At the end of this course, the students; 1) Define research methods. 2) Define data and data types. 3) Define data collection methods and use them. 4) Conduct data analysis using SPSS.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
COURSE DEFINITION
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to subject area and basic concepts (what is research? What is data? What is data analysis?
2nd Week
Research methods 1 (Quantitative research methods)
3rd Week
Research methods 2 (Qualitative research methods)
4th Week
Designing research: data collection methods
5th Week
Introducing SPSS Environment
6th Week
SPSS: entering data, cleaning data, missing data analysis
7th Week
Exploring data (mean, median, standard deviation and interpretations)
8th Week
Midterm
9th Week
Using graphs to explore the data
10th Week
Distribution of data, outlier analysis (normality)
11th Week
Reliability analysis
12th Week
Crosstabs
13th Week
Comparing means (parametric and non-parametric methods)
14th Week
Hypothesis testing, correlation analysis
RECOMENDED OR REQUIRED READING
A. Field. Discovering statistics using SPSS. 3. Baskı, Sage publications, 2009. J. Pallant. SPSS Survival Manual, 2002.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Presentation,Lecture,Practice
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
30
Project
1
25
Total(%)
55
Contribution of In-term Studies to Overall Grade(%)
55
Contribution of Final Examination to Overall Grade(%)
45
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
2,5
2,5
Preparation for Quiz
Individual or group work
14
2
28
Preparation for Final exam
1
25
25
Course hours
14
3
42
Preparation for Midterm exam
1
20
20
Laboratory (including preparation)
Final exam
1
2,5
2,5
Homework
Project
1
25
25
Total Workload
145
Total Workload / 30
4,83
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
