BUSI 213 - Statistics for Social Science

Basic Information

Course Code:

BUSI 213

Course Name:

Statistics for Social Science

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

5.00

Instructor:

Doç. Dr. BÜŞRA SOUMMAKIE

Course Objectives

This course introduces the fundamental concepts and methods of statistics with particular emphasis on their applications in the social sciences. Students learn how to collect, organize, summarize, analyze, and interpret quantitative data. The course covers descriptive statistics, probability concepts, sampling, estimation, hypothesis testing, correlation, regression, and selected non-parametric methods. Emphasis is placed on practical interpretation of statistical results rather than mathematical derivations. Students are also introduced to statistical software and real-world datasets from social sciences.

Learning Outcomes

Code	Description
LO1	Explain basic statistical concepts and terminology used in social sciences.
LO2	Organize and present quantitative and qualitative data using appropriate tables and graphs.
LO3	Calculate and interpret measures of central tendency and dispersion.
LO4	Explain basic concepts of probability and probability distributions.
LO5	Explain sampling distributions and the principles of statistical estimation.
LO6	Formulate and test hypotheses using appropriate statistical procedures.
LO7	Conduct and interpret correlation and regression analyses.
LO8	Use statistical software to conduct basic statistical analyses.
LO9	Interpret statistical findings and communicate conclusions in the context of social science research.

Weekly Course Content

Week	Content
1	Introduction, Statistics in Business, etc. Basic Statistical Concepts. Using Ms. Excel.
2	Defining and Collecting Data
3	Case study: Clear Mountain State Student Surveys Using Excel.
4	Organizing and Visualizing Variables
5	Numerical Descriptive Measures: Central Tendency, Variation, and Shape. Using Excel
6	Case Study
7	Simple Linear Regression, Ch13, Types of Regression Models, Determining the Simple Linear Regression Equation
8	Midterm Exam
9	Simple Linear Regression,Types of Regression Models, Determining the Simple Linear Regression Equation
10	Inferences About the Slope and Correlation Coefficient, Excel
11	Introduction to Multiple Regression, Developing a Multiple Regression Model, Excel
12	Developing a Multiple Regression Model, Excel
13	Using Dummy Variables and Interaction Terms in Regression Models, Excel
14	Using Dummy Variables and Interaction Terms in Regression Models, Excel
15	Final Exam

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	3	14.00	42.00
Practice	2	14.00	28.00
Laboratory	1	12.00	12.00
Pre-Class Individual Study	1	14.00	14.00
Midterm Exam/Preparation	2	9.00	18.00
Final Exam/Preparation	2	12.00	24.00
Homework	4	3.00	12.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%30
2	Homework	%30
3	Quiz	%40
4	Final Exam	%0
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5	LO6	LO7	LO8	LO9
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11
PO-12

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Controversial Course	Listening and comprehension, critical thinking	Standard classroom technologies, multimedia tools, projector, computer, overhead projector
2	Special Support / Structural Examples	Pre-planned specific skills
3	Problem Solving	* Analyzing physical and physiological problems using problem-solving techniques and developing appropriate solutions.
4	Group Work	Students work in groups to prepare presentations.	Research and presentation
5	Homework	Solving and analyzing homework questions using a computer and Excel.	Computer
6	Review / Survey Work	Research – lifelong learning, writing, reading

Sustainable Development Goals