PSYb1170 Statistical Analysis in Psychology

Faculty of Social Studies
Spring 2022
Extent and Intensity
1/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Andrea Stašek (seminar tutor)
Mgr. Jan Širůček, Ph.D. (seminar tutor)
Mgr. Martin Tancoš (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 A. I. Bláhy
  • Timetable of Seminar Groups:
PSYb1170/01: Wed 10:00–10:50 PC25, J. Širůček
PSYb1170/02: Wed 11:00–11:50 PC25, M. Tancoš
PSYb1170/03: Wed 12:00–12:50 U41, A. Stašek
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
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 - 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
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.
Syllabus
  • 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.
Literature
    required literature
  • HOWELL, David C. Statistical methods for psychology. 8th ed. Belmont, CA: Wadsworth Cengage Learning, 2013, xix, 770. ISBN 9781111840853. info
    not specified
  • 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
  • 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 midterm exams. Each is worth 20 points. Their dates are listed in the interactive template. They will take place at the end of tle lecture time slot. Those who miss the midterm exams can retake any of them during the last week of the semester.

Team assignemt
There is a team-work assignment worth 10 points.

Final exam
The final exam is is worth 30 points. To pass the exam the student must earn at least 15 points. The test covers all materials listed in this syllabus and in the imteractive template. A student is allowed to taje the final exam if she has earned a minimum of 15 point from the midterms.

Grading
Midterm, project, and final exams add up to a maximum of 100 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 – 60b F: 59 or less.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
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 2020, Spring 2021, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2022, recent)
  • Permalink: https://is.muni.cz/course/fss/spring2022/PSYb1170