Course Objectives:
This course delves into the practical aspects of applied statistics,
encompassing the formulation of research questions, hypothesis
generation, research design, and data analysis. Students will
acquire hands-on experience utilizing statistical software and the
correct application of various statistical tests. Furthermore, the
course emphasizes the significance of effectively communicating
research findings to diverse audiences, equipping students with
the necessary skills to present their results coherently.
By the conclusion of the course, students are expected to
independently design and execute experiments, analyze the
gathered data using suitable statistical
methodologies, and proficiently convey their findings to a
scientific audience. This fosters an active engagement with the
course material and encourages participation in discussions and
group tasks. In addition to attending lectures and seminars,
students are required to complete a data analysis project,
culminating in an oral seminar presentation. This project offers
students the opportunity to apply their acquired data analysis
skills to a real-world problem in the context of social science
research.
To successfully complete the course, students must obtain at least
70% of their grade through various class activities, including
midterm exams, oral presentations, and the seminar project. This
ensures consistent engagement with the course content and
promotes the ongoing development of mastery of the skills and
concepts explored throughout the course.
Course Content:
Foundations of Applied Statistics: Introduction to the key concepts
and principles of applied statistics in contemporary research
contexts.
Statistical Programming Essentials: Familiarization with
programming language for statistical analysis, including basic
syntax and functionality.
Statistical Concepts Refresher: A review of essential statistical
concepts, including probability theory, distributions, hypothesis
testing, and parametric and nonparametric methods.
Exploratory Data Analysis Techniques: In-depth exploration of
data visualization, descriptive statistics, and methods for detecting
patterns and relationships in data sets.
Confirmatory Data Analysis Approaches: A comprehensive study
of inferential statistical techniques, such as regression analysis,
ANOVA, and hypothesis testing, for validating research
hypotheses.
Empirical Research Project: Application of acquired statistical
knowledge and skills in designing, conducting, and analyzing a
real-world research problem, culminating in an oral seminar
presentation.