Overview
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###### Course Contents

Statistics in Education Program: PhD Education Credit Hours: 03 Edu-711 Course description The course will introduce students to the most commonly used methods in educational and social science research. The course will provide hands on practice for all statistical methods on SPSS for exact calculation of data. It will provide theoretical background for statistical tests of significance. Course Outcomes Upon successful completion of this course, the students will be able to complete the following tasks: 1. Explain the probability theory, the foundation of statistical methods. 2. Analyze the distributions of random variables as well as their properties. 3. Enter data and apply correlation and linear regression. 4. Carry out the statistical analysis using both hand calculation and computer software (SPSS). 5. Test hypotheses applying probability theory. 6. Justify the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions. 7. Interpret the outcomes of an analysis and make a decision based on the statistical results. Teaching Strategies Computer assisted learning. Lecture, Project method, Assignments, Collaborative learning, Presentations • Guidance- Counseling Service Participants are free to discuss the progress of their courses any time they want. However, to avoid inconvenience setting up of appointment is recommended. Course Plan Statistics in Education Units Content Week 1-3 An introduction to SPSS Use of data editor. Some other basic concepts of SPSS e.g. Data manipulation in SPSS, Options under DATA and transform menu, different functions, missing values, compute command Week 4-5 Graphical representations on SPSS Week 6-8 Descriptive Statistics Frequencies and Descriptive compounds ,Measures of Central Tendency (Mean, Median, Mode),Measures of Dispersion (Range, standard deviation, Variance) Week 9 Mid term Week 10-11 Inferential Statistics Testing: t-distribution (One sample,2 sample),ANOVA Week 12-14 Categorical Data analysis Chi-Square( test for nominal, ordinal and interval scale data) Week 15 Regression and Correlation Simple linear regression, simple linear correlation, multiple linear regression, Diagnostic checking Week 16 Revision, Final Exam Assessment: Class participation: 5 marks Attendance: 5 marks Two Presentations: 10 marks Two Assignments: 10 marks Mid term: 30 marks Final term: 30 marks Project: 10 marks Total marks: 100 Recommended Readings and websites: • J.O. Berger, Statistical Decision Theory and Bayesian Analysis. • P. Bickel and K. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics. • M.H. DeGroot, Optimal Statistical Decisions. • A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian Data Analysis. (2nd edition) • D.V. Lindley, Introduction to Probability and Statistics, from a Bayesian viewpoint (2 Vols). • S.J. Press, Applied Multivariate Analysis (from Bayesian and Frequentist Perspectives). • A. O'Hagan, Kendall's Advanced Theory of Statistics, Vol 2b: Bayesian Inference.

###### Course Synopsis

The course will introduce students to the most commonly used methods in educational and social science research. The course will provide hands on practice for all statistical methods on SPSS for exact calculation of data. It will provide theoretical background for statistical tests of significance.

###### Course Learning Outcomes

Upon successful completion of this course, the students will be able to complete the following tasks: 1. Explain the probability theory, the foundation of statistical methods. 2. Analyze the distributions of random variables as well as their properties. 3. Enter data and apply correlation and linear regression. 4. Carry out the statistical analysis using both hand calculation and computer software (SPSS). 5. Test hypotheses applying probability theory. 6. Justify the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions. 7. Interpret the outcomes of an analysis and make a decision based on the statistical results.

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