PSYb1170 Statistical Analysis in Psychology

Faculty of Social Studies
Spring 2026
Extent and Intensity
1/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Jan Širůček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology – Faculty of Social Studies
Contact Person: doc. Mgr. Stanislav Ježek, Ph.D.
Supplier department: Department of Psychology – Faculty of Social Studies
Timetable
Wed 8:00–9:40 P31 Posluchárna I. A. Bláhy
  • Timetable of Seminar Groups:
PSYb1170/01: Wed 10:00–10:50 U41, J. Širůček
PSYb1170/02: Wed 11:00–11:50 U41, J. Širůček
PSYb1170/03: Wed 12:00–12:50 U41, S. Ježek
PSYb1170/04: Wed 13:00–13:50 U41, S. Ježek
Prerequisites
! PSY117 Statistics
The course assumes students have basic knowledge of the prinicples and procedures of research in psychology. It also assumes knowledge and proficiency in the basics of high-school algebra.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 18 fields of study the course is directly associated with, display
Abstract
The main objective of the course is to introduce statistics as a part of psychology and the elementary statistical concepts used in psychological research. Students will learn to passively and actively use these concepts - statistica literacy. They learn to prepare data for analysis, compute elementary statistics, test elementary hypotheses. They also learn to read and communicate statistical findings both in Czech and English.
Learning outcomes
Students who successfully pass the course will be able:

- to code and arrange data in conventional data matrix; 

- to describe variable distributions using descriptive statistics and understand communicated descriptions of distribution in published literature;

- to draw graphs/visualizations describing the distributions of individual variables;

- to statistically conceptualize the various manifestations of associations between variables, to describe them using statistics and visualize them, and to understand such descriptions in publishes literature;

-   to make inferences from sample statistics to population parameters; to compute confidence intervals for basic descriptive statistics; to test elementary hypotheses being aware of the pitfalls of the NHST approach ; 

- to use linear regression with one predictor

- to use conditional probabilities to compute the indices of diagnostic utility of tests.

Key topics
1. Data matrix, types of variables, coding, measurement levels, data checking.
2. Graphical representation of data. Cumulative, absolute and relative frequencies and distribution. Tables, minimum, maximum, outliers. Normal distribution and areas under the curve. Bar chart, histogram.
3. Measures of central tendency and variability, percentiles, standard scores. Boxplot.
4. Measures of association (Pearson, Spearmann, Kendall) and graphical representation of relationship. Scatterplot. Linear, positive, negative association. Partial and part correlation.
5. Linear regression. Statistical prediction, linear vs. non-linear regression. Estimate, model, residual. Least squares method. Regression and residual variance, susms of squares. Coefficient of determination. Homoscedascity.
6. Probability, conditional probability, probability distributions. Bayes' theorem, indices of diagnostic utility.
7. Statistical inference, point vs. interval estimates. Statistics vs parameters. Sampling distribution, standard error. Central limit theorem.
8. Statistical hypothesis testing, Bayesian, Fisherian and Neymann-Pearson approach. Level of significance, Type I and II error, power, effect size, t-tests, tests for Pearson correlations.
9. Basic tests for nominal and ordinal variables.
10. One-way ANOVA.
Study resources and literature
    required literature
  • CUMMING, Geoff and Robert CALILN-JAGEMAN. Introduction to the new statistics : estimation, open science, and beyond. First published. New York: Routledge, Taylor & Francis Group, 2017, xxviii, 56. ISBN 9781138825512. info
  • HOWELL, David C. Statistical methods for psychology. 8th ed. Belmont: Wadsworth Cengage Learning, 2013, xix, 770. ISBN 9781111840853. info
    not specified
  • HENDL, Jan. Přehled statistických metod : analýza a metaanalýza dat. Páté, rozšířené vydán. Praha: Portál, 2015, 734 stran. ISBN 9788026209812. info
Approaches, practices, and methods used in teaching
lecture, problem solution demonstration, group discussion, online discussion forum, critical reading, homework
Method of verifying learning outcomes and course completion requirements
Midterm exams
There are two midterm exams. Each is worth 10 points. Their dates are listed in the interactive template. 

Final exam
The final exam is worth 60 points. To pass the exam the student must earn at least 35 points. The test covers all materials listed in this syllabus and in the interactive template. 

Grading
Midterm, and final exams add up to a maximum of 80 points.  The grading scale is following: A: 80 – 72b B: 71 – 64b C: 63 – 57b D: 56 – 51b E: 50 - 45b a F: 44 and less.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2027.
  • Enrolment Statistics (recent)
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