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Course, academic year 2024/2025
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Statistics 2 - APS100078
Title: Statistika 2
Guaranteed by: Department of Psychology (21-KPS)
Faculty: Faculty of Arts
Actual: from 2024
Semester: summer
Points: 0
E-Credits: 4
Examination process: summer s.:combined
Hours per week, examination: summer s.:1/1, Ex [HT]
Capacity: unlimited / unknown (unknown)
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
Key competences: critical thinking, data literacy
State of the course: taught
Language: Czech
Teaching methods: full-time
Level:  
Additional information: https://dl1.cuni.cz/enrol/index.php?id=15106
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
Guarantor: Mgr. et Mgr. Filip Děchtěrenko, Ph.D.
Mgr. Jana Dlouhá, Ph.D.
Bc. Lucie Zernerová, M.Sc., Ph.D.
Teacher(s): Mgr. Jana Dlouhá, Ph.D.
Mgr. Martin Máčel
Bc. Lucie Zernerová, M.Sc., Ph.D.
Pre-requisite : APS100077
Annotation -
The course Statistics 2 builds on Statistics 1 (APS100077) and further develops students' knowledge in the field of statistical data analysis. This course focuses on more advanced statistical methods such as regression analysis, non-parametric tests, and analysis of variance (ANOVA) and their applications in psychology. Students will use Jamovi software for practical analyses of real-world data.

This course is an important preparation for the follow-up master's course Data Statistical Analysis (APS500009), which expands statistical knowledge to an advanced level.
Last update: Dlouhá Jana, Mgr., Ph.D. (20.09.2024)
Aim of the course -

Course Content

In the course **Statistics 2**, students will learn more advanced statistical techniques that build on their knowledge from **Statistics 1**. This course focuses on the application of these methods in real research situations, with students using Jamovi software. The course concludes with an exam based on the following topics:

1. Regression Methods

  • Simple and multiple linear regression
  • Regression model diagnostics and detection of outliers
  • Non-parametric regression analysis
  • Practical application of regression methods in Jamovi

2. Non-parametric Tests

  • McNemar's test for symmetry
  • Fisher's exact test
  • Tests suitable for ordinal data: Mann-Whitney U-test, Wilcoxon test
  • Practical application of non-parametric tests in Jamovi

3. Analysis of Variance (ANOVA)

  • One-way and multi-way ANOVA
  • Testing interactions between factors
  • Post-hoc tests and their interpretation
  • Practical application of ANOVA in Jamovi

4. Practical Application of Statistical Methods

  • Applying advanced methods to real-world datasets
  • Interpreting the results of statistical analyses
  • Using outputs from Jamovi in academic research
Last update: Dlouhá Jana, Mgr., Ph.D. (20.09.2024)
Course completion requirements -

Knowledge of the principles and applications of selected mathematical and statistical methods covered in the course. Students will demonstrate their understanding during the exam, which consists of a practical and an oral part. Each part contributes equally (50%) to the final grade.

Last update: Dlouhá Jana, Mgr., Ph.D. (06.02.2025)
Literature -

Required Reading:

(Choose one of the following resources that best suits your needs for working with data and statistical analysis)

  • Field, A. (2009). Discovering statistics using SPSS. Sage Publications.
    A classic textbook for learning statistics using SPSS, ideal for students who prefer this tool.
  • Hendl, J. (2004). Přehled statistických metod zpracování dat. Portál.
    A comprehensive overview of statistical methods, written in Czech, offering a clear connection between theoretical and practical statistical analysis.
  • Mareš, P., Rabušic, L., & Soukup, P. (2015). Analýza sociálněvědních dat (nejen) v SPSS. Masarykova Univerzita.
    Focused on data analysis in social sciences, ideal for those planning to analyze data in this field using SPSS.
  • Navarro, D. J., & Foxcroft, D. R. (n.d.). Learning statistics with jamovi.
    https://davidfoxcroft.github.io/lsj-book/learning-statistics-with-jamovi.pdf
    This resource is perfect for students who will be using Jamovi – the software recommended for lectures and practical exercises.

Recommended Reading (to be updated throughout the course):

  • Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. SAGE Publications.
    Excellent for students who prefer using R for statistical analyses. It also allows comparing different statistical methods across multiple software platforms.
Last update: Dlouhá Jana, Mgr., Ph.D. (20.09.2024)
Teaching methods -

The course will utilize a combination of different teaching methods to ensure effective learning of both theoretical and practical knowledge:

  • Lectures: Provide students with the necessary theoretical foundation of statistical methods and concepts.

  • Exercises: Practical applications of statistical methods, particularly using Jamovi software.

  • Self-study: An essential part of the course, allowing students to deepen their knowledge through studying recommended literature and solving assignments independently.

  • Individual Statistical Tasks: Students will have the opportunity to solve specific statistical problems based on real datasets.

Note: Students will use their own laptops during the course. It is necessary to install the Jamovi Desktop software (note: not Jamovi cloud!) beforehand, which is available free of charge for all major operating systems at https://www.jamovi.org/download.html. Please install the latest version (current - latest features).

If you are unfamiliar with this software, it is recommended to review the documentation available in English at https://docs.jamovi.org/.

Due to the number of students and time constraints, it will not be possible to address software installation and technical issues during the lectures. If you encounter difficulties installing the software, please contact the instructor well in advance of the class.

Last update: Dlouhá Jana, Mgr., Ph.D. (04.10.2024)
Other recommended courses -

Basic R (APS300426 / APS300426E)

For students looking to expand their skills in statistical analysis, we recommend the course Basic R. This course is designed for beginners and focuses on the fundamentals of the R programming language, which is widely used in modern statistics. R offers vast possibilities for advanced data processing, graph creation, and analysis, significantly broadening your statistical capabilities.

The course will be taught in English and is open to Erasmus students (course code APS300426E).

Why should you enroll in this course?

  • You will learn the basics of programming in R, one of the most significant tools for statistical data analysis.
  • You will expand your skills beyond what you have learned in your statistics courses – R allows you to analyze more complex datasets.
  • You will interact with students from various countries, as the course is held together with Erasmus students.
Last update: Dlouhá Jana, Mgr., Ph.D. (20.09.2024)
Syllabus -

Course Content

In the course **Statistics 2**, students will learn more advanced statistical techniques that build on their knowledge from **Statistics 1** (APS100077). The course focuses on the application of these methods in real research scenarios, with students using Jamovi software for data analysis. The course concludes with an exam based on the following topics:

1. Regression Methods

  • Simple and multiple linear regression
  • Regression model diagnostics and outlier detection
  • Logistic regression
  • Non-parametric regression analysis
  • Practical application of regression methods in Jamovi

2. Non-parametric Tests

  • McNemar's test for symmetry
  • Fisher's exact test
  • Tests for ordinal data: Mann-Whitney U-test, Wilcoxon test
  • Kruskal-Wallis test
  • Practical application of non-parametric tests in Jamovi

3. Analysis of Variance (ANOVA)

  • One-way and multi-way ANOVA
  • Testing interactions between factors
  • Post-hoc tests and their interpretation
  • Repeated measures in ANOVA
  • Practical application of ANOVA in Jamovi

4. Practical Application of Statistical Methods

  • Applying advanced methods to real-world datasets
  • Interpreting the results of statistical analyses
  • Using outputs from Jamovi for academic research
Last update: Dlouhá Jana, Mgr., Ph.D. (20.09.2024)
 
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