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Applied Statistical Software
Abbreviation: SOCDO245Load: 15(L) + 0(S) + 60(E) + 0(P) + 0(PE) + 0(ME) + 0(EE) + 0(FLE) + 0(PEE) + 0(FE) + 0(CP)
Lecturers in charge: doc. dr. sc. Dario Pavić
Lecturers: doc. dr. sc. Dario Pavić ( Exercises )
Course description: Course description
The aim of the course is to prepare students for using the SPSS statistical software system. This includes independent data entry, selection of appropriate statistical methods, their implementation and interpretation of the results.

e-learning level 1
english level 1

Competency
Improve the ability to apply knowledge in practice.
Develop specific analytical and research skills.
Be able to effectively collect data and manage information.
Be able to effectively analyze social phenomena.
Develop team work and interpersonal skills.
Develop the ability to work independently.
Develop problem-solving skills.
Develop a concern for the quality of scientific the research.
Acquire specialized knowledge necessary to perform research activities within the social sciences and further training.
Be able to effectively carry out research and organize time.
Be able to effectively manage research projects.

Learning Outcomes
1. Prepare data for statistical analysis using SPSS statistical software,
2. Modify the data for statistical analysis, depending on the chosen statistical method,
3. Choose an appropriate statistical method for analyzing data,
4. Apply statistical analysis in an environment of SPSS,
5. Explain the results obtained by statistical analysis with the help of SPSS statistical software,
6. Write a report on the results of statistical analysis.


Week plan
1. Repetition of basic statistical concepts
2. Introduction to SPSS environment, data entry
3. Modifying data, variables and their attributes.
4. Graphic data in SPSS types of graphs, proper use of graphic display, depending on the nature and type of data
5. Methods of sampling and measures of descriptive statistics (measures of central tendency, measures of dispersion)
6. Comparing the means of two groups (t-test), the assumption of t-test.
7. Non-parametric tests for comparing the means of two groups
8. Simple models of analysis of variance (ANOVA)
9. Repeated measures ANOVA, mixed design
10. Repeat for the colloquium. The first test, in the exercise period.
11. Correlation and linear regression models
12. Advanced models of regression analysis (categorical predictors)
13. Non-parametric tests and analysis of categorical data
14. Analysis assumptions of ANOVA repeated measurements and regression analysis
15. Repeat for the Colloquium


Grading
Student evaluation is based on the results of two written exams conducted during class or written exam in the exam period.
Lecture languages: - - -
Compulsory literature:
1. Literatura: Field, A. (2009). Discovering statistics using SPSS, London: Sage (izabrana poglavlja).
Recommended literature: - - -
Legend
L - Lectures
FE - Field exercises
CP - Clinical practicals
S - Seminar
E - Exercises
P - Practicum
PE - Proofreading exercises
ME - Methodology exercises
EE - Experimental exercises
FLE - Practical foreign language exercises
PEE - Physical education excercises
* - Not graded
Copyright (c) 2006. Ministarstva znanosti, obrazovanja i športa. Sva prava zadržana.
Programska podrška (c) 2006. Fakultet elektrotehnike i računarstva.
Oblikovanje(c) 2006. Listopad Web Studio.
Posljednja izmjena 2020-03-30