course can be enrolled in outside the study plan enabled for web enrollment priority enrollment if the course is part of the study plan you can enroll for the course in winter and in summer semester
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
The course will introduce students to the basic data analysis methods used in quantitative social science research. As this is an introductory course, no previous knowledge of statistics is required. Students will learn and practice basic statistical methods by analyzing sociological survey data in a licenced software called IBM SPSS (each registered student will be provided a licence from the Faculty). After taking this course, students should be able to prepare a data set, perform common data management tasks and analyze quantitative data using basic statistical techniques. This introductory data analysis course is recommended to students of Erasmus+ and other foreign exchange programs.
Aim of the course
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
The main objective of this course is to introduce the key statistical theory and teach practical skills in quantitative data analysis. Students will learn the IBM SPSS software environment by editing and analyzing an established questionnaire survey dataset. Hence, the students will learn the basics of secondary data analysis (i.e. basic data management tasks such as creating new variables or subsetting the dataset based on specified conditions, computing descriptive statistics, preparing elementary data visualizations, and making inferences from sample data). This course will prepare students to employ the essential quantitative methods in their research projects and attend follow-up intermediate statistics courses.
Literature
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
Required reading:
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Fourth edition. London: Sage.
(detailed reading assignment from the course textbook will be specified after each class)
Recommended reading:
Agresti, A. (2018). Statistical Methods for the Social Sciences (5th Edition). Pearson.
Wheelan, Ch. (2013). Naked Statistics: Stripping the Dread from the Data. W. W. Norton.
Teaching methods
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
The classes are a combination of lectures and seminars. The first part (approximately 40 minutes) is a lecture during which the tutor introduces key concepts in statistical theory and quantitative data analysis methods (see syllabus below). The second part (approx. 40 minutes) is a seminar where students apply the methods introduced during the lecture in the data analysis software (IBM SPSS).
Requirements to the exam
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
Grading will be based on homework assignments (6 mandatory assignments, each worth 5 points) and a final in-class exam (worth 70 points). Students may earn up to 100 total points.
Grading:
91 - 100 points = grade A
81 - 90 points = grade B
71 - 80 points = grade C
61 - 70 points = grade D
51 - 60 points = grade E
0 - 50 points = not passed (grade F)
NOTE: Total points earned will be rounded to the whole number (e.g., the overall result of 50.5 points is rounded to 51 points, which corresponds to the grade E).
Syllabus
Last update: Mgr. Ivan Petrúšek, Ph.D. (30.01.2024)
Course Schedule
Week 1: Course overview. Introduction to the software environment. Week 2: Descriptive vs inferential statistics. Levels of measurement. Week 3: Introduction to probability and probability distributions. Week 4: Sampling variation. Central limit theorem. Confidence intervals (for the mean). Week 5: Statistical hypotheses testing framework. One-sample t-test. Week 6: Independent-samples t-test. Paired-samples t-test. Week 7: Exploring assumptions of parametric tests. Assumption of normality. Week 8: Analysis of variance (within- and between-group variability, F-test, post-hoc tests). Week 9: Correlation analysis (Covariance, Pearson and Spearman correlation coefficients, Scatterplot). Week 10: Linear regression (method of least squares, simple/multiple regression). Week 11: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs). Week 12: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals). Week 13: Review session.
