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TR

DATA ANALYSIS & COMPUTER APPLICATIONS IN MANAGEMENT

Course
BUSN531 - DATA ANALYSIS & COMPUTER APPLICATIONS IN MANAGEMENT
Department
Master of Business Administration - English - Master
Course Type
Course
Status
Required
Language
English
Credit
3
ECTS
8
T+P+L
3 + 0 + 0
Course Coordinator(s)
Prof. Dr. Cem TANOVA
Prerequisite
-
Keywords

Course Description

This course covers a description of the research process in general and each of the steps involved in more detail. The course focuses on alternative ways of carrying out each step and guidelines for selecting among these alternatives according to the needs of a specific research problem. The course also describes using software packages like SPSS and interpretation of the results of data analysis. Course is designed to enable student to apply commonly used statistical techniques to different datasets. For this purpose, the course will focus on descriptive analysis, correlation, t-test, ANOVA, regression and mediation and moderation analysis. The students will use SPSS software to work on the datasets.

DATA ANALYSIS & COMPUTER APPLICATIONS IN MANAGEMENT

Evaluation Tools (Active Term)

Item Type Weight (%)
Midterm Midterm 40
Final Final 45
Project Project 15
Total 100

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Week Topic
Week 1 Introduction of the course, overview / Research process
Week 2 Descriptive statistics
Week 3 Introduction to SPSS/Data cleaning
Week 4 Descriptive statistics (Class Practice)
Week 5 Reliability and validity
Week 6 Reliability and validity (Class Practice)
Week 7 Reliability and validity (Class Practice)
Week 8 Midterm
Week 9 T-test, ANOVA
Week 10 T-test, ANOVA (Class Practice)
Week 11 Correlation
Week 12 Correlation (Class Practice)
Week 13 Regression
Week 14 Regression (Class Practice)
Week 15 Data Practice

Reference Books & Course Materials

  1. 01 Field, A. 2024. Discovering Statistics Using IBM SPSS Statistics, Sage, London.
  2. 02 Pituch, K.A. & Stevens, J.P. 2016. Applied multivariate statistics for the social sciences, Taylor& Francis: New York.
  3. 03 Te Grotenhuis, M. & Matthijssen, A. 2016. Basic SPSS Tutorial, Sage: UK.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

  1. P01 PO1: Become knowledge workers who whose focus will be on continuous learning as their competitive advantage by learning new methods and skills which they can use both in their lives and workplace, especially in health sector.
  2. P02 PO2: Developed various skills and acquired the necessary knowledge which are required to keep improving themselves by working independently.
  3. P03 PO3: Become among the best professionals in the health care industry due to their developed skills and gathered knowledge which will set them apart from the competition.
  4. P04 PO4: Been able to apply appropriate methods and analytical procedures to analyze health-related issues faced by the organizations and empirically investigate and propose creative and valid solutions based on this analysis.
  5. P05 PO5: Learnt to use technology in class and research in an ethical and secured manner by implementing these skills in their professional and private lives.
  6. P06 PO6: Gained the ability to apply theories and use appropriate management tools and methods collaboratively in order to develop strategic decisions which are specific to unique health management contexts.
  7. P07 PO7: Practiced and mastered communicating effectively, both verbally and in writing, with individuals from different cultures within and outside the academia and organizations.
  8. P08 PO8: Been able to implement the learnt skills and knowledge, by developing research-based solutions to current regional and global health issues.
  9. P09 PO9: Developed enthusiasm and awareness about the environment by associating health and management theories with ethical and environmental approaches in order to further study and investigate critical health managerial issues.

Po-Lo Matrix

The PO-LO matrix has not been populated yet.