Introduction to Statistics - JPB159

• Title: Introduction to Statistics
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2023
• Semester: both
• E-Credits: 6
• Hours per week, examination: 1/1, Ex [HT]
• Capacity: winter:25 / 25 (25); summer:unknown / unknown (25)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• 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; you can enroll for the course in winter and in summer semester
• Guarantor: PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Ivan Petrúšek, Ph.D.
• Teacher(s): Mgr. Ivan Petrúšek, Ph.D.; PhDr. Ing. Petr Soukup, Ph.D.
• Class: Courses not for incoming students

Annotation

Last update: PhDr. Ing. Petr Soukup, Ph.D. (19.02.2024)

Course in the summwer semester (from Februarz 2024) is only for PPE students.
All materials for the course will be availasble in SIS or Google disc:
https://drive.google.com/drive/folders/1u4wWngIPUR-Vf02dNQM-ctTIZ7WwAyPp?usp=sharing

This is a mandatory course for students of Politics, Philosophy and Economics programme. Students will learn and practice basic statistical methods by analyzing sociological survey data in a program called jamovi (freeware). As this is an introductory course, no previous knowledge of statistics is required.

Lecture is available also via MS Teams:https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTMwOWUyNWEtYWQ0OC00OTJhLWFiYjUtYjM4NzYzZTcyNDNj%40thread.v2/0?context=%7b%22Tid%22%3a%2273844aaf-f10c-4dee-aaaf-5eeb27962a5d%22%2c%22Oid%22%3a%2244019797-e6cf-458d-996e-9e9b298c7895%22%7d

Course completion requirements

Last update: PhDr. Ing. Petr Soukup, Ph.D. (18.09.2023)

Grading will be based on homework assignments (6 mandatory assignments, each worth 5 points) and a final exam (worth 70 points). Students may earn up to 100 total points.

Deadline for homework assignments: Monday (11:59 am) via email (assignments are submitted to Petr Soukup at: [e-mail]). In other words, students will have eight days to prepare and submit their homework assignments.

Grading:

• 91 - 100 points = grade A
• 81 - 90 points = grade B
• 71 - 80 points = grade C
• 61 - 70 points = grade D
• 51 - 60 points = grade E
• 0 - 50 points = not passed (grade F)

NOTE: Total points earned will be rounded to the whole number (e. g. the overall result of 50.5 points is rounded to 51 points and corresponds to the grade E).

Literature

Last update: PhDr. Ing. Petr Soukup, Ph.D. (18.09.2023)

Mandatory:

https://www.learnstatswithjamovi.com/

Recommended:

deVaus, D. (2002). Surveys in social research. London:Routledge - Taylor & Francis Group.

Teaching methods

Last update: PhDr. Ing. Petr Soukup, Ph.D. (05.10.2023)

The classes are a combination of lectures and seminars. The first part of each class (approx. 40 minutes) is a lecture during which the tutor introduces key concepts in statistical theory and methods of data analysis (see syllabus below). The second part (approx. 40 minutes) is a seminar where students apply the methods introduced during the lecture in the jamovi environment. This freeware can be dowloaded at: https://www.jamovi.org/download.html

The course will be taught in PC lab 229.  You can use your own computers as well.

Link for MS Teams:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_OGQxNjUwOWYtMTRiZS00NjFjLThkMDktNjUyYWRhZGQ2NDk0%40thread.v2/0?context=%7b%22Tid%22%3a%2273844aaf-f10c-4dee-aaaf-5eeb27962a5d%22%2c%22Oid%22%3a%2244019797-e6cf-458d-996e-9e9b298c7895%22%7d

 

Syllabus

Last update: PhDr. Ing. Petr Soukup, Ph.D. (18.09.2023)

Course Schedule

Week 1: Course overview. Introduction to jamovi environment.

Week 2: Data matrix. Data preparattion (recode, compute). Assigning of labels to variables.
Week 3: Descriptive statistics.
Week 4: Introduction to probability distributions. Sampling variation. Central limit theorem. Confidence intervals (for the mean).
Week 5: Statistical hypotheses testing framework. One-sample t-test.
Week 6: Independent-samples t-test. 
Week 7: Analysis of variance (within- and between-group variability, F-test, post-hoc tests).
Week 8: Correlation analysis (Pearson and Spearman correlation coefficients, Scatterplot).
Week 9: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs).
Week 10: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals).