E0420 Data Analysis in Biomedical and Environmental Sciences I

Přírodovědecká fakulta
podzim 2024
1/2/0. 2 kr. (plus ukončení). Ukončení: zk.
Mgr. Gabriela Kšiňanová, Ph.D. (přednášející)
Mgr. Albert Kšiňan, Ph.D. (přednášející)
Mgr. Andrea Dalecká, Ph.D. (přednášející)
Mgr. Hynek Pikhart, Ph.D., M.Sc. (přednášející)
prof. RNDr. Jana Klánová, Ph.D.
RECETOX – Přírodovědecká fakulta
Kontaktní osoba: Mgr. Albert Kšiňan, Ph.D.
Dodavatelské pracoviště: RECETOX – Přírodovědecká fakulta
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.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
Cíle předmětu
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).
Výstupy z učení
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
  • Lectures
  • 1. Introduction – content overview, grading, assignments
  • 2. Descriptive statistics, data cleaning, missing data, outliers
  • 3. Chi-square
  • 4. Correlation
  • 5. T test
  • 6. ANOVA
  • 7. General linear model, ANCOVA
  • 8. Simple linear regression – general linear model family, predictor, outcome, covariate, slope
  • 9. Multiple linear regression, hierarchical regression
  • 10. Logistic regression and Poisson regression
  • 11. Variable transformation and dummy variables
  • 12. Interactions
  • 13. Effect size
  • Practical sessions
  • 1. Introduction to SPSS – software overview, importing data files, saving data files, syntax, types of variables, labels
  • 2. Basic descriptive statistics – obtaining frequencies, sum, mean, standard deviation, histogram, boxplot, scatterplot confidence intervals, data cleaning, outliers
  • 3. Contigency tables, estimating chi-square
  • 4. Correlations, issues with correlations
  • 5. Comparing means, estimating t tests
  • 6. Comparing multiple means, estimating ANOVA
  • 7. ANCOVA
  • 8. Comparing estimates from different inferential statistics, estimating simple regression
  • 9. Estimating hierarchical linear regression
  • 10. Estimating logistic regression
  • 11. Computing transformations, creating dummy variables
  • 12. Computing and interpeting interaction effects
  • 13. Obtaining effect sizes (Cohen’s d, Cohen’s f, R2, beta, PAF)
    povinná literatura
  • 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
Výukové metody
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.
Metody hodnocení
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. There will be opportunities for earning extra credit throughout the semester.
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%).
Vyučovací jazyk
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Předmět je vyučován každoročně.
Výuka probíhá každý týden.
Předmět je zařazen také v obdobích podzim 2021, podzim 2022.