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Lecturer(s)
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Dofková Radka, doc. PhDr. Ph.D.
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Zdráhal Tomáš, doc. RNDr. CSc.
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Course content
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1. Introduction to Social Science Research Populations and Samples. Inferential and Descriptive Statistics. Sampling Methods. Variables and Scales of Measurement. Research designs, Graphs. 2. Measures of Central Tendency- Mean, Median & Mode Data Collection. The Mean, Median, Mode. Skewed Distribution. 3. Measures of Variability Measures of central tendency vs. Measures of variability. Range. Variance, Standard Deviation. Sample statistics as estimates of population parameters. Formulas for calculating the variance. Differences between the sample and population formulas. Sample Statistics vs. Population Parameters. 4. The Normal Distribution Sampling Method and Normal Distribution. Describing a Distribution of Scores. 5. Standardization and Z Scores Calculating Probabilities using z Scores and the Normal Distribution. 6. Standard Error The Standard Error of the Mean. Central Limit Theorem. Calculating z and t values. 7. Statistical Significance, Effect Size, and Confidence Intervals Statistical significance. Hypothesis testing. Effect size. Confidence intervals. 8. t Tests t Distributions. 1-Sample t Test. Independent Samples t Test. Effect size. Dependent (i.e., Paired) Samples t Test. 9. One-Way Analysis of Variance (ANOVA) One-way ANOVA vs. Multiple t tests. Partitioning the Variance in a One-Way ANOVA. Calculating an F value. Interpreting the F value. Performing the post-hoc Tukey tests. Effect Size. 10. Factorial Analysis of Variance (ANOVA) Main Effects and Interaction Effects. Covariates and Analysis of Covariance. Simple Effects. Effect Size. 11. Repeated Measures Analysis of Variance (ANOVA) Partitioning the Variance. Partial Effects, Simple Effects, and Effect Size. 12. Correlation The Pearson Correlation Coefficient. Testing for Statistical Significance. 13. Regression Simple Linear Regression. Multiple Regression. 14. Nonparametric Statistics and the Chi-Square Test of Independence Chi-Square Test of Independence. 15. Factor Analysis and Reliability Analysis Principal Components Factor Analysis. Exploratory Factor Analysis (EFA). Confirmatory Factor Analysis. Reliability Analysis.
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Learning activities and teaching methods
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Work with Text (with Book, Textbook)
- Homework for Teaching
- 60 hours per semester
- Preparation for the Course Credit
- 5 hours per semester
- Semestral Work
- 5 hours per semester
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Learning outcomes
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Prerequisites
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None
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Assessment methods and criteria
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Student performance
Email for instructions to the instructor tomas.zdrahal@upol.cz
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Recommended literature
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CHRÁSKA, Miroslav. (2016). Metody pedagogického výzkumu: základy kvantitativního výzkumu. 2., aktualizované vydání.. Praha.
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