E0420 Data Analysis in Biomedical and Environmental Sciences I

Faculty of Science
Autumn 2025
Extent and Intensity
1/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Mgr. Gabriela Kšiňanová, Ph.D. (lecturer)
Mgr. Albert Kšiňan, Ph.D. (lecturer)
prof. Mgr. Hynek Pikhart, Ph.D., M.Sc. (lecturer)
Guaranteed by
prof. Mgr. Hynek Pikhart, Ph.D., M.Sc.
RECETOX – Faculty of Science
Contact Person: Mgr. Albert Kšiňan, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Fri 8:00–9:50 D29/347-RCX2
Prerequisites
Students should have some basic knowledge of statistics, i.e., ideally any previous Introduction to Statistics course. Students should be familiar with the following terms: sample, dataset, variable (continuous/ordinal/nominal; dependent/independent), research question, hypothesis testing, statistical significance, mean, mode, median, standard deviation, distribution.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The goal of this course is to teach students to perform basic descriptive and inferential statistical analyses. Students will learn to prepare a dataset, carry out essential statistical analyses using the SPSS software and interpret them. This course will be followed by E0430 in the spring semester, which will be focused on advanced methods of statistical analyses (HLM, analysis of longitudinal data). R scripts for each topic will be also available.
Learning outcomes
At the end of the course the student will be able to:
-work with datasets - create datasets, transform variables, identify problematic cases, clean data
-use IBM SPSS Statistics for data handling and data analysis
-create SPSS syntax files that are reproducible
-understand, conduct, and interpret common statistical inferential tests in SPSS
-report the results in a proper format
Syllabus
  • Lectures
  • 1. Introduction – content overview, grading, assignments
  • 2. Data preparation, data cleaning, missing data, creating variables
  • 3. Descriptive statistics, outliers, creating plots
  • 4. Chi-square
  • 5. T-test
  • 6. General linear model: ANOVA
  • 7. Recapitulation class
  • 8. Correlation
  • 9. Simple linear regression – general linear model family, predictor, outcome, covariate, slope
  • 10. Multiple linear regression, hierarchical regression
  • 11. Logistic regression
  • 12. Variable transformation and dummy variables
  • 13. Interactions
  • Practical sessions
  • 1. Introduction to SPSS – software overview, importing data files, saving data files, syntax, types of variables, labels
  • 2. Data preparation – creating variables via compute, recode, and do if commands, restructuring data, missing data
  • 3. Basic descriptive statistics – obtaining frequencies, sum, mean, standard deviation, histogram, boxplot, scatterplot confidence intervals, identifying outliers
  • 4. Contingency tables, estimating chi-square
  • 5. Estimating t-test
  • 6. Estimating ANOVA
  • 7. Recapitulation class
  • 8. Correlations, issues with correlations
  • 9. Estimating simple regression
  • 10. Estimating multiple linear and hierarchical regression
  • 11. Estimating logistic regression
  • 12. Computing transformations, creating dummy variables
  • 13. Computing and interpreting interaction effects
Literature
    required literature
  • FIELD, Andy P. Discovering statistics using IBM SPSS statistics. 5th edition. Los Angeles: Sage, 2018, xxix, 1070. ISBN 9781526419521. info
  • COHEN, Jacob. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. Mahwah: Lawrence Erlbaum Associates, 2003, xxviii, 70. ISBN 9780805822236. info
Teaching methods
The teaching format is an in-person lecture supported by PowerPoint presentations followed by a practicum in the computer lab. Students will use IBM SPSS statistical software to perform statistical analyses discussed in the lectures.
Assessment methods
Students will complete a practical data analysis exercise during practical session, and will be asked to submit it after each such session to receive attendance and activity points. Additionally, there will be two quizzes throughout the semester (multiple choice format). The quizzes will not be cumulative. Lastly, for the final assignment, students can choose between final project, which consists of statistical analysis and its write-up using their own data or data provided by the instructors if necessary, and final exam.
The grading is broken down as follows:

Attendance and activity (30% of grade)
Quiz 1 (15% of grade)
Quiz 2 (15% of grade)
Exam/ Final project (40% of grade)

This corresponds to the following grades: A (100%-92%), B (91%-84%), C (83%-76%), D (75%-68%), E (67%-60%), F (< 60%).
Language of instruction
English
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms autumn 2021, Autumn 2022.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2025/E0420