ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
REK5202	Research Methods II	Spring	3	0	3	8

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	Turkish
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Prof. Dr. HASAN KEMAL SUHER
Recommended Optional Program Components:	None
Course Objectives:	Development of skills in advertising and marketing research with respect to generating research questions, following correct methods in order to reach reliable results, planning qualitative and quantitative studies, developing questionnaries, coding data and analyzing the data with the SPSS program and reporting the findings.

Learning Outcomes

The students who have succeeded in this course;
1)The students who succeeded in this course;
The students will be able to define what marketing and advertising research is, what kinds of information it can provide, and how it is used by marketing management.

2)To identify and explain alternative research methods and their relative strengths and weaknesses.

3)To determine which advertising and marketing research methods will be suitable to analyze which types of marketing problems.

4)To identify and describe major types of measurement techniques and data collection methods.

5)To analyze data obtained through marketing research using the SPSS software.

Course Content

This course provides a broad overview of social sciences researches especially marketing and advertising research from a practical and applied perspective. Students will learn the basics of research and how to conduct a research project.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Social Research - Research Design
3)	Conceptualism, Operational Defination and Mesurement
4)	Sampling - Reability and Validity
5)	Research Techniques (Survey Research)
6)	Research Techniques (Survey Research)
7)	Research Techniques (Focus Group)
8)	Qualitative Data Analysis
9)	Quantative Data Analysis (Introduction to Statistics and Hypothesis Testing)
10)	Introduction to SPSS (Menus and Data Entry)
11)	Frequency Tables and Descriptive Statistics
12)	Cross Tables and Chi-Square Analysis
13)	T-Test
14)	One Way of Analysis (ANOVA)

Sources

Course Notes / Textbooks:

Earl Babbie. The Practice of Social Research, 12th Edition (America: Wadsworth, 2010)

Roger D. Wimmer ve Joseph R. Dominick, Mass Media Research, An Introduction (America: Wadsworth, 2011) 9. Basım

Darren George and Paul Mallery, SPSS For Windows Step By Step, A Simple Guide and Reference, 10th Edition (America: Pearson, 2010)

References:

Arthur Asa Berger, Media and Communication Research Methods, An Introduction to Qualitative and Quantitative Approaches (Sage Publications, 2000)

John W. Creswell, Research Design, Qualitative, Quantitative and Mixed Methods Approaches, 2nd Edition (Sage Publications, 2003)

Ian Brace, Questionnaire Design, How to Plan, Structure and Write Survey Material for Effective Market Research (İngiltere: Kogan Page, 2004)

Filiz Çakar, Sosyal Bilimlerde İstatistik (Alfa Yayınları, 2000)

Şener Büyüköztürk, Veri Analizi El Kitabı, İstatistik, Araştırma Deseni, SPSS Uygulamaları ve Yorum, 4. Basım (Pegem yayıncılık, 2004)

Darren George and Paul Mallery, SPSS For Windows Step By Step, A Simple Guide and Reference, 6th Edition (America: Pearson, 2006)

Andy Field, Discovering Statistics Using SPSS, 2nd Edition (Sage Publications, 2005)

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	14	% 0
Homework Assignments	2	% 10
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	13	75
Laboratory	13	75
Final	3	45
Total Workload		195

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Have sufficient background in mathematics, science and artificial intelligence engineering.
2)	Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions.
3)	Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose.
4)	Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction.
5)	Select and use modern techniques and tools necessary for engineering applications.
6)	Design and conduct experiments, collect data, and analyse and interpret results.
7)	Work effectively both as an individual and as a multi-disciplinary team member.
8)	Access information via conducting literature research, using databases and other resources
9)	Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning.
10)	Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field.
11)	Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level.
12)	Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age.
13)	Have a sense of professional and ethical responsibility.
14)	Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices.