sap course 1434120298

Course Code & Number:

MATH 233

Course Title:

Statistics for Social Sciences

Level of Course:

BS

Credits:

(2+0+2) 3 TEDU Credits, 5 ECTS Credits

Catalog Description:

Basic concepts in statistics. Organizing data. Descriptive statistics. Probability calculations and distributions. Practices in statistic package programs. Testing hypotheses. Correlation, regression, variation analyses.

Pre-requisites & Co-requisites:

Pre-requisites: NONE
Co-requisites: NONE
Grading: 

Final Exam - 40% 
Quiz - 10% 
Lab Work - 30% 
Writing assignments (Oral, Poster) - 20%

Year of Study: 
Sophomore
Semester: 
Fall
Mode of Delivery: 
Face-to-face
Language of Instruction: 
English
Course Type: 
Compulsary
Required Reading: 
1. Sirkin, R. M. (2006). Statistics for the social sciences (3rd ed.). Thousand Oaks, CA:Sage.
Extended Description: 

Introduction to Statistics for Social Sciences. How statistical analysis is used in Social Sciences? Testing hypotheses. Levels of measurement and forms of data. Defining variables. Gathering the data. Operational definitions. Validity. Reliability. Measuring central tendency. Measuring dispersion. Constructing and interpreting contingency tables. Statistical inference and tests of significance. What is statistical inference? Probability distributions and one-sample z and t tests . Two-sample t tests. One-way analysis of variance. Measuring association in contingency tables. The chi-square test . Additional aspects of correlation and regression analysis. Partial correlations and causal models. Multiple correlation and the coefficient of multiple determination . Multiple regression. The standardized partial regression slope. Using a regression printout . Stepwise multiple regression. Computer applications. Emphasizes social science examples and cases.

Learning Outcomes: 

Upon succesful completion of this course, a student will be able to
1. Explain the fundamental statistical concepts covered throughout the course.
2. Use graphical and numerical methods to summarize data.
3. Calculate the probabilities of events under natural assumptions on the population.
4. Analyze data sets to make inference about characteristics of a population.
5. Apply statistical techniques in problems of interest and obtain useful conclusions.
6. Use SPSS program to analyze data sets

Planned Learning Activities and Teaching Methods: 
Telling/Explaining
Discussion/Debate
Questioning
Reading
Peer Teaching
Inquiry
Collaborating
Oral Presentation
Web Searching
Assessment Methods and Criteria: 
Test / Exam
Labrotary Work

Student Workload:

Debate
10
hrs
Observation
5
hrs
Midterm Exam 1
1
hrs
Midterm Exam 2
1
hrs
Final Exam
2
hrs
Research Review
15
hrs
Report on a Topic
18
hrs
Case Study Analysis
20
hrs
Others
20
hrs

Prepared By:

Revised By:

teduadmin