PSY117 Statistical Analysis in Psychology

Faculty of Social Studies
Spring 2020
Extent and Intensity
1/1/0. 5 credit(s). Type of Completion: zk (examination).
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Karel Rečka (seminar tutor)
Mgr. Jan Širůček, Ph.D. (seminar tutor)
Mgr. Adam Ťápal, M.A. (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
Wed 8:00–9:40 P31 Posluchárna A. I. Bláhy
  • Timetable of Seminar Groups:
PSY117/01: Wed 10:00–10:50 P21, J. Širůček
PSY117/02: Wed 11:00–11:50 P21, A. Ťápal
PSY117/03: Wed 12:00–12:50 PC25, S. Ježek
PSY117/04: Wed 13:00–13:50 PC25, K. Rečka
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 14 fields of study the course is directly associated with, display
Course objectives
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. 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
Student who successfully passes the course will be able: - to arrange data in conventional data matrix; to compute basic descriptive statistics describing the distribution of individual variables and relationships among them; to draw graphs describing the distribution of individual variables and relationships among them; to compute confidence intervals for basic descriptive statistics; to test elementary hypotheses; to use linear regression with one predictor; to use conditional probabilities to compute the indices of diagnostic utility of tests.
  • 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.
    required literature
  • HOWELL, David C. Statistical methods for psychology. 8th ed. Belmont, CA: 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
Teaching methods
lecture, problem solution demonstration, group discussion, online discussion forum, critical reading, homework
Assessment methods
Midterm exams
There are three very short midterm exams. Each is worth 10 points. Their dates are listed in the interactive template. The number of points earned from the three midterms is computed according to the following Excel formula =ROUND((SUM(P1;P2;P3)-MIN(P1;P2;P3))*1,5;0). This formula takes the best two results and scales them back to 30. This allows for one bad result or even absence. It is generally not possible to re-take the tests at a later date.
Team assignemt
There is a team-work assignment worth 10 points.

Final exam
The final exam is a paper-and-pencil test with a personal calculator worth 50 points. To pass the exam the student must earn at least 30 points. The test covers all materials listed in this syllabus and in the imteractive template.

Midterm and final exams add up to a maximum of 80 points. To pass the course at least 60% is needed. The grading scale is following:
A: 90 - 80p B: 79 – 73p C: 72 – 68b D: 67 – 63b E: 62 – 58b F: 57 or less.
Language of instruction
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Cvičení je děleno na tři skupiny.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2000, Spring 2001, Spring 2002, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019.
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