EDUCATIONAL TECHNOLOGY (TURKISH, NONTHESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CNG5003 Advanced Statistics 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: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. ARZU BUYRUK GENÇ
Recommended Optional Program Components: None
Course Objectives: The course topics will include a review of the statistical terms (location, dispersion, and shape), robustness, and bivariate relationships. An emphasis will be given to statistical and practical significance (effect sizes and confidence intervals), multiple regression analysis and other general linear model concepts. The course will also introduce some of the advanced topics, such as factor analysis and structural equation modeling depending on student readiness and interests.
Classes will be held in lecture format supported by question & answer sessions and collaborative problem solving through group work. For this, you may be asked to work in small groups with classmates. You will be given assignments to solve at home by yourself, and send your solutions to the instructor. Students will be actively participating in classroom discussions, presenting to the whole class, working in collaboration to conduct a research study, and disseminate the findings. Microsoft Excel, PASW (SPSS or STATA), and AMOS (or MPLUS) are the key software that will be used during class time or will be necessary to successfully complete the assignments.

Learning Outcomes

The students who have succeeded in this course;
By reading the materials and studying this course, you should acquire the competency
1) to interpret statistical terms,
2) to make sense out of (and set up) statistical tables and figures,
3) to know which specific research questions can be answered by each of a variety of statistical procedures,
4) to be aware of what can and cannot be accomplished when someone sets up and tests one or more null hypotheses,
5) to notice the misuse of statistics,
6) to distinguish between good and poor research designs.

Course Content

Demystifying APA and standards for educational and psychological testing, Introduction to educational statistics, Univariate statistics: Location, dispersion, and shape, The importance of sum of squares (SOS), Standardized score world (z-scores) and normal distributions, Bivariate relationships, correlation and causality, Simple regression, Introduction to hypothesis testing, Sampling distributions, Test statistics, Inferences concerning one or two means, One-way ANOVA, Practical significance: Effect sizes and confidence intervals, Power, Multiple Regression Analysis, Multiple Regression Analysis, β weights versus structural coefficients, Hierarchical Multiple Regression, Multiway ANOVA, ANOVA via Regression, Factor analysis, Confirmatory factor analysis, Structural equation modeling (SEM), Nonparametric tests

Weekly Detailed Course Contents

Week Subject Related Preparation
1) • Demystifying APA and standards for educational and psychological testing. • Introduction to educational statistics
2) • Univariate statistics: Location, dispersion, and shape
3) • The importance of sum of squares (SOS) • Standardized score world (z-scores) and normal distributions
4) • Bivariate relationships, correlation and causality • Simple regression
5) • Introduction to hypothesis testing • Sampling distributions
6) • Test statistics
7) • Inferences concerning one or two means
8) • One-way ANOVA
9) • Practical significance: Effect sizes and confidence intervals • Power
10) Multiple Regression Analysis
11) Multiple Regression Analysis
12) β weights versus structural coefficients, Hierarchical Multiple Regression
13) Multiway ANOVA, ANOVA via Regression, Factor analysis, Confirmatory factor analysis, Structural equation modeling (SEM), Nonparametric tests
14) • Final Review

Sources

Course Notes / Textbooks:
References: 1) Pagano, R. R. (2010). Understanding statistics in the behavioral sciences (9th ed.). Belmont, CA: Cengage.
2) Huck, S. W. (2012). Reading statistics and research (6th ed.). Boston, MA: Pearson.
Suggested texts:
3) Thompson, B. (2008). Foundations of behavioral statistics: An insight-based approach. New York: Guilford.
4) Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 3 % 60
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 8 112
Homework Assignments 3 15 45
Final 1 3 3
Total Workload 202

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) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. 2
2) To be able to comprehend the interdisciplinary interaction with which the field is related. 3
3) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. 3
4) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. 2
5) To be able to solve the problems encountered in the field by using research methods. 4
6) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. 4
7) To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary. 2
8) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. 3
9) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. 4
10) To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes. 3
11) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. 3
12) Being able to independently carry out a work that requires expertise in the field. 3
13) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. 3
14) Being able to lead in environments that require the resolution of problems related to the field. 3