Statistics in SPSS - JSM406

• Anglický název: Statistics in SPSS
• Zajišťuje: Katedra sociologie (23-KS)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2021 do 2022
• Semestr: oba
• E-Kredity: 8
• Rozsah, examinace: 1/1 [hodiny/týden]
• Počet míst: zimní:neurčen / 40 (20); letní:neurčen / neurčen (20)
• Minimální obsazenost: neomezen
• Virtuální mobilita / počet míst: ne
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu; při zápisu přednost, je-li ve stud. plánu; předmět lze zapsat v ZS i LS
• Garant: PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Ivan Petrúšek
• Vyučující: Mgr. Ivan Petrúšek; PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Tereza Svobodová
• Třída: Courses for incoming students
• Je prerekvizitou pro: JSM503
• Je záměnnost pro: JSM513

Anotace

Poslední úprava: Mgr. Ivan Petrúšek (25.01.2022)

This introductory course of applied statistics is primarily recommended to students of Erasmus+ and other foreign exchange programs. The course is also recommended to Czech students from 3rd or higher grade (programs: Applied research and its methodology, Social and Public Policy). Students will learn and practice basic statistical methods by analyzing sociological survey data in a program called SPSS (Statistical Product and Service Solutions). As this is an introductory course, no previous knowledge of statistics is required.

Literatura

Poslední úprava: Mgr. Ivan Petrúšek (25.01.2022)

Mandatory:

Field, A. (2009). Discovering Statistics Using SPSS. Third edition. London: Sage.

(detailed reading assignment from the course textbook will be specified after each class; please view the files section for pdf of the textbook)

Recommended:

Norušis,M., J. (2005). SPSS 13.0 :statistical procedures companion. New Jersey: Prentice Hall.

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

Recommended book in Czech language: Mareš, P. - Rabušic, L. - Soukup, P. (2015): Analýza sociálněvědních dat (nejen) v SPSS. Brno: muniPRESS. (ch. 2 - ch. 10)

Metody výuky

Poslední úprava: Mgr. Ivan Petrúšek (22.02.2022)

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 SPSS environment. Institute of Sociological Studies will provide the enrolled students with the SPSS licence (so that they will have the software installed on their personal computers).

The course will be taught ONLINE via zoom in the summer semester. Online classes will be recorded and the videos will be available to enrolled students. To enter the online classes (on Tuesday between 17:00 and 18:20) please use the following zoom link: https://cuni-cz.zoom.us/j/91734503051?pwd=OU92K0RzbGdZSWU3czg0aVZMSXBldz09

Požadavky ke zkoušce

Poslední úprava: Mgr. Ivan Petrúšek (25.01.2022)

Grading will be based on homework assignments (7 mandatory assignments, each worth 5 points) and a final exam (worth 65 points). Students may earn up to 100 total points.

Deadline for homework assignments: Wednesday (11:59 pm) via email. 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).

Sylabus

Poslední úprava: Mgr. Ivan Petrúšek (25.01.2022)

Course Schedule

Week 1: Course overview. Introduction to SPSS environment.
Week 2: Descriptive vs inferential statistics. Levels of measurement.
Week 3: Introduction to probability and probability distributions.
Week 4: 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. Paired-samples t-test.
Week 7: Exploring assumptions of parametric tests. Assumption of normality.
Week 8: Analysis of variance (within- and between-group variability, F-test, post-hoc tests).
Week 9: Correlation analysis (Covariance, Pearson and Spearman correlation coefficients, Scatterplot).
Week 10: Linear regression (method of least squares, simple/multiple regression).
Week 11: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs).
Week 12: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals).
Week 13: Review session (preparation for the final exam).