PředmětyPředměty(verze: 861)
Předmět, akademický rok 2012/2013
  
Statistics in SPSS - JSM406
Anglický název: Statistics in SPSS
Zajišťuje: Katedra sociologie (23-KS)
Fakulta: Fakulta sociálních věd
Platnost: od 2012 do 2012
Semestr: oba
Body: 6
E-Kredity: 6
Rozsah, examinace: 1/1 Zk [hodiny/týden]
Počet míst: zimní:neurčen / neomezen (28)
letní:neurčen / neurčen (28)
Minimální obsazenost: neomezen
Stav předmětu: vyučován
Jazyk výuky: angličtina
Způsob výuky: prezenční
Poznámka: předmět je možno zapsat mimo plán
povolen pro zápis po webu
předmět lze zapsat v ZS i LS
Garant: PhDr. Ing. Petr Soukup, Ph.D.
Mgr. Jan Schubert
Vyučující: Mgr. Jan Schubert
PhDr. Ing. Petr Soukup, Ph.D.
P//Je prerekvizitou pro: JSM503
Anotace - angličtina
Poslední úprava: Mgr. Ivan Petrúšek (08.02.2016)
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).
Literatura - angličtina
Poslední úprava: Mgr. Ivan Petrúšek (06.02.2020)

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.

Czech students: Mareš, Rabušic, Soukup. Analýza sociálněvědních dat (nejen) v SPSS. 2015. muniPRESS, Brno. (ch. 2 - ch. 10)

Elektronic textbook: StatSoft, Inc. (2004). Electronic Statistics Textbook. Tulsa, OK: StatSoft.
http://www.statsoft.com/Textbook

Metody výuky - angličtina
Poslední úprava: Mgr. Ivan Petrúšek (06.02.2020)

Classes: Tuesday 14:00 - 15:20, Jinonice, computer lab 2074.

The classes are a combination of lectures and seminars. The first part (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.

Požadavky ke zkoušce - angličtina
Poslední úprava: Mgr. Ivan Petrúšek (06.02.2020)

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

Deadline for homework assignments: Monday (11:59 pm) before the next class (via email: ivan.petrusek@fsv.cuni.cz).

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
< 50 points = not passed (F)
Sylabus - angličtina
Poslední úprava: Mgr. Ivan Petrúšek (06.02.2020)

1. Course overview. Introduction to SPSS environment. Data entry exercise.

2. Descriptive vs inferential statistics. Levels of measurement. Transforming variables in SPSS (recoding variables, computing variables). Missing values definitions. Frequency tables and descriptive statistics.

3. Introduction to probability and probability distributions. Normal distribution. Standardized scores. Exploring cardinal data with graphs (histogram, boxplot).

4. Sampling variation. Central limit theorem. Confidence intervals (for a mean).

5. Statistical hypotheses testing framework (null/alternative hypothesis, p-value, type I/II error). One-sample t-test.

6. Independent-samples t-test. Paired-samples t-test.

7. Exploring assumptions of parametric tests. Assumption of normality.

8. Analysis of variance (within- and between-group variability, F-test, multiple comparisons using post-hoc tests).

9. Correlation analysis (covariance, Pearson and Spearman correlation coefficient). Scatterplots.

10. Linear regression (method of least squares, simple/multiple regression). Interpretation of slope and intercept.

11. Analysis of categorical data I (confidence interval for a proportion, crosstabs).

12. Analysis of categorical data II (chi-square test of independence, contingency coefficients, adjusted residuals).

13. Review session.

 
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