SubjectsSubjects(version: 861)
Course, academic year 2012/2013
  
Statistics in SPSS - JSM406
Title: Statistics in SPSS
Guaranteed by: Department of Sociology (23-KS)
Faculty: Faculty of Social Sciences
Actual: from 2012 to 2012
Semester: both
Points: 6
E-Credits: 6
Hours per week, examination: 1/1 Ex [hours/week]
Capacity: winter:unknown / unlimited (28)
summer:unknown / unknown (28)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
you can enroll for the course in winter and in summer semester
Guarantor: PhDr. Ing. Petr Soukup, Ph.D.
Mgr. Jan Schubert
Teacher(s): Mgr. Jan Schubert
PhDr. Ing. Petr Soukup, Ph.D.
P//Is pre-requisite for: JSM503
Annotation
Last update: 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).
Literature
Last update: 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

Teaching methods
Last update: 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.

Requirements to the exam
Last update: 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)
Syllabus
Last update: 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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