• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 3017 STATISTICAL ANALYSIS AND RESEARCH METHODS ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ISTEM KESER

Offered to

Econometrics Econometrics (Evening)

Course Objective

The main objective of the course is to gain the ability of planning and applying a statistical research throughly. Univariate and multivariate data analysis are focused and computer-based applications on data of social sciences are aimed within the context of this course.

Learning Outcomes of the Course Unit

1   To be able to plan the statistical research throughly 2   To be able to analyse univariate and multivariate data with a computer software 3   To be able to determine errors in research 4   To be able to define appropriate sampling method 5   To be able to determine suitable data acquisition methods 6   To be able to interpret results of the statistical research 7   To be able to report the statistical reseach according to codes of conduct

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Determining aims of statistical research 2 Making a literature review about the problem 3 Investigation of statistical concepts (e.g. data, sample, target population, etc.) 4 Investigation of data source and data gathering methods 5 Desining a survey 6 Determining the sampling approach 7 Organising the field 8 Mid-term 9 Mid-term 10 Data Analysis (univariate statistical methods and computer applications) 11 Data Analysis (univariate statistical methods and computer applications) 12 Data Analysis (multivariate statistical methods and computer applications) 13 Data Analysis (multivariate statistical methods and computer applications) 14 Making reports of statistical reseach results according to codes of conduct

Recomended or Required Reading

Roy Sabo, Edward Boone, Statistical Research Methods, Springer, 2013 2. Soleman H. Abu-Bader, Using Statistical Methods in Social Science Research with a Complete SPSS Guide, Second Edition , 2011

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 STT TERM WORK (SEMESTER) 2 MTE MIDTERM EXAM 3 MTEG MIDTERM GRADE STT * 0.50 +MTE * 0.50 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 25 25 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 1 LO.2 1 1 LO.3 1 1 LO.4 1 1 LO.5 1 1 LO.6 1 1 LO.7 1 1