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Course, academic year 2023/2024
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Basic Statistics - APS100060
Title: Základy statistiky
Guaranteed by: Department of Psychology (21-KPS)
Faculty: Faculty of Arts
Actual: from 2023
Semester: winter
Points: 0
E-Credits: 6
Examination process: winter s.:combined
Hours per week, examination: winter s.:2/2, Ex [HT]
Capacity: 55 / 55 (unknown)
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
Key competences:  
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level: basic
Additional information: https://dl1.cuni.cz/course/view.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. Karolína Vlčková, Ph.D.
Teacher(s): Mgr. Jana Dlouhá
Bc. Lucie Zernerová, M.Sc., Ph.D.
Incompatibility : APS100041
Interchangeability : APS100006, APS100041
Is incompatible with: APS100060E
Is pre-requisite for: APS100020
Is interchangeable with: APS100041
Annotation -
Last update: Mgr. Jana Dlouhá (12.07.2023)
The course "Fundamentals of Statistics" introduces students to basic statistical concepts and terms with an emphasis on understanding the difference between descriptive and inductive statistics. Students will learn the principles of significance testing and null hypotheses, as well as apply the most common basic statistical techniques in the analysis of empirical data in the field of psychology. The course is supported by the e-learning platform for data analysis.
Aim of the course -
Last update: Mgr. Jana Dlouhá (12.07.2023)

The aim of the course "Basics of Statistics" is for students to acquire the fundamental principles and methods of statistical analysis of empirical data in various areas of psychology. Students should gain the skills necessary for correct interpretation of the results of statistical analyses and be able to present these results in accordance with APA format.

Note: This course does not aim to cover data analysis methods necessary for evaluating more complex data situations or to optimize the application of statistical methods depending on the specific data situation. These advanced aspects of statistical analysis are the main focus of the course in the subsequent master's study.

Acquired knowledge:

Upon completion of the course, students will be able to:

  1. Describe the basic methods of descriptive statistics.

  2. Explain the concept of random variables and statistics as a function of sample data.

  3. Understand the meaning and basic principles of inductive statistics, including interval estimation and hypothesis testing.

  4. Know the basic parametric tests (t-tests and chi-square tests, significance tests of correlation coefficients), their assumptions, and situations suitable for their application.

Acquired skills:

Upon completion of the course, students will be able to:

  1. Calculate adequate descriptive statistical characteristics of a data set using a calculator, spreadsheet editor, or appropriate statistical software.

  2. Apply an appropriate statistical technique to verify simple research hypotheses depending on the data situation (design and type of variables).

  3. Interpret the results of basic statistical analyses in professional literature.

 

 
Literature -
Last update: Mgr. Jana Dlouhá (12.07.2023)

Elementary (choose one):


(just choose one)



Field, A. (2009). Discovering statistics using SPSS. Sage Publications.

Hendl, J. (2004). Přehled statistických metod zpracování dat. Portál.

Mareš, P., Rabušic, L., & Soukup, P. (2015). Analýza sociálněvědních dat (nejen) v SPSS. Masarykova Univerzita.

Navarro, D. J., & Foxcroft, D. R. (n.d.). Learning statistics with jams. https://davidfoxcroft.github.io/lsj-book/learning-statistics-with-jamovi.pdf


Recommended (to be continually updated):
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. SAGE Publications.
Teaching methods -
Last update: Mgr. Jana Dlouhá (02.10.2023)

  • lectures

  • seminars (using jamovi software)

  • home-study

  • statistical excersises

Students will be using their own laptops during the instruction. Please install the jamovi Desktop software in advance (note, not jamovi cloud!), which is freely available for all common OS on the website https://www.jamovi.org/download.html. Please choose the latest solid version (suitable for most users). Currently, it is version 2.3.28 (Last updated on 2.10.2023).

If you have not previously worked with similar software, I recommend at least casually familiarizing yourself with the documentation available in English here https://docs.jamovi.org/.

Due to the number of students and the time constraints of the course, it will not be possible to address software installation and technical problems during lectures. If you are unable to install the software even after utilizing all other available information resources, please contact the instructor well in advance of the lecture.
Requirements to the exam -
Last update: Mgr. Jana Dlouhá (12.07.2023)

Exam Format:

The examination consists of two parts:

  1. Practical Part: In this section, students are expected to complete assigned exercises independently within specified deadlines throughout the semester. Successful completion of these exercises is a prerequisite for the certification.

  2. Oral Part: This part of the exam assesses the student's knowledge and understanding of the principles of basic statistical methods, as defined in the course syllabus.

Requirements for Passing the Course:

In order to successfully pass this course, students need to demonstrate:

  • A clear understanding and knowledge of the principles of basic statistical methods as outlined in the syllabus.

  • The ability to apply these statistical methods in practice.

Exam Schedule:

Exam dates are scheduled during the exam period that follows the semester in which the course is taught. If students express interest in additional exam dates, further sessions will be scheduled during the summer semester.
Syllabus -
Last update: Mgr. Jana Dlouhá (12.07.2023)

The course content serves as the requirements for the exam:

  1. Introduction to the Subject Matter:

    • Role of mathematical statistics in psychology

    • Basic statistical concepts

    • Issues with quantification in psychology

    • Types of scales

  2. Descriptive (Descriptive) Statistics Methods:

    • Graphical presentation

    • Quantiles

    • Basic statistical characteristics

    • Standard scores (z-transformation, standard scales used in psychology)

    • Concept of statistical dependence

    • Pearson's correlation coefficient

    • Spearman's correlation coefficient

    • Other correlation coefficients

    • Significance tests of correlation coefficients

  3. Inductive Statistical Methods:

    • Principles of statistical induction: population (basic set) and sample

    • Point and interval estimation of the population mean - confidence interval

    • Testing statistical hypotheses: null and alternative hypotheses - test characteristic - significance level - one-sided and two-sided test - p-value - z-test - statistical power

    • Some basic statistical tests:

      • One-sample t-test

      • Two-sample t-test

      • Paired t-test (for dependent samples)

      • Their nonparametric equivalents

      • Linear regression

      • Chi-square goodness of fit test

      • Chi-square test of independence

      • McNemar's symmetry test

      • Fisher's exact test

      • Analysis of Variance (ANOVA)

 

c) Testing of statistical hypotheses: null and alternative hypothesis - test characteristics - level of significance - one-sided and double-sided test - p-value - z-test - statistical power.

 d) Some basic statistical tests: one-sampling t-test - two-samples t-test - paired t-test (for dependent samples) - linear regression - chi-square test of good fit - chi-square test of independence - Mc-Nemar test of symmetry - Fischer exact test - basics of non-parametric tests and ANOVA
 
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