Introduction to Data Analysis - JSM406

• Title: Introduction to Data Analysis
• Form of teaching: lecture+practicals
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2025
• Duration in semesters: 1
• Semester: both
• E-Credits: 8
• Hours per week, examination: 1/1, Ex [HT]
• Capacity: winter:unknown / 25 (15); summer:unknown / unknown (15)
• Maximum number of enrolled students: 15
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Repeated enrollment: 2 / 2 / 1 / 2
• Note: 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
• Guarantor: PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Ivan Petrúšek, Ph.D.
• Teacher(s): Mgr. Tereza Klegr, Ph.D.; Mgr. Ivan Petrúšek, Ph.D.; PhDr. Ing. Petr Soukup, Ph.D.
• Class: Courses for incoming students
• Is pre-requisite for: JSM503
• Is interchangeable with: JSM513

Annotation

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 Statistics (each registered student will be provided a licence from the Faculty of Social Sciences). 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.

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Aim of the course

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 Statistics 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 to a target population). This course will prepare students to employ the essential quantitative methods in their research projects and attend follow-up intermediate statistics courses. 

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Literature

Required reading:

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Edition). London: Sage.

(detailed reading assignment from the course textbook will be specified after each class, other editions of this textbook may also be used by students)

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.

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Teaching methods

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 the syllabus below). The second part (approximately 40 minutes) is a seminar where students apply the methods introduced during the lecture in the data analysis software (IBM SPSS Statistics). 

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Course assessment methods and requirements for successful completion, grading scheme

Grading will be based on homework assignments (6 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).

Use of AI in the Course

Students may use generative artificial intelligence tools as supportive assistants during their individual preparation. This is permitted only on the condition that they remain critical of the outputs, verify the information obtained, and explicitly state how artificial intelligence was used in their homework assignments. Generative AI tools may be used as a supplementary aid for conducting analyses for homework assignments, for problem-solving in IBM SPSS Statistics, or for editing homework assignment text. The use of generative artificial intelligence during the final exam is not allowed.

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Syllabus

Course Schedule:

Week 1: Course overview. Introduction to the IBM SPSS Statistics 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: Analysis of variance (within- and between-group variability, F-test, post-hoc tests).
Week 8: Correlation analysis (covariance, Pearson and Spearman correlation coefficients, scatterplot).
Week 9: Linear regression (method of least squares, simple/multiple regression).
Week 10: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs).
Week 11: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals).
Week 12: Review session.

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)

Learning outcomes

Upon successful completion of this course, students will be able to:

• Prepare and manage quantitative datasets, including creating new variables, recoding data, and subsetting cases.
• Understand and apply basic statistical concepts and inference techniques used in quantitative social science research.
• Compute descriptive statistics, produce simple data visualizations, and interpret analytical results.
• Utilise IBM SPSS Statistics effectively for data management and quantitative data analysis.
  
• Apply fundamental quantitative techniques in their own research and progress to intermediate statistics courses. 

Last update: Petrúšek Ivan, Mgr., Ph.D. (31.01.2026)