E0420 Data Analysis in Biomedical and Environmental Sciences I

Přírodovědecká fakulta
podzim 2022
Rozsah
1/1/0. 2 kr. (plus ukončení). Ukončení: zk.
Vyučující
Mgr. Albert Kšiňan, Ph.D. (přednášející)
Mgr. Gabriela Kšiňanová, Ph.D. (přednášející)
Mgr. Hynek Pikhart, Ph.D., M.Sc. (přednášející)
Mgr. Andrea Dalecká, Ph.D. (cvičící)
Consuelo Quispe Haro (cvičící)
Garance
prof. RNDr. Jana Klánová, Ph.D.
RECETOX – Přírodovědecká fakulta
Kontaktní osoba: Mgr. Albert Kšiňan, Ph.D.
Dodavatelské pracoviště: RECETOX – Přírodovědecká fakulta
Rozvrh
St 9:00–10:50 D29/347-RCX2
Předpoklady
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 (SEM, 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
Osnova
  • Lectures
  • 1. Introduction – content overview, grading, assignments
  • 2. Descriptive statistics, data cleaning, missing data, outliers
  • 3. Chi-square, T-test, ANOVA
  • 4. Correlation
  • 5. Issues with associations – linear and nonlinear relationships, causality, confounding, spuriousness
  • 6. Simple linear regression – general linear model family, predictor, outcome, covariate, slope
  • 7. Multivariate regression, hierarchical regression
  • 8. Logistic regression
  • 9. Transformations, dummy variables
  • 10. Mediation
  • 11. Moderation
  • 12. Effect size – different types of effect sizes, interpretation
  • 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. Estimating correlations
  • 4. Issues with correlations
  • 5. Estimating chi-square, t-test, ANOVA
  • 6. Comparing estimates from different inferential statistics, estimating simple regression
  • 7. Multivariate regression
  • 8. Logistic regression
  • 9. Computing transformations, creating dummy variables
  • 10. PROCESS macro for working with mediation
  • 11. PROCESS macro – moderation, simple slopes and the region of significance
  • 12. Obtaining effect sizes (Cohen’s d, Cohen’s f, R2, beta, PAF)
Literatura
    povinná literatura
  • 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 the practical session, and will be asked to submit it after each such session to receive attendance and activity points. Additionally, there will be three quizzes throughout the semester (multiple choice format). The quizzes will not be cumulative. Lastly, there will be no final exam. Instead, students will submit an assignment, which will consist of statistical analysis and its write-up using their own data or data provided by the instructors if necessary. There will be opportunities for earning extra credit throughout the semester.
The grading is broken down as follows:

Attendance and activity (15% of grade)
Quiz 1 (15% of grade)
Quiz 2 (15% of grade)
Quiz 3 (15% of grade)
Final assignment (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
Angličtina
Navazující předměty
Další komentáře
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2021.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/sci/podzim2022/E0420