional Programme Components |
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None |
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| Aim(s) of Course |
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Helping students to gain the capability of gathering datas and analyzing datas in social sciences. |
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| Course Contents |
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Data types ( nominal, continous, discontinous, proportional). Definitive statistics are used due to the data types (Pearson r., Kendall´s Tau b, Kendall´s Tau c, Spearman r., etc.) Stages of survey preparation. Preparation of questions. Source of errors. Testing reliability and validity (Cronbach´s alpha, t-test, test-retest, KR20/21, Split half)
Scales are using on information, attitude, behaviour, values. Scales of Thurstone, Guttman, Likert, Semantic Differential.
Data gathering methods such as; Mail, phone, face to face, observation and focus groups
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| Language of Instruction |
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Turkish |
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| Work Place |
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Class |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
The importance of research. Research methods |
Lecture notes and recommended resources relevant sections |
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2 |
Defining the problem in research and define the purposes |
Lecture notes and recommended resources relevant sections |
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3 |
Defining the variables. Data types |
Lecture notes and recommended resources relevant sections |
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4 |
Definitive statistics are used due to the data types |
Lecture notes and recommended resources relevant sections |
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5 |
What is survey. Steps of survey preparation |
Lecture notes and recommended resources relevant sections |
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6 |
Preparation of questions. Error sources |
Lecture notes and recommended resources relevant sections |
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7 |
Test of validity and reliability of surveys |
Lecture notes and recommended resources relevant sections |
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8 |
Likert Scale |
Lecture notes and recommended resources relevant sections |
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9 |
Mid Term |
Lecture notes and recommended resources relevant sections |
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10 |
Semantic Differential Scale |
Lecture notes and recommended resources relevant sections |
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11 |
Thurstone Scale |
Lecture notes and recommended resources relevant sections |
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12 |
Guttman Scale |
Lecture notes and recommended resources relevant sections |
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13 |
Data gathering methods; Mail, phone |
Lecture notes and recommended resources relevant sections |
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14 |
Data gathering methods;observation
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Lecture notes and recommended resources relevant sections |
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15 |
Data gathering methods;focus groups. |
Lecture notes and recommended resources relevant sections |
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16/17 |
Presentations and discussions |
Projects prepared |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Öğretim üyesince hazırlanan ve sürekli güncellenen ders notları ve slaytları
Survey Research Methods (Floyd J. Fowler)
Handbook in Research and Evaluation (Stephen Isaac; William B. Michael)
Sosyal Bilimlerde Araştırma (Prof.Dr.Ali Balcı)
Instrumentation and Data Collection Procedures fort he Social Sciences: Course Material (Emmalou Norland)
Anket (Türker Baş)
Basic Statistics for Behavioral Sciences)
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
2 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Able to further develop and deepen knowledge acquired based on the undergradute level proficiencies in the fields of farm management and agricultural policy |
1 |
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2 |
Able to comprehend interactions among related disciplines and field of agricultural economics |
1 |
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3 |
Able to use theoretical and practical knowledge of agricultural economics in their specialization area |
1 |
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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 |
2 |
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5 |
Able to use software widely used in agricultural economics |
2 |
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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 |
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7 |
Ability to take the lead in multidisciplinary teams and work in teams |
1 |
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8 |
Able to critically evaluate specialized knowledge and abilities acquired in agricultural economics and direct his/her own learning process |
2 |
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9 |
Able to transfer research results using verbal, written and visual tools |
3 |
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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 |
1 |
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11 |
Constantly adapt himself to new scientific developments |
3 |
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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 |
4 |
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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). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
16 |
3 |
48 |
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Out of Class Study (Preliminary Work, Practice) |
16 |
3 |
48 |
| Assesment Related Works |
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Homeworks, Projects, Others |
2 |
15 |
30 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
15 |
15 |
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Total Workload: | 151 |
| Total Workload / 25 (h): | 6.04 |
| ECTS Credit: | 6 |
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